Patent Application
20100145749
This invention relates to event-driven systems and, in particular, to a method and system for modeling and managing business and IT components and their interrelationships.
All major enterprises today require their Information technology (IT) infrastructure to conduct business. This business IT infrastructure is composed both of the IT itself (hardware and software), and of the business processes which this IT supports. The purpose of this IT infrastructure is to support the enterprise's business objectives. Despite this fact, current goals for optimizing a business IT infrastructure typically focus on IT measures (e.g., increase the site availability by 1%), when what the enterprise really cares about are the business objectives, such as total income generated by the infrastructure.
Optimizing the IT infrastructure according to business objectives is not a trivial task, as it is unclear how settings of parameters at the IT level will affect the business objectives. Moreover, optimizing the IT infrastructure is not a one time effort, as there may be changes that occur in the environment in which the infrastructure operates, that may render any pre-defined setting suboptimal. Two examples of such changes are failures of hardware and software components, and significant changes in the usage characteristics of this infrastructure.
Therefore, what is required is both the recognition that a business must automatically and continuously optimize its IT infrastructure according to business metrics, and an automatic mechanism for carrying out this optimization, taking into account significant changes in the environment of the IT infrastructure.
US20030187709A1 (Brodsky et al.) published Oct. 2, 2003 and entitled “Adaptive enterprise optimization (AEO) framework and methods” discloses an Adaptive Enterprise Optimization (AEO) server that allows users to model their individual business entities or parts of the value chain through and to model their decision/optimization problems based on these business entities. This is done using a high level modeling language. Based on the models and user input data, the AEO server is able to automatically generate appropriate global optimization problems, and solve them using advanced mathematical programming and constraint database programming technologies.
This reference is fairly typical of adaptive models that optimize business decisions, but it does not discuss optimization of the business infrastructure itself in order to achieve specified business objectives.
U.S. Pat. No. 6,557,035 (McKnight) issued Apr. 29, 2003 and entitled “Rules-based method of and system for optimizing server hardware capacity and performance” discloses a method of optimizing server hardware performance and predicting server hardware bottlenecks monitors server hardware utilization parameters over a selected time period and computes the averages of the measurements. The method then compares the computed averages to thresholds. If some of the computed averages are equal to or greater than the threshold, the method reports a performance bottleneck and provides a recommended solution for the bottleneck. The method predicts a future server hardware performance bottleneck by computing running averages of the measured server utilization parameters. The method uses a linear regression analysis to determine a trend in the running averages and compares the trend to threshold values to predict the occurrence of a performance bottleneck.
While this patent relates to optimization of server hardware performance per se, it is not directed to the problem of optimization or fine-tuning of hardware performance in order to achieve optimal performance according to business objectives of business software run on the hardware.
There is therefore a need to provide an improved method and system for optimizing performance of a business IT infrastructure in order to achieve desired business objectives.
It is therefore an object of the invention to provide an improved method and system for to provide an improved method and system for optimizing performance of a business IT infrastructure in order to achieve desired business objectives.
This object is realized in accordance with a broad aspect of the invention by a computer implemented method for optimizing an IT business infrastructure and business process parameters according to predetermined business objectives, the method comprising:
In such a method the optimizing is performed by a suitably programmed computer, which is preferably further adapted to:
In accordance with one preferred embodiment of the invention, there is provided a computer implemented method of optimizing performance of a business IT infrastructure according to pre-defined business objectives, the method comprising:
In order to understand the invention and to see how it may be carried out in practice, a preferred embodiment will now be described, by way of non-limiting example only, with reference to the accompanying drawings, wherein identical reference numerals are used to refer to similar components and in which:
In a preferred embodiment of the invention, the situation manager 10 is an off-the-shelf situation awareness unit, such as that known by AMIT by International Business Machines Inc. of Armonk, N.Y., USA. AMIT is an acronym for “Active Middleware Technology” and is described in U.S. Pat. No. 6,604,093 (Etzion et al.) published Aug. 5, 2003, entitled “Situation awareness system” and commonly assigned to the present assignee. AMIT is a situation management system that provides tools for defining intervals during which a given situation is meaningful and for detecting and reacting to the occurrence of the situation during such intervals. Such an interval is referred to as a “lifespan” and begins with an initiating event, or initiator, and ends with a terminating event, or terminator. AMIT enables manipulation of the initiator and terminator, such as by attachment of conditions to the initiating and terminating events. It also allows multiple, interrelated lifespans to run concurrently, with predefined relations between the lifespans.
Thus, AMIT enables temporal relations among events to be defined and detected simply and flexibly and serves as a general purpose vehicle for implementing a vast range of different applications. The events that are processed by such a system and the manner in which they are derived depends on the application that is implemented using the system, it being understood that AMIT operates independently of the application even though it serves as a kernel to the application.
The modeling unit 11 comprises a memory (not shown) that stores a business model 25 and a definitions unit 26 that creates an objectives definition that defines business objectives. The business rules are input manually based on the business objectives and an Overall Business Metric as part of the input provided to ARAD. The internal AMIT engine is part of the simulation environment. The business rules determine how IT level metrics affect the business objectives as well service level agreements or other contract definitions based on the objectives definition, although service level agreements or other contract definitions are not mandatory A service level agreement is a contractual agreement between two parties—a service provider—that provides a service, and a service consumer, regarding some service level, such as “ensuring that 95% of all transactions have a response time less then 3 seconds”. A service level agreement usually has two numerical quantities associated with it: a price paid by service consumer to ensure the service level agreement, and a penalty paid by the service consumer whenever the agreement is violated. There are several formalisms for specifying service level agreements. One such approach is described by Ludwig et al. in “A Service Level Agreement Language for Dynamic Electronic Services” published in Fourth IEEE International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems (WECWIS'02), Jun. 26-28, 2002, p.25. However, these can also be specified using the situation manager rule language, such as AMIT in the preferred embodiment. An optimizer 27 is coupled to the definitions unit 26 and determines a reference optimization of the business objectives based on the business model 25 incorporating the business rules and service level agreements or contract definitions as fed thereto by the definitions unit 26. A monitoring unit 28 monitors the performance of the IT infrastructure in respect of business objectives during runtime, and a comparator 29 coupled to the monitoring unit 28 compares the runtime performance of the business infrastructure in achieving the desired business objectives with the reference optimization achieved by the business model. If the difference between the runtime performance of the business infrastructure in achieving the desired business objectives and the reference optimization is significant, the business model 25 is automatically updated and optimizer 27 re-determines the reference optimization of the business objectives.
A Business Level Model 37 allows the calculation of the business metrics and is used to calculate the impacts of events at the IT level on the business objectives, as well as serving as inputs to an overall business metric computation 38, which is the quantity that measures the overall alignment of the IT infrastructure with the business objectives. The Business Level Model 37 may include things such as gains from commissions, explicit penalties paid to customers whenever service level guarantees are violated, customers deserting due to poor service, gaining new customers due to a good reputation, and losing customers due to poor reputation. In order to enable optimization, this economic model should also enable the calculation of an “end result”—a single quantity that can be used to quantify the alignment of the IT with the business objectives. An example of such a quantity is the total income generated by the IT infrastructure. This quantity may be referred to as the overall business metric.
The System User Behavior Model 35, the System Model 36 and the Business Level Model 37 together constitute the business model 25 shown in
Another aspect that is modeled in the architecture is a set of Actions/Policies 39 that can be changed by the optimizer 27. For a given set of actions or policies, the set of components described above can be used to calculate the value of the overall business metric. There are many possible examples of actions or policies—both at the IT and business level. One example of an IT level policy is a queuing policy, that determines the priority of incoming customers requests according to fields accompanying the request such as customer ID, customer type, amount in request, etc., so that higher priority requests are served before others. Setting such parameters has a potentially high impact on business objectives, as for example, requests from customers that increase the income more should be assigned a higher priority. An example of a business level policy or action is the penalty amount paid when violating service level agreements, based on rules such as how low penalties affect market share. It should be noted that just by using the above-described models, “what-if” analysis can be carried out, and can be used to make managerial decisions based on business objectives and return on investment (ROI).
All of the above models are coupled to the optimizer 27 that carries out a search on the set of allowable actions or policies, and attempts to find the set of actions/policies that optimizes the overall business metric. A System State Updater 40 is used to update all of the above models, whenever a significant difference is detected between the optimized models as determined by the optimizer 27 (and constituting the reference optimization), and the actual performance of the business IT infrastructure.
The above is a general architecture. For each type of model described above (System User Behavior model, System Model, etc.), several methods may be used to implement it. Examples are:
Examples are:
The unified IT simulation model 45 creates events, which are fed into the AMIT engine 46, allowing the calculation by the AMIT engine of the impact of the IT level events on the business metrics, and the calculation of the overall Business Level Objective. Situations determined by the AMIT engine 46 are fed both to the System Simulation Model 45 and to the Overall Business Metric computation. Both the impact of the IT level events on the business metrics, and the calculation of the overall Business Level Objective are calculated by one AMIT engine, using a set of rules. The division here is more a logical division—between a first set rules that specify how IT level events affect the business objectives, and a second set of rules from which the Overall Business Metric is calculated.
As noted above, the comparator 29 compares the actual business IT infrastructure and the ARAD optimization model and determines whether the difference is significant. Therefore, it is necessary to define both what constitutes such a difference, and an algorithm for recognizing such differences and updating the optimization model accordingly.
A significant difference may be defined as a difference between the business objectives and metrics measured on the actual IT infrastructure, and the same metrics as predicted by the ARAD models that is defined as “significant” according to predetermined criteria. It is thus necessary to pre-define what constitutes a significant difference between the model and the actual infrastructure. In one embodiment of the invention, only differences between business objectives as calculated by the models and the actual business objectives as measured by the IT infrastructure are taken into account. In such an embodiment, it is not necessary to look at differences of other parts of the model, for example the incoming traffic, as the assumption is that if it does not impact the business objectives, it does not matter. Then, as noted above, it is necessary to define what constitutes a significant difference between the modeled business objectives and measured business objectives, and this is defined by statistical tests such as Chi-squared, when the measured business objectives are treated as the actual distribution, and the modeled business objectives are treated as the empirical distribution.
An algorithm for recognizing this difference and updating the models may be defined as follows:
It will be understood that
It will also be understood that the situation manager according to the invention may be a suitably programmed computer. Likewise, the invention contemplates a computer program being readable by a computer for executing the method of the invention. The invention further contemplates a machine-readable memory tangibly embodying a program of instructions executable by the machine for executing the method of the invention.
In the method claims that follow, alphabetic characters and Roman numerals used to designate claim steps are provided for convenience only and do not imply any particular order of performing the steps.
