Patent Grant
8428983
This invention relates, in general, to managing customer environments to provide support for business applications, and in particular, to automatically providing a pattern system environment that characterizes the customer environment and supports business resiliency.
Today, customers attempt to manually manage and align their availability management with their information-technology (IT) infrastructure. Changes in either business needs or the underlying infrastructure are often not captured in a timely manner and require considerable rework, leading to an inflexible environment.
Often high-availability solutions and disaster-recovery technologies are handled via a number of disparate point products that target specific scopes of failure, platforms or applications. Integrating these solutions into an end-to-end solution is a complex task left to the customer, with results being either proprietary and very specific, or unsuccessful.
Customers do not have the tools and infrastructure in place to customize their availability-management infrastructure to respond to failures in a way that allows for a more graceful degradation of their environments. As a result, more drastic and costly actions may be taken (such as a site switch) when other options (such as disabling a set of applications or users) could have been offered, depending on business needs.
Coordination across availability management and other systems-management disciplines is either nonexistent or accomplished via non-reusable, proprietary, custom technology.
There is little predictability as to whether the desired recovery objective will be achieved, prior to time of failure. There are only manual, labor-intensive techniques to connect recovery actions with the business impact of failures and degradations.
Any change in the underlying application, technologies, business recovery objectives, resources or their interrelationships require a manual assessment of impact to the hand-crafted recovery scheme.
Based on the foregoing, a need exists for a capability that automatically manages and aligns a customer's environment (e.g., for availability) with its information technology (IT) infrastructure. In particular, a need exists for a representation, automatically formed, of the IT resources of a customer's business environment, in which the representation is based on information relating to how IT resources are utilized during a customer's business cycle.
The shortcomings of the prior art are overcome and additional advantages are provided through the provision of a method of facilitating management of customer environments. The method includes, for instance, obtaining information associated with one or more information technology (IT) resources of a customer, wherein the information indicates how the one or more IT resources are utilized during a business cycle of the customer; and automatically forming a pattern system environment (PSE) based on the obtained information, wherein the pattern system environment is a representation of the IT resources of the customer's business environment.
Computer program products and systems relating to one or more aspects of the present invention are also described and claimed herein.
Additional features and advantages are realized through the techniques of the present invention. Other embodiments and aspects of the invention are described in detail herein and are considered a part of the claimed invention.
One or more aspects of the present invention are particularly pointed out and distinctly claimed as examples in the claims at the conclusion of the specification. The foregoing and other objects, features, and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:
In managing a customer's environment, such as its business environment, there is a set of requirements unaddressed by existing technology, which causes unpredictable down time, large-impact failures and recoveries, and significant extra labor cost, with resulting loss of business revenue. These requirements include, for instance:
21. Ability to divide pre-conditioning work between long running and immediate, nondisruptive short term actions.
22. Impact only the smallest set of resources required during recovery, to avoid negative residual or side effects for attempting to recover a broader set of resources than what is actually impacted by the failure.
The above set of requirements is addressed, however, by a Business Resiliency (BR) Management System, of which one or more aspects of the present invention are included. The Business Resiliency Management System provides, for instance:
One goal of the BR system is to allow customers to align their supporting information technology systems with their business goals for handling failures of various scopes, and to offer a continuum of recovery services from finer grained process failures to broader scoped site outages. The BR system is built around the idea of identifying the components that constitute a business function, and identifying successive levels of recovery that lead to more complex constructs as the solution evolves. The various recovery options are connected by an overall BR management capability that is driven by policy controls.
Various characteristics of one embodiment of a BR system include:
A Business Resilience System is capable of being incorporated in and used by many types of environments. One example of a processing environment to incorporate and use aspects of a BR system, including one or more aspects of the present invention, is described with reference to
Processing environment 100 includes, for instance, a central processing unit (CPU) 102 coupled to memory 104 and executing an operating system 106. Examples of operating systems include AIX® and z/OS®, offered by International Business Machines Corporation; Linux; etc. AIX® and z/OS® are registered trademarks of International Business Machines Corporation, Armonk, N.Y., U.S.A. Other names used herein may be registered trademarks, trademarks or product names of International Business Machines Corporation or other companies.
The operating system manages execution of a Business Resilience Runtime Component 108 of a Business Resilience System, described herein, and one or more applications 110 of an application container 112.
As examples, processing environment 100 includes an IBM® System z™ processor or a pSeries® server offered by International Business Machines Corporation; a Linux server; or other servers, processors, etc. Processing environment 100 may include more, less and/or different components than described herein. (pSeries® is a registered trademark of International Business Machines Corporation, Armonk, N.Y., USA.)
Another example of a processing environment to incorporate and use aspects of a BR System, including one or more aspects of the present invention, is described with reference to
As shown, a processing environment 200 includes for instance, a central processing complex 202 coupled to an input/output (I/O) subsystem 204. Central processing complex 202 includes, for instance, a central processing unit 206, memory 208, an operating system 210, a database management system 212, a Business Resilience Runtime Component 214, an application container 216 including one or more applications 218, and an I/O facility 220.
I/O facility 220 couples central processing complex 202 to I/O subsystem 204 via, for example, a dynamic switch 230. Dynamic switch 230 is coupled to a control unit 232, which is further coupled to one or more I/O devices 234, such as one or more direct access storage devices (DASD).
Processing environments 100 and/or 200 may include, in other embodiments, more, less and/or different components.
In yet another embodiment, a central processing complex 300 (
For example, network service 302 of central processing complex 300 is coupled to a switch 308 of network subsystem 306. Switch 308 is coupled to a switch 310 via routers 312 and firewalls 314. Switch 310 is further coupled to a network service 316 of processing environment 304.
Processing environment 304 further includes, for instance, a central processing unit 320, a memory 322, an operating system 324, and an application container 326 including one or more applications 328. In other embodiments, it can include more, less and/or different components.
Moreover, CPC 300 further includes, in one embodiment, a central processing unit 330, a memory 332, an operating system 334, a database management system 336, a Business Resilience Runtime Component 338, an application container 340 including one or more applications 342, and an I/O facility 344. It also may include more, less and/or different components.
I/O facility 344 is coupled to a dynamic switch 346 of an I/O subsystem 347. Dynamic switch 346 is further coupled to a control unit 348, which is coupled to one or more I/O devices 350.
Although examples of various environments are provided herein, these are only examples. Many variations to the above environments are possible and are considered within the scope of the present invention.
In the above-described environments, a Business Resilience Runtime Component of a Business Resilience System is included. Further details associated with a Business Resilience Runtime Component and a Business Resilience System are described with reference to
In one example, a Business Resilience System 400 is a component that represents the management of recovery operations and configurations across an IT environment. Within that Business Resilience System, there is a Business Resilience Runtime Component (402) that represents the management functionality across multiple distinct Recovery Segments, and provides the service level automation and the support of creation of the recovery sequences. In addition, there are user interface (404), administration (406), installation (408) and configuration template (410) components within the Business Resilience System that enable the administrative operations that are to be performed. Each of these components is described in further detail below.
Business Resilience Runtime Component 402 includes a plurality of components of the BR System that are directly responsible for the collection of observations, creation of PSEs, policy acceptance, validation, error detection, and formulation of recovery sequences. As one example, Business Resilience Runtime Component 402 includes the following components:
In addition to the Business Resilience Runtime Component of the BR system, the BR system includes the following components, previously mentioned above.
The user interface, admin mailbox, install logic and/or template components can be part of the same computing unit executing BR Runtime or executed on one or more other distributed computing units.
To further understand the use of some of the above components and their interrelationships, the following example is offered. This example is only offered for clarification purposes and is not meant to be limiting in any way.
Referring to
As a result of these conditions leading up to runtime, the following subscriptions have already taken place:
These steps highlight one example of an error detection process:
In addition to the above, BR includes a set of design points that help in the understanding of the system. These design points include, for instance:
Goal Policy Support
BR is targeted towards goal based policies—the customer configures his target availability goal, and BR determines the preparatory actions and recovery actions to achieve that goal (e.g., automatically).
Availability management of the IT infrastructure through goal based policy is introduced by this design. The BR system includes the ability to author and associate goal based availability policy with the resource Recovery Segments described herein. In addition, support is provided to decompose the goal policy into configuration settings, preparatory actions and runtime procedures in order to execute against the deployed availability goal. In one implementation of the BR system, the Recovery Time Objective (RTO—time to recover post outage) is a supported goal policy. Additional goal policies of data currency (e.g., Recovery Point Objective) and downtime maximums, as well as others, can also be implemented with the BR system. Recovery Segments provide the context for association of goal based availability policies, and are the scope for goal policy expression supported in the BR design. The BR system manages the RTO through an understanding of historical information, metrics, recovery time formulas (if available), and actions that affect the recovery time for IT resources.
RTO goals are specified by the customer at a Recovery Segment level and apportioned to the various component resources grouped within the RS. In one example, RTO goals are expressed as units of time intervals, such as seconds, minutes, and hours. Each RS can have one RTO goal per Pattern System Environment associated with the RS. Based on the metrics available from the IT resources, and based on observed history and/or data from the customer, the RTO goal associated with the RS is evaluated for achievability, taking into account which resources are able to be recovered in parallel.
Based on the RTO for the RS, a set of preparatory actions expressed as a workflow is generated. This preparatory workflow configures the environment or makes alterations in the current configuration, to achieve the RTO goal or to attempt to achieve the goal.
In terms of optimizing RTO, there are tradeoffs associated with the choices that are possible for preparatory and recovery actions. Optimization of recovery choice is performed by BR, and may include interaction at various levels of sophistication with IT resources. In some cases, BR may set specific configuration parameters that are surfaced by the IT resource to align with the stated RTO. In other cases, BR may request that an IT resource itself alter its management functions to achieve some portion of the overall RS RTO. In either case, BR aligns availability management of the IT resources contained in the RS with the stated RTO.
Metrics and Goal Association
In this design, as one example, there is an approach to collecting the required or desired metrics data, both observed and key varying factors, system profile information that is slow or non-moving, as well as potential formulas that reflect a specific resource's use of the key factors in assessing and performing recovery and preparatory actions, historical data and system information. The information and raw metrics that BR uses to perform analysis and RTO projections are expressed as part of the IT resources, as resource properties. BR specific interpretations and results of statistical analysis of key factors correlated to recovery time are kept as BR Specific Management data (BRMD).
Relationships used by BR, and BR Specific Resource Pairing Information
BR maintains specific information about the BR management of each resource pairing or relationship between resources. Information regarding the BR specific data for a resource pairing is kept by BR, including information such as ordering of operations across resources, impact assessment information, operation effect on availability state, constraint analysis of actions to be performed, effects of preparatory actions on resources, and requirements for resources to co-locate or anti-co-locate.
Evaluation of Failure Scope
One feature of the BR function is the ability to identify the scope and impact of a failure. The BR design uses a Containment Region to identify the resources affected by an incident. The Containment Region is initially formed with a fairly tight restriction on the scope of impact, but is expanded on receiving errors related to the first incident. The impact and scope of the failure is evaluated by traversing the resource relationships, evaluating information on BR specific resource pairing information, and determining most current state of the resources impacted.
Generation and Use of Workflow
Various types of preparatory and recovery processes are formulated and in some cases, optionally initiated. Workflows used by BR are dynamically generated based on, for instance, customer requirements for RTO goal, based on actual scope of failure, and based on any configuration settings customers have set for the BR system.
A workflow includes one or more operations to be performed, such as Start CICS, etc. Each operation takes time to execute and this amount of time is learned based on execution of the workflows, based on historical data in the observation log or from customer specification of execution time for operations. The workflows formalize, in a machine readable, machine editable form, the operations to be performed.
In one example, the processes are generated into Business Process Execution Language (BPEL) compliant workflows with activities that are operations on IT resources or specified manual, human activities. For example, BRM automatically generates the workflows in BPEL. This automatic generation includes invoking routines to insert activities to build the workflow, or forming the activities and building the XML (Extensible Mark-Up Language). Since these workflows are BPEL standard compliant, they can be integrated with other BPEL defined workflows which may incorporate manual activities performed by the operations staff. These BR related workflows are categorized as follows, in one example:
Since the set of BR actions described above modify existing IT environments, visibility to the actions that are taken by BR prior to the actual execution is provided. To gain trust in the decisions and recommendations produced by BR, the BR System can run in ‘advisory mode’. As part of advisory mode, the possible actions that would be taken are constructed into a workflow, similar to what would be done to actually execute the processes. The workflows are then made visible through standard workflow authoring tooling for customers to inspect or modify. Examples of BPEL tooling include:
BR tooling spans the availability management lifecycle from definition of business objectives, IT resource selection, availability policy authoring and deployment, development and deployment of runtime monitors, etc. In one example, support for the following is captured in the tooling environment for the BR system:
The policy lifecycle for BR goal policies, such as RTO goals, includes, for example:
One of the points in determining operational state of a Recovery Segment is that this design allows for customers to configure a definition of specific ‘aggregated’ states, using properties of individual IT resources. A Recovery Segment is an availability management context, in one example, which may include a diverse set of IT resources.
The customer may provide the rules logic used within the Recovery Segment to consume the relevant IT resource properties and determine the overall state of the RS (available, degraded and unavailable, etc). The customer can develop and deploy these rules as part of the Recovery Segment availability policy. For example, if there is a database included in the Recovery Segment, along with the supporting operating system, storage, and network resources, a customer may configure one set of rules that requires that the database must have completed the recovery of in-flight work in order to consider the overall Recovery Segment available. As another example, customers may choose to configure a definition of availability based on transaction rate metrics for a database, so that if the rate falls below some value, the RS is considered unavailable or degraded, and evaluation of ‘failure’ impact will be triggered within the BR system. Using these configurations, customers can tailor both the definitions of availability, as well as the rapidity with which problems are detected, since any IT resource property can be used as input to the aggregation, not just the operational state of IT resources.
Failure During Workflow Sequences of Preparatory, Recovery, Preventive
Failures occurring during sequences of operations executed within a BPEL compliant process workflow are intended to be handled through use of BPEL declared compensation actions, associated with the workflow activities that took a failure. The BR System creates associated “undo” workflows that are then submitted to compensate, and reset the environment to a stable state, based on where in the workflow the failure occurred.
Customer Values
The following set of customer values, as examples, are derived from the BR system functions described above, listed here with supporting technologies from the BR system:
Management of the IT environment is adaptively performed, as described herein and in a U.S. Patent Application “Adaptive Business Resiliency Computer System for Information Technology Environments,” (POU920070364US1), Bobak et al., co-filed herewith, which is hereby incorporated herein by reference in its entirety.
Many different sequences of activities can be undertaken in creating a BR environment. The following represents one possible sequence; however, many other sequences are possible. This sequence is provided merely to facilitate an understanding of a BR system and one or more aspects of the present invention. This sequence is not meant to be limiting in any way. In the following description, reference is made to various U.S. patent applications, which are co-filed herewith.
On receiving the BR and related product offerings, an installation process is undertaken. Subsequent to installation of the products, a BR administrator may define the configuration for BR manager instances with the aid of BRM configuration templates.
Having defined the BRM configuration a next step could be to define Recovery Segments as described in “Recovery Segments for Computer Business Applications,” (POU920070108US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
Definition of a RS may use a representation of resources in a topology graph as described in “Use of Graphs in Managing Computing Environments,” (POU920070112US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
It is expected that customers will enable BR operation in “observation” mode for a period of time to gather information regarding key metrics and operation execution duration associated with resources in a RS.
At some point, sufficient observation data will have been gathered or a customer may have sufficient knowledge of the environment to be managed by BR. A series of activities may then be undertaken to prepare the RS for availability management by BR. As one example, the following steps may be performed iteratively.
A set of functionally equivalent resources may be defined as described in “Use of Redundancy Groups in Runtime Computer Management of Business Applications,” (POU920070113US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
Specification of the availability state for individual resources, redundancy groups and Recovery Segments may be performed as described in “Use of Multi-Level State Assessment in Computer Business Environments,” (POU920070114US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
Representations for the IT environment in which BR is to operate may be created from historical information captured during observation mode, as described herein, in accordance with one or more aspects of the present invention. These definitions provide the context for understanding how long it takes to perform operations which change the configuration—especially during recovery periods.
Information on relationships between resources may be specified based on recommended best practices—expressed in templates—or based on customer knowledge of their IT environment as described in “Conditional Computer Runtime Control of an Information Technology Environment Based on Pairing Constructs,” (POU920070110US1), Bobak et al., which is hereby incorporated herein by reference in its entirety. Pairing processing provides the mechanism for reflecting required or desired order of execution for operations, the impact of state change for one resource on another, the effect execution of an operation is expected to have on a resource state, desire to have one subsystem located on the same system as another and the effect an operation has on preparing the environment for availability management.
With preliminary definitions in place, a next activity of the BR administrator might be to define the goals for availability of the business application represented by a Recovery Segment as described in “Programmatic Validation in an Information Technology Environment,” (POU920070111 US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
Managing the IT environment to meet availability goals includes having the BR system prioritize internal operations. The mechanism utilized to achieve the prioritization is described in “Serialization in Computer Management,” (POU920070105US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
Multiple operations are performed to prepare an IT environment to meet a business application's availability goal or to perform recovery when a failure occurs. The BR system creates workflows to achieve the required or desired ordering of operations, as described in “Dynamic Generation of processes in Computing Environments,” (POU920070123US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
A next activity in achieving a BR environment might be execution of the ordered set of operations used to prepare the IT environment, as described in “Dynamic Selection of Actions in an Information Technology Environment,” (POU920070117US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
Management by BR to achieve availability goals may be initiated, which may initiate or continue monitoring of resources to detect changes in their operational state, as described in “Real-Time Information Technology Environments,” (POU920070120US1), Bobak et al., which is hereby incorporated herein by reference in its entirety. Monitoring of resources may have already been initiated as a result of “observation” mode processing.
Changes in resource or redundancy group state may result in impacting the availability of a business application represented by a Recovery Segment. Analysis of the environment following an error is performed. The analysis allows sufficient time for related errors to be reported, insures gathering of resource state completes in a timely manner and insures sufficient time is provided for building and executing the recovery operations—all within the recovery time goal, as described in “Management Based on Computer Dynamically Adjusted Discrete Phases of Event Correlation,” (POU920070119US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
A mechanism is provided for determining if events impacting the availability of the IT environment are related, and if so, aggregating the failures to optimally scope the outage, as described in “Management of Computer Events in a Computer Environment,” (POU920070118US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
Ideally, current resource state can be gathered after scoping of a failure. However, provisions are made to insure management to the availability goal is achievable in the presence of non-responsive components in the IT environment, as described in “Managing the Computer Collection of Information in an Information Technology Environment,” (POU920070121 US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
With the outage scoped and current resource state evaluated, the BR environment can formulate an optimized recovery set of operations to meet the availability goal, as described in “Defining a Computer Recovery Process that Matches the Scope of Outage,” (POU920070124US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
Formulation of a recovery plan is to uphold customer specification regarding the impact recovery operations can have between different business applications, as described in “Managing Execution Within a Computing Environment,” (POU920070115US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
Varying levels of recovery capability exist with resources used to support a business application. Some resources possess the ability to perform detailed recovery actions while others do not. For resources capable of performing recovery operations, the BR system provides for delegation of recovery if the resource is not shared by two or more business applications, as described in “Conditional Actions Based on Runtime Conditions of a Computer System Environment,” (POU920070116US1), Bobak et al., which is hereby incorporated herein by reference in its entirety.
Having evaluated the outage and formulated a set of recovery operations, the BR system resumes monitoring for subsequent changes to the IT environment.
In support of mainline BR system operation, there are a number of activities including, for instance:
In order to build a BR environment that meets recovery time objectives, IT configurations within a customer's location are to be characterized and knowledge about the duration of execution for recovery time operations within those configurations is to be gained. IT configurations and the durations for operation execution vary by time, constituent resources, quantity and quality of application invocations, as examples. Customer environments vary widely in configuration of IT resources in support of business applications. Understanding the customer environment and the duration of operations within those environments aids in insuring a Recovery Time Objective is achievable and in building workflows to alter the customer configuration of IT resources in advance of a failure and/or when a failure occurs.
A characterization of IT configurations within a customer location is built by having knowledge of the key recovery time characteristics for individual resources (i.e., the resources that are part of the IT configuration being managed; also referred to as managed resources). Utilizing the representation for a resource, a set of key recovery time objective (RTO) metrics are specified by the resource owner. During ongoing operations, the BR manager gathers values for these key RTO metrics and gathers timings for the operations that are used to alter the configuration. It is expected that customers will run the BR function in “observation” mode prior to having provided a BR policy for availability management or other management. While executing in “observation” mode, the BR manager periodically gathers RTO metrics and operation execution durations from resource representations. The key RTO metrics properties, associated values and operation execution times are recorded in an Observation log for later analysis through tooling. Key RTO metrics and operation execution timings continue to be gathered during active BR policy management in order to maintain currency and iteratively refine data used to characterize customer IT configurations and operation timings within those configurations.
Examples of RTO properties and value range information by resource type are provided in the below table. It will be apparent to those skilled in the art that additional, less, and/or different resource types, properties and/or value ranges may be provided.
A specific example of key RTO properties for a z/OS® image is depicted in
The z/OS® image has a set of RTO metrics associated therewith, as described above. Other resources may also have its own set of metrics. An example of this is depicted in
Further, in one example, the RTO properties from each of the resources that are part of the Recovery Segment for App A have been gathered by BR and formed into an “observation” for recording to the Observation log, as depicted at 850.
Resources have varying degrees of functionality to support RTO goal policy. Such capacity is evaluated by BR, and expressed in resource property RTOGoalCapability in the BRMD entry for the resource. Two options for BR to receive information operation execution timings are: use of historical data or use of explicitly customer configured data. If BR relies on historical data to make recovery time projections, then before a statistically meaningful set of data is collected, this resource is not capable of supporting goal policy. A mix of resources can appear in a given RS—some have a set of observations that allow classification of the operation execution times, and others are explicitly configured by the customer.
Calculation of projected recovery time can be accomplished in two ways, depending on customer choice: use of historical observations or use of customers input timings. The following is an example of values for the RTOGoalCapability metadata that is found in the BRMD entry for the resource that indicates this choice:
If the customer is in observation mode, then historical information is captured, regardless of whether the customer has indicated use of explicitly input timings or use of historical information.
The administrator can alter, on a resource basis, which set of timings BR is to use. The default is to use historical observations. In particular, a change source of resource timing logic is provided that alters the source that BR uses to retrieve resource timings. The two options for retrieving timings are from observed histories or explicitly from admin defined times for operation execution. The default uses information from the observed histories, gathered from periodic polls. If the customer defines times explicitly, the customer can direct BR to use those times for a given resource. If activated, observation mode continues and captures information, as well as running averages, and standard deviations. The impact to this logic is to alter the source of information for policy validation and formulation of recovery plan.
With respect to the historical observations, there may be a statistically meaningful set of observations to verify. The sample size should be large enough so that a time range for each operation execution can be calculated, with a sufficient confidence interval. The acceptable number of observations to qualify as statistically meaningful, and the desired confidence interval are customer configurable using BR UI, but provided as defaults in the BRMD entry for the resource. The default confidence interval is 95%, in one example.
There are metrics from a resource that are employed by BR to enable and perform goal management. These include, for instance:
There is also a set of information about the resource that is employed—this information is provided as defaults in the BRMD entry for the resource, but provided to the BR team in the form of best practices information/defaults by the domain owners:
In addition to the resources defined herein as part of the IT configuration that is managed, there are other resources, referred to herein as assessed resources. Assessed resources are present primarily to provide observation data for PSE formation, and to understand impact(s) on managed resources. They do not have a decomposed RTO associated with them nor are they acted on for availability by BR. Assessed resources have the following characteristics, as examples:
Similarly, there are likely scenarios where a resource exists in a customer environment that already has an alternative availability management solution, and does not require BR for its availability. However, since other resources that are managed by BR may be dependent on them, they are observed and assessed in order to collect observation data and understand their impacts on managed resources. Additionally, there may be resources that do not have alternative management solutions, but the customer simply does not want them managed by BR, but other managed resources are dependent upon them. They too are classified as assessed resources.
These assessed resources share many of the same characteristics of managed resources, such as, for example:
Finally, there are a few restrictions that BR imposes upon assessed resources, in this embodiment:
To facilitate the building of the customer's IT configuration, observations regarding the customer's environment are gathered and stored in an observation log. In particular, the observation log is used to store observations gathered during runtime in customer environments, where each observation is a collection of various data points. They are created for each of the Recovery Segments that are in “observation” mode. These observations are used for numerous runtime and administrative purposes in the BR environment. As examples the observations are used:
The administrator may also disable observation mode for a Recovery Segment, which stops it from polling for data and creating subsequent observation records for insertion in the log. However, the accumulated observation log is not deleted. In one example, an RS remains in observation mode throughout its lifecycle. The UI displays the implications of disabling observation mode.
In BR, the observations that are collected by BR during runtime can be grouped into two categories, as examples:
A periodic poll observation is a point-in-time snapshot of the constituent resources in a Recovery Segment. Observation data points are collected for those resources in the Recovery Segment(s) which have associated BR management data for any of the following reasons, as examples:
The full value of these observations is derived for an RS when they include data that has been gathered for its constituent resources, plus the resources that those are dependent upon. In one embodiment, the administrator is not forced to include all dependent resources when defining a Recovery Segment, and even if that were the case, there is nothing that prevents them from deleting various dependent resources. When defining a Recovery Segment, the BR UI provides an option that allows the customer to display the dependency graph for those resources already in the Recovery Segment. This displays the topology from the seed node(s) in the Recovery Segment down to and including the dependent leaf nodes. The purpose of this capability is to give the customer the opportunity to display the dependent nodes and recommend that they be included in the Recovery Segment.
Preparatory and recovery workflows are built by the BR manager to achieve the customer requested RTO policy based on resource operations timings. During active policy monitoring by the BR manager, measurements of achieved time for operations are recorded in observations to the log and used to maintain the running statistical data on operation execution times. Observations written to the log may vary in the contained resource RTO metrics and operation execution timings.
Observations are also collected from of any of the BPEL workflows created by BR in the customer's environment. There is a standard template that each BR BPEL workflow uses. As part of that template, observation data is captured at the start of, during, and at the completion of each workflow. Specifically, in one example, one observation is created at the end of the workflow with data accumulated from completion of each activity. This information is used to gather timings for workflow execution for use in creating subsequent workflows at time of failure.
One embodiment of the logic associated with a workflow monitor is described with reference to
As one example, the monitoring process is delayed until a workflow is initiated, STEP 900. In response to initiating a workflow, STEP 902, the monitoring function delays until a workflow activity completes, STEP 904 (e.g., CICS is started, etc.). In response to completion of a workflow activity, statistics regarding the operation execution duration are retrieved, STEP 906, and monitoring continues if there are additional activities to be invoked by the workflow, INQUIRY 908. When all activities associated with the workflow have completed, an observation log 910 that includes the operation execution duration statistics is created, STEP 912. Further, the BR management data for the resources on which operations were invoked is updated, STEP 914, on completion of this invocation of the workflow monitor.
In one example, the observation log (e.g., 910) used to store the collected data is implemented as one or more DB2® tables in the Business Resilience datastore that physically resides in the BR environment. (DB2® is a registered trademark of International Business Machines Corporation.) That database is created, for instance, at installation time, and the observation log tables are created and initialized (if necessary) at that time. In this example, it is not associated with a particular resource and is not used to persist any resource properties. The typical access mechanism is via JDBC calls from the BR UI client(s) and the RS resource using JDBC type four drivers. One example of the physical model of the observation log (and any other related tables) is depicted in
In one embodiment, observation log 1000 includes, for instance, the singleton data values for the observation. Example fields and values within those fields are described below.
In one embodiment, observation resource table 1002 includes, for instance, the list of resources in the observation.
In one embodiment, observation operation table 1004 includes, for instance, the list of properties and operations of the resources in the observation.
In one embodiment, observation BRAD table 1006 includes, for instance, the delay time from the RS to each BRAD in the observation.
Data recorded to the observation log is used by a series of tools (e.g., the BR UI, such as the Eclipse plug-ins, and the cluster analysis tools, such as Mathematica) to build representations of customer IT configurations and perform statistical analysis of operation duration times. Each observation in the log includes a set of RTO metric properties, associated values and operation execution timings for a set of resources. A group of observations is selected, as described below, based on, for instance, a set of categories including time and date, resources included in the IT configuration, level of resource consumption, and/or resource configuration parameters. For each observation and each RTO metric, tooling provides a means, in accordance with an aspect of the present invention, to form clusters that minimize differences in RTO metrics. These clusters are termed “Pattern System Environments” (PSEs).
In particular, in accordance with an aspect of the present invention, a capability is provided for forming (e.g., form, create, build, construct, assemble, etc.) a Pattern System Environment that is a representation of the IT resources of a customer's business environment. The Pattern System Environment is, for instance, automatically formed based on obtained information (e.g., recorded, provided, determined, retrieved, evaluated, had, etc.) associated with IT resources of the customer. This information indicates, for example, how the IT resources are used during a business cycle (i.e., before, during and after business hours) of the customer. Specifically, the information indicates how the one or more IT resources are utilized over time by the customer.
In one example, the PSE can be altered by the customer. Note that not all resources or metrics may appear in a PSE. A customer may choose to modify the number of PSEs identified by the tooling. From the collection of observations that comprise a PSE, statistics are calculated on selected RTO metrics and operation duration times. Statistical analysis on selected RTO metrics and operation duration times yields average, range and standard deviation values.
Observations from the log are used as input to the cluster formation tooling logic. A selection of which observations are to be utilized can be customer tailored. For example, observations within the log may be associated with a customer defined RS—that is all of these observations will have the same collection of constituent resources. Observations in the log, or from a set of merged log(s), may be from multiple RSs either with shared resources or without having shared resources. Observations may also be recorded by the BR manager based on a RS that has been enabled for BR management only for the recording of observations on RTO metrics and operation execution timings.
Customers may or may not know characteristics of their environment on which to base PSE formation by the cluster analysis tooling. As an example, many enterprise customers do know their environment and business processes—based on date and time, based on resources used for a business process, based on utilization of a set of resources and/or based on configuration parameters for resources.
One embodiment of the logic associated with forming a Pattern System Environment is described with reference to
Referring to
A second selection criterion for observations is based on minimizing differences in the collection of resources that form an observation. In one example, this is not viewed as a simple counting of the number of same resources in observations. Instead, support in tooling provides for weighting of the importance to the presence or absence of specified resource instances. The tooling UI provides for customer selection of an observation based on the presence of a resource instance and supports grouping of resource occurrences by type. For example, in a given observation:
A third criterion for grouping of observations is supported by the tooling based on utilization of resources. Resource utilization metrics associated with a resource, such as z/OS®, within a given observation may include:
The remaining RTO factors in observations from the log form a fourth level of criterion.
Further, via the UI, as an example, one or more observation logs are selected to be processed (e.g., logs for last 30 days, logs of this particular system, etc), STEP 1102, and therefrom, a set of observation log records is selected, STEP 1104. The selection of records may be performed based on PSE formation criteria, customer direction or based on results from principle component analysis or factor analysis routines provided as part of the tooling. Principle component analysis and factor analysis are described in one or more of the following:
In addition to retrieving the log records, default or specific formulas to be used in determining the relative distance between RTO values in observations are specified by the UI, STEP 1106. Additionally, key metrics to be used in cluster analysis may be provided via the UI, STEP 1108, with default values being those used to establish PSE formation criteria.
Thereafter, a determination is made as to whether analysis (e.g., principle component analysis or factor analysis) is desired to determine key metrics to be used in cluster analysis, instead of, or in addition, to those identified above, INQUIRY 1110.
If analysis is desired, then selected data is passed to the tooling to perform the analysis to identify key metrics, STEP 1112. In particular, for some environments, the key characteristics to determine clusters of PSE(s) may not be known before cluster analysis tooling is utilized. Thus, all observations recorded as part of the specified observation log(s) may be selected. For these environments, the tooling supports a form of factor analysis or principle component analysis to determine the critical key metrics that differentiate the observations in the log. This provides for building clusters that minimize the differences in RTO metric values between observations. Subsequent iterations through the PSE creation tooling can be executed to select observation log data based on these critical key metrics. BR developed extensions to tool packages (e.g., Eclipse) suggests seeds for principle component or factor analysis. Some or all of the following seeds may be recommended: groupings based on date and time, groupings based on resources contained in the observation log records, groupings based on resource utilization levels or groupings based on configuration parameters.
Subsequent to identifying the key metrics to be used, either by selection in STEP 1108 or analysis in STEP 1112, weighting is provided for the key metrics, STEP 1114. For example, tooling support for cluster analysis provides support for weighting of key RTO metrics to yield the most distinguished clusters. Weighting factors may be recommended by the tooling from factor analysis or principle component analysis, or recommended by direct customer input. In one particular example:
In another example:
In addition to the above, the tooling provides for conversion of non-numeric values of key metrics to a numeric representation, STEP 1116 (
From the selected collection of observations, a number of clusters or Pattern Systems Environments are suggested by the tooling. The number of clusters or Pattern System Environments can be specified by the customer or suggestion of the tooling may be accepted, STEP 1118. Thereafter, cluster formation is invoked with input including the observation log records (i.e., those retrieved in STEP 1104), as well as the key metrics, STEP 1120. Many commercial and open source tool packages are available that accept data, form clusters and provide statistical analysis of the data and clusters formed. These packages include, for instance:
After cluster analysis has executed, PSE formation results are displayed with data on key metrics for PSE(s), STEP 1122. Here, the “goodness” of the PSE(s) is displayed. For instance, an indication of how sparse or dense the PSE(s) are is provided.
In creating the PSE, observation records are examined to match the time/date intervals associated with the PSE(s). Time/date intervals for PSE(s) are suggested by the tooling based on the observations associated with the PSE(s) from cluster analysis. Optionally, time/date ranges may be assigned by the customer to the PSE(s). Specifically, in one example, the customer is provided an opportunity to modify the time/date intervals associated with the formed PSE(s), INQUIRY 1124. That is, is the date/time ranges of the records used to build the PSE what is desired or are there holidays or is another time preferred, etc. If it is not what is desired, the customer alters the date/time, STEP 1126.
Subsequent to altering the date/time or if the customer is satisfied with the date/time, operation execution duration times are sorted into the PSE(s) based on the time of operation execution, STEP 1128. In particular, a second pass through the selected observation records is made, and each log record is placed into a PSE based on the recorded operation execution time of the record. This is driven by the date/time ranges of the PSE(s). For instance, an operation execution time for 3 A.M. (i.e., operation occurred at 3 A.M.) is placed in the PSE covering 3 A.M., etc. The operation execution duration time may be different than the time of the observation record having been recorded to the log. Multiple observation records may reflect execution of the same operation which is recognized by the operation execution date/time. Multiple observations for the same operation execution are consolidated into a single event.
From the set of unique operation execution duration measurements in the PSE(s), an average and standard deviation are calculated and stored as part of the PSE(s) to determine the reliability of the measured observations, STEP 1130. The statistics associated with operation execution duration are updated during runtime with measurements of operation execution duration by matching the time/date of operation execution to the time/date associated with the PSE(s) current for a RS.
During the second pass, observation log records are fit to PSE(s) in order to generate statistics for RTO metrics, as described below. By default, the key RTO metrics used in forming the PSE (from cluster analysis tooling) are used to sort observations into PSE(s). Optionally, through the UI interface, customer specification of metrics to be used to fit observations to PSE(s) can be provided by the customer. If the customer wishes to change the selection of the key metrics (e.g., not satisfied with results thus far), INQUIRY 1132, the selection is altered, STEP 1134 (
Returning to INQUIRY 1132 (
Further, for each selected observation log record, STEP 1138, a center of gravity is calculated, STEP 1140. That is, treating the set of key RTO metrics to be an N-dimensional space, a center of gravity is calculated. As an example, the distance from the center of gravity in the N-dimensional space to each key RTO metric forming the N-dimensional space is minimized. If specified, weights are used to influence calculation of the center of gravity for the PSE(s) and observation log records. Using the default or customer provided distance formula and the calculated centers of gravity, the distance from an observation to each PSE is calculated, STEP 1142. The observation log record is then associated with the PSE for which the distance is minimum, STEP 1144.
Subsequent to processing each selected observation log record, processing continues with STEP 1160 (
Tooling also provides for iterations enabling customer specified variances in, for instance: specification of the selection criteria for observations to be included in the analysis; weighting for RTO metrics; conversion routines for non-numeric data; statistics on operation execution; enabling BRM use of the operation execution duration time; explicit specification by customer of an operation execution duration time to be used by the BRM; and/or formula for calculating distance between values of RTO metrics between observations. Thus, a determination is made as to whether a change to the PSE formation is desired, INQUIRY 1164. If so, then processing continues with STEP 1100 (
Additionally, a unique date and time range is associated with PSE use through customer interaction, STEP 1168. Moreover, if the created PSE(s) are to be saved, INQUIRY 1170, relevant PSE table(s) 1172 are created or updated, STEP 1174. However, if the created PSE(s) are not to be saved, INQUIRY 1170, then processing completes without saving the PSE(s) in a PSE table, STEP 1176. This completes processing for creating a Pattern System Environment.
As described above, PSE(s) are built using tooling based on observation records accumulated by BR where the records used may be selected by the customer based on date/time, resources contained in the observation, resource utilization levels and/or various configuration parameters, as examples. Multiple clusters or PSE(s) can be formed by the tooling based on a target number of clusters determined by the tool or specified by the customer. After formation of clusters or PSE(s), the BR administrator can change the PSE(s) by combining clusters or requesting more clusters to be formed. The PSE(s) formed reflect a historical view of how the customer environment defines the periods of date/time for which a given PSE is to apply. Specification of future date/time can be based on the historical date/time used to form the PSE and/or can be based on customer knowledge of business cycles.
A given PSE environment describes the average and standard deviation for key RTO metrics and the average and standard deviation for operation duration. A given PSE environment applies to one or more specific date/time ranges. At any point in time, in this embodiment, there is one PSE environment for a RS that describes the environment current for execution of the business application associated with the RS. There is a PSE associated with the date/time ranges during which the business application associated with the RS may be active.
At any time the RS is actively being monitored, there is one PSE current for that RS. Observations made by BR on key RTO metrics and operation execution duration during runtime are applied to maintain the running average and standard deviation of statistics associated with the one PSE current at the point in time the observation is made. When updating running averages and standard deviation statistics for operation execution duration, the retrieved resource data is evaluated to insure the operation being reported occurred within the current PSE time/date range and multiple observations reporting the same time/date for an operation are consolidated into a single event.
If the BR administrator alters the RS by adding resource operations, observations from the log are selected in tool space based on the same criteria as used in building the original PSE(s) for the RS. Observations for the resource operation being added to the RS are best fit to the existing PSE(s) for the RS. This may be performed non-disruptively. From the observations best fit to a PSE, statistics for key RTO metrics and operating execution duration are calculated. When calculating operating execution duration statistics, the time associated with the operation is used to fit to the set of PSE(s). The time of execution of the operation may differ from the time the observation record was logged. When forming operation duration statistics, observations having the same date/time of operation execution are consolidated into a single event. When the resource is added to the runtime environment for the RS, new observations from the resource are accumulated into the current PSE(s) associated with the RS.
If a resource operation is to be removed from a PSE, it may be removed non-disruptively, if it is not being used in validation. Otherwise, it may be removed disruptively (i.e., stop active monitoring).
Altering the runtime environment definition of PSE(s) adheres to the same set of constraints as applied to the BR administrator build time for PSE(s). There is a set of PSE(s) that apply to the times that the RS is to be actively monitored. The set of PSE(s) does not overlap in date/time of applicability. When a new set of PSE(s) is to be applied to a RS, there are two options: the RS may be stopped from actively monitoring to maintain a policy and the new PSE environment applied to the RS, or the PSE environment can be applied to the runtime RS environment. A change to the PSE environment when the RS is not actively monitoring for policy enforcement occurs through tooling when the BR administrator applies a new set of PSE(s) to the RS. Subsequently, the altered RS may resume active monitoring. Any validated policy is revalidated as the PSE environment may have caused operation execution durations to be different than previously.
To change the PSE environment associated with a RS actively being monitored (i.e., non-disruptively), the BRMD entry associated with each resource in the RS is altered to reflect the new PSE environment. The RS is altered to cause any validated policies to be revalidated. For example, the administrator is notified to revalidate the policies. The policy currently being enforced is revalidated against the new set of PSE(s) based on the new PSE operation execution duration. If the existing policy cannot be validated against the new set of PSE(s), the change in PSE environment cannot be accomplished against the RS while it is actively being monitored. If the current policy can be validated against the new set of PSE(s), a transaction is started, updates to the resource(s) within the RS are made and the transaction commits. Once committed, new observations associated with the RS update the statistics for resource key RTO metrics and operation execution duration associated with the one PSE currently active for the RS.
Tooling for cluster analysis links to other BR services in a variety of ways. BR runtime recognizes when there are sufficient observations to trigger a request for cluster analysis based on defaults or customer adjusted statistical thresholds. BR runtime sends a notification to the BR administrator's mailbox indicating cluster analysis may be desirable. Cluster analysis output is to be appropriate as input to the BR Eclipse plugin. From the cluster analysis output, the BR plugin builds a table for each operation associated with a resource. Each table includes rows representing the PSE and the operation execution duration for that PSE. Operation execution duration is maintained by BR runtime as a running average. Associated with each PSE, BR builds representations of the resource key RTO metrics and associated statistics which are maintained by BR runtime as a set of running averages.
In one implementation of the BR system, the persistence for the Pattern System Environments is provided and implemented as a number of relational database tables in the BR system datastore that physically resides in the BR environment. That database is created at installation time, and the PSE tables are created and initialized (if necessary) at that time. One typical access mechanism will be via JDBC calls from the BR UI component, as well as from the Recovery Segment, using JDBC type 4 drivers. One example of a physical model of the PSE table (and any other related tables) is depicted in
As an example, Pattern System Environment database table 1200 is used to maintain the set of PSEs that the customers wish to use for policy validation. One embodiment of Pattern System Environment table 1200 is shown below. The field names having an asterisk associated therewith represent the columns that are surfaced as externals of the Recovery Segment.
In one embodiment, PSE RESOURCE table 1202 includes, for instance, the list of IT resources that provide data for the PSE.
In one embodiment, PSE Operation table 1204 includes, for instance, the list of properties and operations for the IT resources included in the construction of the PSE.
As an example, PSE Observation table 1206 is an intersection table used to materialize the many-to-many relationships between a PSE and an observation. An observation can be used to form multiple PSEs, and a PSE has multiple observations. This table maintains the list of observation records that are used to formulate the cluster, and the list of clusters that an observation was used to formulate. One example of the table is shown below.
Policies are defined to a RS, for a given PSE, or a set of PSEs. Policy validation is performed for a set of PSEs, and the information on operation execution timings is managed per PSE. Since, in this embodiment, PSE configuration does not allow gaps or overlaps in PSEs, at any given time, there is one and only one “current PSE” for a given RS. When the current PSE expires, that is called a “PSE boundary”. PSE boundaries are detected by a timer that is set for each current PSE. When the timer expires, BR assesses whether there is a policy for which a customer has issued ‘Activate’ that is targeted to the current PSE. If it finds one, it activates that policy. If BR does not find a policy that is targeted to the new PSE, the RS enters a deact-monitor state where it deactivates, no events are processed, but the prepared environment is left in tact.
For example, a customer may have PSE-A active from 8 am-6 pm, PSE-B from 6:01 pm-1 AM, and PSE-C from 1:01 AM-7:59 AM. If policy-m is for PSE-A and PSE-B, and policy-s is for PSE-C, then on the boundary between PSE-B and PSE-C, there is a deactivation of policy-m and an activation of policy-s. The opposite occurs on the boundary between PSE-C and PSE-A at 8:00 AM.
When a customer defines and validates policies, one specified for multiple PSEs is to be consistent with each other in terms of the prepared environment. Switching to another policy over a PSE boundary, during runtime operations, runs through policy activate, and does not require a re-prepare of the environment. An explicit policy change or topology change may cause a re-prepare, but not a dynamic switch of PSE. BR enforces that at policy validate time of the policies for a given RS, over the set of PSEs that they target.
When a customer activates a policy, BR checks the current PSE with the PSE on the activate request, and fully activates if there is a match. Otherwise, BR accepts the request for activation, and activates when the current PSE matches the target on the policy.
At any given time, for a specific RS, there is one PSE. At any given time for this RS, there is one policy being actively monitored. A single policy can span multiple PSEs, and the customer is not required to have different policies for the various PSEs in a RS.
A process to change the PSEs associated with a RS is also provided. This runs from the UI, where each individual task is its own transaction and committed (or aborted). The following are the set of tasks for this process, in one example:
This completes processing associated with changing a PSE.
In a further example, this processing is performed non-disruptively (i.e., system remains running and actively monitoring).
Described in detail herein is a capability for automatically forming a Pattern System Environment that provides a representation of a customer's business environment. In one example, the Pattern System Environment is used in providing business resiliency for the customer.
One or more aspects of the present invention can be included in an article of manufacture (e.g., one or more computer program products) having, for instance, computer usable media. The media has therein, for instance, computer readable program code means or logic (e.g., instructions, code, commands, etc.) to provide and facilitate the capabilities of the present invention. The article of manufacture can be included as a part of a computer system or sold separately.
One example of an article of manufacture or a computer program product incorporating one or more aspects of the present invention is described with reference to
A sequence of program instructions or a logical assembly of one or more interrelated modules defined by one or more computer readable program code means or logic direct the performance of one or more aspects of the present invention.
Advantageously, a capability is provided for automatically forming a Pattern System Environment based on information associated with one or more information technology (IT) resources of a customer. The Pattern System Environment is a representation of the IT resources of the customer's business environment, and the information on which the Pattern System Environment is based indicates how the IT resources are utilized during a business cycle of the customer. Advantageously, the representation is programmatic in that the information is stored in a data structure, which is capable of being searched and revised. Data can be extracted from the data structure.
Although various embodiments are described above, these are only examples. For example, the processing environments described herein are only examples of environments that may incorporate and use a Pattern System Environment and/or one or more other aspects of the present invention. Environments may include other types of processing units or servers or the components in each processing environment may be different than described herein. Each processing environment may include additional, less and/or different components than described herein. Further, the types of central processing units and/or operating systems or other types of components may be different than described herein. Again, these are only provided as examples.
Moreover, an environment may include an emulator (e.g., software or other emulation mechanisms), in which a particular architecture or subset thereof is emulated. In such an environment, one or more emulation functions of the emulator can implement one or more aspects of the present invention, even though a computer executing the emulator may have a different architecture than the capabilities being emulated. As one example, in emulation mode, the specific instruction or operation being emulated is decoded, and an appropriate emulation function is built to implement the individual instruction or operation.
In an emulation environment, a host computer includes, for instance, a memory to store instructions and data; an instruction fetch unit to obtain instructions from memory and to optionally, provide local buffering for the obtained instruction; an instruction decode unit to receive the instruction fetched and to determine the type of instructions that have been fetched; and an instruction execution unit to execute the instructions. Execution may include loading data into a register for memory; storing data back to memory from a register; or performing some type of arithmetic or logical operation, as determined by the decode unit. In one example, each unit is implemented in software. For instance, the operations being performed by the units are implemented as one or more subroutines within emulator software.
Further, a data processing system suitable for storing and/or executing program code is usable that includes at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements include, for instance, local memory employed during actual execution of the program code, bulk storage, and cache memory which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution.
Input/Output or I/O devices (including, but not limited to, keyboards, displays, pointing devices, DASD, tape, CDs, DVDs, thumb drives and other memory media, etc.) can be coupled to the system either directly or through intervening I/O controllers. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems, and Ethernet cards are just a few of the available types of network adapters.
Further, although the environments described herein are related to the management of availability of a customer's environment, one or more aspects of the present invention may be used to manage aspects other than or in addition to availability. Further, one or more aspects of the present invention can be used in environments other than a business resiliency environment.
Yet further, many examples are provided herein, and these examples may be revised without departing from the spirit of the present invention. For example, in one embodiment, it is indicated that there is only one active PSE at a time for a RS. In other embodiments, there may be more than one active PSE for a RS. Further, in the tables described herein, there are references to particular products, such as Workload Manager (WLM) or other products offered by International Business Machines Corporation or other companies. These again are only offered as examples, and other products may also be used. Yet further, other clustering and/or analysis tooling products may be employed. Additionally, although tables and databases are described herein, any suitable data structure may be used. There are many other variations that can be included in the description described herein and all of these variations are considered a part of the claimed invention.
Further, for completeness in describing one example of an environment in which a PSE may be utilized, certain components and/or information is described that is not needed for one or more aspects of the present invention. These are not meant to limit the aspects of the present invention in any way.
One or more aspects of the present invention can be provided, offered, deployed, managed, serviced, etc. by a service provider who offers management of customer environments. For instance, the service provider can create, maintain, support, etc. computer code and/or a computer infrastructure that performs one or more aspects of the present invention for one or more customers. In return, the service provider can receive payment from the customer under a subscription and/or fee agreement, as examples. Additionally or alternatively, the service provider can receive payment from the sale of advertising content to one or more third parties.
In one aspect of the present invention, an application can be deployed for performing one or more aspects of the present invention. As one example, the deploying of an application comprises providing computer infrastructure operable to perform one or more aspects of the present invention.
As a further aspect of the present invention, a computing infrastructure can be deployed comprising integrating computer readable code into a computing system, in which the code in combination with the computing system is capable of performing one or more aspects of the present invention.
As yet a further aspect of the present invention, a process for integrating computing infrastructure, comprising integrating computer readable code into a computer system may be provided. The computer system comprises a computer usable medium, in which the computer usable medium comprises one or more aspects of the present invention. The code in combination with the computer system is capable of performing one or more aspects of the present invention.
The capabilities of one or more aspects of the present invention can be implemented in software, firmware, hardware, or some combination thereof. At least one program storage device readable by a machine embodying at least one program of instructions executable by the machine to perform the capabilities of the present invention can be provided.
The flow diagrams depicted herein are just examples. There may be many variations to these diagrams or the steps (or operations) described therein without departing from the spirit of the invention. For instance, the steps may be performed in a differing order, or steps may be added, deleted, or modified. All of these variations are considered a part of the claimed invention.
Although embodiments have been depicted and described in detail herein, it will be apparent to those skilled in the relevant art that various modification, additions, substitutions and the like can be made without departing from the spirit of the invention and these are therefore considered to be within the scope of the invention as defined in the following claims.
| Number | Name | Date | Kind |
|---|---|---|---|
| 5023873 | Stevenson et al. | Jun 1991 | A |
| 5386561 | Huynh et al. | Jan 1995 | A |
| 5452452 | Gaetner et al. | Sep 1995 | A |
| 5481694 | Chao et al. | Jan 1996 | A |
| 5537542 | Eilert et al. | Jul 1996 | A |
| 5604863 | Allen et al. | Feb 1997 | A |
| 5631831 | Bird et al. | May 1997 | A |
| 5652908 | Douglas et al. | Jul 1997 | A |
| 5734837 | Flores et al. | Mar 1998 | A |
| 5790780 | Brichta et al. | Aug 1998 | A |
| 5797005 | Bahls et al. | Aug 1998 | A |
| 5797129 | Rohan | Aug 1998 | A |
| 5826080 | Dworzecki | Oct 1998 | A |
| 5887168 | Bahls et al. | Mar 1999 | A |
| 6012044 | Maggioncalda et al. | Jan 2000 | A |
| 6012152 | Douik et al. | Jan 2000 | A |
| 6041306 | Du et al. | Mar 2000 | A |
| 6336138 | Caswell et al. | Jan 2002 | B1 |
| 6385613 | Grewell et al. | May 2002 | B1 |
| 6393386 | Zager et al. | May 2002 | B1 |
| 6393485 | Chao et al. | May 2002 | B1 |
| 6408277 | Nelken | Jun 2002 | B1 |
| 6449688 | Peters et al. | Sep 2002 | B1 |
| 6480944 | Bradshaw et al. | Nov 2002 | B2 |
| 6625751 | Starovic et al. | Sep 2003 | B1 |
| 6732118 | Hermann et al. | May 2004 | B2 |
| 6785768 | Peters et al. | Aug 2004 | B2 |
| 6862696 | Voas et al. | Mar 2005 | B1 |
| 6874010 | Sargent | Mar 2005 | B1 |
| 6934247 | Bhattal et al. | Aug 2005 | B2 |
| 6954786 | Vered et al. | Oct 2005 | B1 |
| 6983321 | Trinon et al. | Jan 2006 | B2 |
| 6983362 | Kidder et al. | Jan 2006 | B1 |
| 7032186 | Gasser et al. | Apr 2006 | B1 |
| 7047337 | Armstrong et al. | May 2006 | B2 |
| 7058947 | Raja et al. | Jun 2006 | B1 |
| 7111297 | Sankaranarayan et al. | Sep 2006 | B1 |
| 7197749 | Thornton et al. | Mar 2007 | B2 |
| 7243267 | Klemm et al. | Jul 2007 | B2 |
| 7313573 | Leung et al. | Dec 2007 | B2 |
| 7325161 | Rakic et al. | Jan 2008 | B1 |
| 7395537 | Brown et al. | Jul 2008 | B1 |
| 7409356 | Geddes et al. | Aug 2008 | B1 |
| 7437611 | Agarwal et al. | Oct 2008 | B2 |
| 7490265 | Baskey et al. | Feb 2009 | B2 |
| 7499954 | Cherkauer et al. | Mar 2009 | B2 |
| 7509529 | Colucci et al. | Mar 2009 | B2 |
| 7529981 | Childress et al. | May 2009 | B2 |
| 7536585 | Keeton et al. | May 2009 | B1 |
| 7568019 | Bhargava et al. | Jul 2009 | B1 |
| 7587483 | Florissi et al. | Sep 2009 | B1 |
| 7610512 | Gerber | Oct 2009 | B2 |
| 7620953 | Tene et al. | Nov 2009 | B1 |
| 7627728 | Roeck et al. | Dec 2009 | B1 |
| 7650341 | Oratovsky et al. | Jan 2010 | B1 |
| 7707173 | Nanavati et al. | Apr 2010 | B2 |
| 7730363 | Takezawa et al. | Jun 2010 | B2 |
| 7743001 | Vermeulen et al. | Jun 2010 | B1 |
| 7747730 | Harlow | Jun 2010 | B1 |
| 7752310 | Kageyama | Jul 2010 | B2 |
| 7774457 | Talwar et al. | Aug 2010 | B1 |
| 7774458 | Trinon et al. | Aug 2010 | B2 |
| 7818421 | Machida | Oct 2010 | B2 |
| 7865582 | Santos et al. | Jan 2011 | B2 |
| 7933872 | Kulkarni et al. | Apr 2011 | B2 |
| 7934119 | Takamoto et al. | Apr 2011 | B2 |
| 7937706 | Casotto | May 2011 | B2 |
| 7958393 | Bobak et al. | Jun 2011 | B2 |
| 7962590 | Or et al. | Jun 2011 | B1 |
| 8051106 | Bird | Nov 2011 | B2 |
| 8260893 | Bandhole et al. | Sep 2012 | B1 |
| 20010056398 | Scheirer | Dec 2001 | A1 |
| 20010056554 | Chrabaszcz | Dec 2001 | A1 |
| 20020022952 | Zager et al. | Feb 2002 | A1 |
| 20020049749 | Helgeson et al. | Apr 2002 | A1 |
| 20020059512 | Desjardins | May 2002 | A1 |
| 20020069102 | Vellante et al. | Jun 2002 | A1 |
| 20020078130 | Thornton et al. | Jun 2002 | A1 |
| 20020078381 | Farley et al. | Jun 2002 | A1 |
| 20020091991 | Castro | Jul 2002 | A1 |
| 20020194045 | Shay et al. | Dec 2002 | A1 |
| 20020198727 | Ann et al. | Dec 2002 | A1 |
| 20030051186 | Boudnik et al. | Mar 2003 | A1 |
| 20030056013 | Laschkewitsch et al. | Mar 2003 | A1 |
| 20030078823 | Yoshioka et al. | Apr 2003 | A1 |
| 20030084100 | Gahan et al. | May 2003 | A1 |
| 20030093672 | Cichowlas | May 2003 | A1 |
| 20030135384 | Nguyen | Jul 2003 | A1 |
| 20030139956 | Guenther et al. | Jul 2003 | A1 |
| 20030200482 | Sullivan | Oct 2003 | A1 |
| 20030212580 | Shen | Nov 2003 | A1 |
| 20030212685 | Rakotoarivelo et al. | Nov 2003 | A1 |
| 20030225602 | Hagmann et al. | Dec 2003 | A1 |
| 20030236677 | Casati et al. | Dec 2003 | A1 |
| 20040034553 | Cole et al. | Feb 2004 | A1 |
| 20040054690 | Hillerbrand et al. | Mar 2004 | A1 |
| 20040078373 | Ghoneimy et al. | Apr 2004 | A1 |
| 20040119752 | Beringer et al. | Jun 2004 | A1 |
| 20040143470 | Myrick et al. | Jul 2004 | A1 |
| 20040158777 | Bae et al. | Aug 2004 | A1 |
| 20040162741 | Flaxer et al. | Aug 2004 | A1 |
| 20040181476 | Smith et al. | Sep 2004 | A1 |
| 20040186905 | Young et al. | Sep 2004 | A1 |
| 20040193476 | Aerdts | Sep 2004 | A1 |
| 20040199768 | Nail | Oct 2004 | A1 |
| 20040210452 | Aboujaoude et al. | Oct 2004 | A1 |
| 20040243699 | Koclanes et al. | Dec 2004 | A1 |
| 20050015641 | Alur et al. | Jan 2005 | A1 |
| 20050033600 | Geddes et al. | Feb 2005 | A1 |
| 20050043977 | Ahern et al. | Feb 2005 | A1 |
| 20050049906 | Leymann et al. | Mar 2005 | A1 |
| 20050060662 | Soares et al. | Mar 2005 | A1 |
| 20050071470 | O'Brien et al. | Mar 2005 | A1 |
| 20050086091 | Trumbly et al. | Apr 2005 | A1 |
| 20050091351 | Badovinatz et al. | Apr 2005 | A1 |
| 20050096949 | Aiber et al. | May 2005 | A1 |
| 20050119905 | Wong et al. | Jun 2005 | A1 |
| 20050125768 | Wong et al. | Jun 2005 | A1 |
| 20050172306 | Agarwal et al. | Aug 2005 | A1 |
| 20050177406 | Facciorusso et al. | Aug 2005 | A1 |
| 20050198244 | Eilam et al. | Sep 2005 | A1 |
| 20050228852 | Santos et al. | Oct 2005 | A1 |
| 20050235248 | Victoria et al. | Oct 2005 | A1 |
| 20050262242 | Byers et al. | Nov 2005 | A1 |
| 20060004265 | Pulkkinen et al. | Jan 2006 | A1 |
| 20060010234 | Reedy et al. | Jan 2006 | A1 |
| 20060020866 | Lo et al. | Jan 2006 | A1 |
| 20060037022 | Byrd et al. | Feb 2006 | A1 |
| 20060041505 | Enyart | Feb 2006 | A1 |
| 20060064335 | Goldszmidt et al. | Mar 2006 | A1 |
| 20060074731 | Green et al. | Apr 2006 | A1 |
| 20060074735 | Shukla et al. | Apr 2006 | A1 |
| 20060074993 | Pulamarasetti et al. | Apr 2006 | A1 |
| 20060106626 | Jeng et al. | May 2006 | A1 |
| 20060111921 | Chang et al. | May 2006 | A1 |
| 20060112383 | Chang et al. | May 2006 | A1 |
| 20060117221 | Fisher et al. | Jun 2006 | A1 |
| 20060123022 | Bird | Jun 2006 | A1 |
| 20060129562 | Pulamarasetti et al. | Jun 2006 | A1 |
| 20060149842 | Dawson et al. | Jul 2006 | A1 |
| 20060161444 | Lubrecht et al. | Jul 2006 | A1 |
| 20060161466 | Trinon et al. | Jul 2006 | A1 |
| 20060179136 | Loboz et al. | Aug 2006 | A1 |
| 20060190368 | Kesterman | Aug 2006 | A1 |
| 20060190583 | Whalen | Aug 2006 | A1 |
| 20060190775 | Aggarwal et al. | Aug 2006 | A1 |
| 20060218558 | Torii et al. | Sep 2006 | A1 |
| 20060224702 | Schmidt et al. | Oct 2006 | A1 |
| 20060245354 | Gao et al. | Nov 2006 | A1 |
| 20060245369 | Schimmelpfeng et al. | Nov 2006 | A1 |
| 20060248546 | Andreev et al. | Nov 2006 | A1 |
| 20060259526 | Booz et al. | Nov 2006 | A1 |
| 20060287875 | Reddy et al. | Dec 2006 | A1 |
| 20060293942 | Chaddha et al. | Dec 2006 | A1 |
| 20070005739 | Sadiq et al. | Jan 2007 | A1 |
| 20070011331 | Morimotto et al. | Jan 2007 | A1 |
| 20070027734 | Hughes | Feb 2007 | A1 |
| 20070038490 | Joodi | Feb 2007 | A1 |
| 20070038492 | Ryan et al. | Feb 2007 | A1 |
| 20070067296 | Malloy et al. | Mar 2007 | A1 |
| 20070079097 | Karnowski et al. | Apr 2007 | A1 |
| 20070100712 | Kilpatrick et al. | May 2007 | A1 |
| 20070112847 | Dublish et al. | May 2007 | A1 |
| 20070143166 | Leymann et al. | Jun 2007 | A1 |
| 20070150571 | Haga et al. | Jun 2007 | A1 |
| 20070165525 | Kageyama | Jul 2007 | A1 |
| 20070179826 | Cutlip et al. | Aug 2007 | A1 |
| 20070198678 | Dieberger et al. | Aug 2007 | A1 |
| 20070234408 | Burch et al. | Oct 2007 | A1 |
| 20070266029 | Baskey et al. | Nov 2007 | A1 |
| 20070271219 | Agarwal et al. | Nov 2007 | A1 |
| 20070276885 | Valiyaparambil et al. | Nov 2007 | A1 |
| 20070286219 | Knop et al. | Dec 2007 | A1 |
| 20070294406 | Suer et al. | Dec 2007 | A1 |
| 20070300204 | Andreev et al. | Dec 2007 | A1 |
| 20080016335 | Takahashi et al. | Jan 2008 | A1 |
| 20080052719 | Briscoe et al. | Feb 2008 | A1 |
| 20080063423 | Matoba | Mar 2008 | A1 |
| 20080140495 | Bhamidipaty et al. | Jun 2008 | A1 |
| 20080215909 | Bretschneider et al. | Sep 2008 | A1 |
| 20080294777 | Karve et al. | Nov 2008 | A1 |
| 20080295100 | Ainsworth | Nov 2008 | A1 |
| 20080317217 | Bernardini et al. | Dec 2008 | A1 |
| 20090037363 | Kozlov et al. | Feb 2009 | A1 |
| 20090077210 | Musman et al. | Mar 2009 | A1 |
| 20090125751 | Dawson et al. | May 2009 | A1 |
| 20090150456 | Balasubramanian et al. | Jun 2009 | A1 |
| 20090150887 | Sanghvi et al. | Jun 2009 | A1 |
| 20090171703 | Bobak et al. | Jul 2009 | A1 |
| 20090171704 | Bobak et al. | Jul 2009 | A1 |
| 20090171705 | Bobak et al. | Jul 2009 | A1 |
| 20090171707 | Bobak et al. | Jul 2009 | A1 |
| 20090171708 | Bobak et al. | Jul 2009 | A1 |
| 20090171730 | Bobak et al. | Jul 2009 | A1 |
| 20090171731 | Bobak et al. | Jul 2009 | A1 |
| 20090171732 | Bobak et al. | Jul 2009 | A1 |
| 20090171733 | Bobak et al. | Jul 2009 | A1 |
| 20090172149 | Bobak et al. | Jul 2009 | A1 |
| 20090172460 | Bobak et al. | Jul 2009 | A1 |
| 20090172461 | Bobak et al. | Jul 2009 | A1 |
| 20090172470 | Bobak et al. | Jul 2009 | A1 |
| 20090172668 | Bobak et al. | Jul 2009 | A1 |
| 20090172669 | Bobak et al. | Jul 2009 | A1 |
| 20090172670 | Bobak et al. | Jul 2009 | A1 |
| 20090172671 | Bobak et al. | Jul 2009 | A1 |
| 20090172674 | Bobak et al. | Jul 2009 | A1 |
| 20090172682 | Bobak et al. | Jul 2009 | A1 |
| 20090172687 | Bobak et al. | Jul 2009 | A1 |
| 20090172688 | Bobak et al. | Jul 2009 | A1 |
| 20090172689 | Bobak et al. | Jul 2009 | A1 |
| 20090172740 | Corl | Jul 2009 | A1 |
| 20090172769 | Bobak et al. | Jul 2009 | A1 |
| 20090249337 | Vasilevsky et al. | Oct 2009 | A1 |
| 20100280863 | Wilcock et al. | Nov 2010 | A1 |
| 20110004564 | Rolia et al. | Jan 2011 | A1 |
| Number | Date | Country |
|---|---|---|
| WO 9910814 | Mar 1999 | WO |
| Entry |
|---|
| Office Action for U.S. Appl. No. 11/965,862 dated Jun. 23, 2010. |
| Final Office Action for U.S. Appl. No. 11/966,495 dated May 14, 2010. |
| Office Action for U.S. Appl. No. 11/965,872 dated Jul. 6, 2010. |
| Office Action for U.S. Appl. No. 11/966,495 dated Oct. 29, 2009. |
| Office Action for U.S. Appl. No. 11/965,874 dated Mar. 18, 2010. |
| Office Action for U.S. Appl. No. 11/965,897 dated Mar. 31, 2010. |
| Office Action for U.S. Appl. No. 11/965,930 dated Apr. 1, 2010. |
| Evans, James, “Optimization Algorithms for Networks and Graphs, Second Edition”, 1992, CCR, ISBN 0824786025, Chapters 1-3 (pp. 1-76) and Chapter 11 (pp. 390-413). |
| Jolliffe, Ian T., “Principal Component Analysis”, 2002, Pringer, ISBN 0387954422, Chapters 1-4 (pp. 1-77), Chapters 6-7 (pp. 111-166) and Chapters 12-14 (pp. 299-405). |
| Lau, Hang T., “A Java Library of Graph Algorithms and Optimization Discrete Mathematics and Its Applications,” 2006, Chapman & Hall, ISBN 1584887184, Chapter 2.2 (pp. 39-43). |
| Horstmann, Cay S. et al., Core Java 2—vol. II—Advanced Features (7th Edition), Sun Microsystems Press, 2005, ISBN 0131118269, Chapter 1 (pp. 1-22). |
| “Parasoft BPEL Maestro,” http://www.parasoft.com/jsp/products/home.jsp?product=BPEL. |
| Final Office Action for U.S. Appl. No. 11/965,930 dated Sep. 29, 2010. |
| Final Office Action for U.S. Appl. No. 11/965,874 dated Oct. 14, 2010. |
| Office Action for U.S. Appl. No. 11/965,862 dated Dec. 7, 2010. |
| Final Office Action for U.S. Appl. No. 11/965,855 dated Dec. 22, 2010. |
| Office Action for U.S. Appl. No. 11/965,832 dated Dec. 22, 2010. |
| Final Office Action for U.S. Appl. No. 11/965,872 dated Dec. 22, 2010. |
| Patent Application for U.S. Appl. No. 12/975,520, filed Dec. 22, 2010 entitled “Real-Time Information Technology Environments”. |
| Office Action for U.S. Appl. No. 11/965,855 dated Apr. 30, 2010. |
| Bolie et al., “BPEL Cookbook: Best Practices for SOA-based Integration and Composite Applications Development”, 2006, Packt Publishing, ISBN 1904811337; Chapter 1; 12 pages. |
| Juric, et al., Business Process Execution Language for Web Services—An Architect and Developer's Guide to Orchestrating Web Services Using BPEL4WS, Second Edition; 2006; Packt Publishing; ISBN 1904811817; Chapter 5; 28 pages. |
| Horstman et al., Core Java 2: vol. I—Fundamentals (7th Edition); Core Series; 2003; Sun Microsystems, Inc., ISBN 0131472025; pp. 324-327. |
| Perry, J. Steven, “Java Management Extension: 1st Edition,” Jun. 15, 2002; O'Reilly & Associates, Inc.; ISBN 0596002459; Chapter 1 (pp. 1-32) and Chapter 7 (pp. 207-222). |
| Weerawarana et al., “Web Services Platform Architecture: SOAP, WSDL, WS-Policy, WS-Addressing, WS-BPEL, WS-Reliable Messaging, and More,” 2005; Pearson Education, Inc.; ISBN 0131488740; Chapter 14 (pp. 313-340). |
| Van Bon, Jan, “Foundations of IT Service Management Based on ITIL,” Sep. 2005; Van Haren Publishing; ISBN 9077212582; Chapter 6 (pp. 69-85). |
| Kreger et al., “JAVA and JMX—Building Manageable Systems,” Jan. 2003; ISBN 0672324083; pp. 54-64. |
| Final Office Action for U.S. Appl. No. 11/965,897 dated Sep. 17, 2010. |
| “BPEL Project”, http://www.eclipse.org/bpel/. |
| “Factor Analysis Using SAS PROC FACTOR”, http://www.utexas.edu/cc/docs/stat53.html. |
| “Principal Components and Factor Analysis”, http://statsoft.com/textbook/stfacan.html. |
| “WebSphere Intergration Developer”, http://www-306.ibm.com/software/integration/wid/about/?S—CMP=rnav. |
| Final Office Action for U.S. Appl. No. 11/965,922 dated Jul. 29, 2011. |
| Office Action for U.S. Appl. No. 11/965,845 dated Jul. 29, 2011. |
| Office Action for U.S. Appl. No. 11/965,832 dated Aug. 23, 2011. |
| Office Action for U.S. Appl. No. 11/965,902 dated Aug. 23, 2011. |
| Final Office Action for U.S. Appl. No. 11/965,838 dated Aug. 19, 2011. |
| Final Office Action for U.S. Appl. No. 11/965,913 dated Aug. 17, 2011. |
| Office Action for U.S. Appl. No. 11/965,894 dated Jul. 18, 2011. |
| Office Action for U.S. Appl. No. 11/966,619 dated Feb. 2, 2011. |
| Office Action for U.S. Appl. No. 11/965,926 dated Feb. 3, 2011. |
| Rood, Melody A., “Enterprise Architecture: Definition, Content & Utility,” IEEE, Jul. 1994, pp. 106-111. |
| Buchanan, Richard D., et al., “Aligning Enterprise and IT Investments with Corporate Goals,” OMG White Paper, 2002, p. 1-13 (cited by Examiner in Office Action for U.S. Appl. No. 11/965,926—no further date information available.). |
| Restriction Requirement for U.S. Appl. No. 11/965,889 dated Feb. 16, 2011. |
| Office Action for U.S. Appl. No. 11/965,913 dated Mar. 1, 2011. |
| Office Action for U.S. Appl. No. 11/965,845 dated Mar. 11, 2011. |
| Liu, Rong et al., “Modeling Business Contexture and Behavior Using Business Artifacts,” Lecture Notes in Computer Science, 2007, vol. 4495/2007, pp. 324-339 (cited by Examiner in Office Action for U.S. Appl. No. 11/965,845—no further date information available.). |
| Office Action for U.S. Appl. No. 11/965,922 dated Mar. 16, 2011. |
| Final Office Action for U.S. Appl. No. 11/965,832 dated May 12, 2011. |
| Office Action for U.S. Appl. No. 11/965,855 dated Oct. 3, 2011. |
| Office Action for U.S. Appl. No. 11/965,951 dated Oct. 5, 2011. |
| Final Office Action for U.S. Appl. No. 11/965,877 dated Nov. 8, 2011. |
| Final Office Action for U.S. Appl. No. 11/965,899 dated Nov. 22, 2011. |
| Final Office Action for U.S. Appl. No. 11/965,917 dated Dec. 21, 2011. |
| Final Office Action for U.S. Appl. No. 11/965,902 dated Dec. 21, 2011. |
| Final Office Action for U.S. Appl. No. 11/965,894 dated Dec. 27, 2011. |
| Final Office Action for U.S. Appl. No. 11/965,832 dated Jan. 9, 2012. |
| Final Office Action for U.S. Appl. No. 11/965,889 dated Jan. 11, 2012. |
| Office Action for U.S. Appl. No. 11/965,978 dated Jun. 22, 2011. |
| Office Action for U.S. Appl. No. 11/965,889 dated May 25, 2011. |
| Final Office Action for U.S. Appl. No. 11/966,619 dated Jun. 15, 2011. |
| Office Action for U.S. Appl. No. 11/965,899 dated Jun. 20, 2011. |
| Office Action for U.S. Appl. No. 11/965,877 dated Jun. 20, 2011. |
| Office Action for U.S. Appl. No. 11/965,862 dated Jul. 8, 2011. |
| Office Action for U.S. Appl. No. 11/965,917 dated Jul. 11, 2011. |
| Final Office Action for U.S. Appl. No. 11/965,926 dated Jul. 19, 2011. |
| Office Action for U.S. Appl. No. 11/966,495 dated Feb. 1, 2012. |
| Final Office Action for U.S. Appl. No. 11/965,862 dated Feb. 14, 2012. |
| Office Action for U.S. Appl. No. 11/965,872 dated Feb. 16, 2012. |
| Final Office Action for U.S. Appl. No. 11/965,951 dated Feb. 22, 2012. |
| Office Action for U.S. Appl. No. 11/965,926 dated Mar. 2, 2012. |
| Office Action for U.S. Appl. No. 11/965,874 dated Mar. 19, 2012. |
| Office Action for U.S. Appl. No. 11/965,845 dated Apr. 18, 2012. |
| Office Action for U.S. Appl. No. 11/965,922 dated Apr. 20, 2012. |
| Crawford, C. H. et al., “Toward an On Demand Service-Oriented Architecture,” IBM Systems Journal, vol. 44, No. 1, 2005, pp. 81-107. |
| Office Action for U.S. Appl. No. 11/965,894 dated Jun. 13, 2012. |
| Final Office Action for U.S. Appl. No. 11/965,889 dated Jun. 20, 2012. |
| Office Action for U.S. Appl. No. 11/965,902 dated Jul. 5, 2012. |
| Office Action for U.S. Appl. No. 12/975,520 dated Jul. 18, 2012. |
| Final Office Action for U.S. Appl. No. 11/965,906 dated Jul. 23, 2012. |
| Final Office Action for U.S. Appl. No. 11/965,894 dated Oct. 23, 2012. |
| Office Action for U.S. Appl. No. 11/965,862 dated Sep. 26, 2012. |
| Final Office Action for U.S. Appl. No. 11/965,926 dated Oct. 5, 2012. |
| Kramer, Jeff et al., “The Evolving Philosophers Problem: Dynamic Change Management,” Aug. 1991, pp. 1-33. |
| Final Office Action for U.S. Appl. No. 11/965,922 dated Aug. 28, 2012. |
| Office Action for U.S. Appl. No. 11/965,899 dated Aug. 28, 2012. |
| Final Office Action for U.S. Appl. No. 11/965,845 dated Aug. 29, 2012. |
| Number | Date | Country | |
|---|---|---|---|
| 20090171706 A1 | Jul 2009 | US |
