Patent Grant
8656019
1. Field of the Invention
The field of the invention is data processing, or, more specifically, methods, apparatus, and products for data processing workload administration in a cloud computing environment.
2. Description of Related Art
The development of the EDVAC computer system of 1948 is often cited as the beginning of the computer era. Since that time, computer systems have evolved into extremely complicated devices. Today's computers are much more sophisticated than early systems such as the EDVAC. Computer systems typically include a combination of hardware and software components, application programs, operating systems, processors, buses, memory, input/output devices, and so on. As advances in semiconductor processing and computer architecture push the performance of the computer higher and higher, more sophisticated computer software has evolved to take advantage of the higher performance of the hardware, resulting in computer systems today that are much more powerful than just a few years ago.
One of the areas of technology that has seen recent advancement is cloud computing. Cloud computing is increasingly recognized as a cost effective means of delivering information technology services through a virtual platform rather than hosting and operating the resources locally. However, the large scale take-off of cloud technology is hindered by significant issues of data security, privacy, and other policy challenges. Current cloud control policies are designed from centralized controller architecture limiting the ability of the network to efficiently reallocate resources in response to an increase in workload demand—with no provisions for self-regulating feedback to adaptively redistribute workload when policy requirements are no longer met.
Data processing workload administration in a cloud computing environment including distributing, by a workload policy manager on behalf of cloud clients according to a workload execution policy, data processing jobs among a plurality of clouds, each cloud comprising a network-based, distributed data processing system that provides one or more cloud computing services, the cloud computing services comprising Software as a Service (‘SaaS’) and Platform as a Service (‘PaaS’); deploying, by a job placement engine in each cloud according to the workload execution policy onto servers in each cloud, the data processing jobs distributed to each cloud; determining, by each job placement engine during execution of each data processing job, whether workload execution policy for each deployed job continues to be met by computing resources within the cloud where each job is deployed; and advising, by each job placement engine, the workload policy manager when workload execution policy for a particular job cannot continue to be met by computing resources within the cloud where the particular job is deployed.
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of example embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts of example embodiments of the invention.
Example methods, apparatus, and products for data processing workload administration in a cloud computing environment in accordance with the present invention are described with reference to the accompanying drawings, beginning with
PaaS is the delivery from a cloud of a computing platform and solution stack as a service. PaaS includes the provision of a software development platform designed for cloud computing at the top of a cloud stack. PaaS also includes workflow facilities for application design, application development, testing, deployment and hosting as well as application services such as team collaboration, web service integration and marshalling, database integration, security, scalability, storage, persistence, state management, application versioning, application instrumentation and developer community facilitation. These services are provisioned as an integrated solution over a network, typically the World Wide Web (‘web’) from a cloud. Taken together, SaaS and PaaS are sometimes referred to as ‘cloudware.’
In addition to SaaS and PaaS, cloud computing services can include many other network-based services, such as, for example, utility computing, managed services, and web services. Utility computing is the practice of charging for cloud services like utilities, by units of time, work, or resources provided. A cloud utility provider can, for example, charge cloud clients for providing for a period of time certain quantities of memory, I/O support in units of bytes transferred, or CPU functions in units of CPU clock cycles utilitized.
Managed services implement the transfer of all management responsibility as a strategic method for improving data processing operations of a cloud client, person or organization. The person or organization who owns or has direct oversight of the organization or system being managed is referred to as the offerer, client, or customer. The person or organization that accepts and provides the managed service from a cloud is regarded as a managed service provider or ‘MSP.’ Web services are software systems designed to support interoperable machine-to-machine interaction over a network of a cloud.
Web services provide interfaces described in a machine-processable format, typically the Web Services Description Language (‘WSDL’). Cloud clients interact with a cloud's web services as prescribed by WSDL descriptions using Simple Object Access Protocol (‘SOAP’) messages, typically conveyed using the HyperText Transport Protocol (‘HTTP’) with an eXtensible Markup Language (‘XML’) serialization.
The example cloud computing environment (102) of
The workload execution policy (117) is composed of associations of data processing job specifications for the data processing jobs and cloud computing resources provided by the clouds. The data processing jobs are data processing tasks requested by cloud clients and executed by servers (106, 107) in the clouds (104, 105). Data processing jobs can include anything executable on a cloud server, including for example, data entry in word processing documents, spreadsheet administrations, database management tasks, massively parallel computations, modeling of physical processes, email administration, operation of online presentation tools, calendar services, customer relations management, online conference support, accounting services, social networking, online sales and purchasing support, voice communications such Voice Over Internet Protocol (‘VoIP’), and so on, and so on, and so on.
The cloud computing resources provided by the cloud include runtime requirements of computing resources to be provided from a cloud for data processing jobs, and specifications of how cloud services and resources are to be provided can be set forth in service level agreements or ‘SLAs.’ Runtime requirements can be defined in terms of hardware resources, CPU speeds available from servers in a cloud, CPU cycles available in a cloud, I/O facilities available in a cloud, types and quantities of memory available in a cloud, and so on. An SLA is a specification of a level of service to be provided from a cloud to a cloud client. Specifications set forth as an ‘ SLA’ can be informal or formal, even in some cases part of a legally binding service contract. SLA specifications can include descriptions of services, priorities, responsibilities, guaranteed minima and maxima, delivery or performance times and schedules, and so on. SLAs can specify levels of availability, serviceability, performance, operation, or other attributes of a cloud service, such as costs and billing schedules. SLA specifications can be characterized as ‘target’ levels or ‘minimum’ levels, allowing cloud clients to be informed what to expect (the minimum), while also providing a measurable (average) target value that shows a typical level of performance.
Cloud computing resources provided by clouds is further explained with reference to Table 1. Each record in Table 1 associates computing resources with a particular cloud. The resources include services, run time resources, and SLA undertakings available in the clouds. In particular, records 1-5 represent respectively the availability in cloud (104) of PaaS as x86 hardware running Windows OS, Billing SaaS, General Ledger SaaS, availability of certain run time resources described as CPU, Memory, and I/O resources, and available SLA undertaking in terms of type and value, e.g., maximum job duration undertakings, maximum response time undertakings, and so on. Similarly, records 6-10 represent respectively the availability in cloud (105) of PaaS as Power hardware running a Linux OS, word processing SaaS, spreadsheet SaaS, availability of certain run time resources described as CPU, Memory, and I/O resources, and available SLA undertaking in terms of type and value.
Associations of data processing job specifications for the data processing jobs and cloud computing resources provided by the clouds can be implemented as illustrated, for example, by Table 2. The records in Table 2 represent a workload execution policies (117), that is, associations of specifications of data processing jobs in terms of job type for a particular cloud client and cloud computing resources expressed as run time requirements in terms of CPU, memory, and I/O requirements and required SLA undertakings expressed in terms of SLA specification type and value. In particular, the records in the example of Table 2 represent respectively workload execution policies specifying cloud computing resource requirements:
Tables 1 and 2 aid in explanation of the function of the workload policy manager (116). As mentioned, the workload policy manager (116) distributes data processing jobs (134) among the clouds (104, 105) by distributing, on behalf of cloud clients (118) according to a workload execution policy (117), data processing jobs among the clouds (104, 105). The workload policy manager (116), or at least the server (114) upon which the workload policy manager is deployed, can be configured with information of the kind illustrated by Tables 1 and 2, accessible in memory to the workload policy manager, so that the workload policy manager (116), upon receiving from a cloud client a request for data processing services, that is, a request for execution of a data processing job, can look up in Table 2, on the basis of the job type and the identity of the cloud client requesting the job, the cloud computing resources required by each such data processing job. The workload policy manager (116) then can determine by use of the information in Table 1 which cloud or clouds can provide the required cloud computing resources for any particular job and distribute each job to a cloud that can meet the workload execution policy requirements for the job. In a particular example according to the information in Tables 1 and 2, the workload policy manager (116), in distributing data processing jobs among clouds according to workload execution policies, will distribute spreadsheet jobs for cloud client UserID-2 only to cloud (105), because only cloud (105) can meet the workload execution policy requirement to provide the PaaS of Power hardware running a Linux OS.
In the example cloud computing (102) environment of
The job placement engines (110, 111) also monitor the process of job execution on the servers (106, 107) of the clouds and determine during execution of each data processing job whether workload execution policy for each deployed job continues to be met by computing resources within the cloud where each job is deployed. The job placement engines method can determine whether workload execution policy for each deployed job continues to be met by determining that workload execution policy for at least one deployed job is not currently met by the server upon which the at least one job is deployed, and then determining whether there is another server within the same cloud upon which the workload execution policy can be met. A job placement engine can then redeploy such a job within the same cloud if there is another server within the same cloud upon which the workload execution policy can be met—determining that workload execution policy for a particular job cannot continue to be met by computing resources within the cloud where the particular job is deployed only after trying a failing to find within the same cloud another server upon which the workload execution policy for the job can be met.
The job placement engines also advise the workload policy manager (116) when workload execution policy for a particular job cannot continue to be met by computing resources within the cloud where the particular job is deployed. The workload policy manager (116) then determines whether to leave the job where it is presently deployed and accept performance that does not really meet the policy, redistribute the job to another cloud where the policy can be met, or kill the job and advise the cloud client of failure.
In some embodiments, a job placement engine (110, 111), when advising the workload policy manager (116) that workload execution policy for a particular job cannot continue to be met by computing resources within the cloud where the particular job is deployed also advises the workload policy manager (116) of the existence of any jobs deployed within the same cloud having lower priorities than the particular job for which policy cannot continue to be met. In such embodiments, the workload policy manager also determines whether to preempt one or more of the lower priority jobs in the same cloud—and advise the job placement engine to redeploy the job within the same cloud—or redistribute the particular job to another cloud.
The arrangement of servers and other devices making up the example system illustrated in
For further explanation of cloud clients,
Stored in RAM (168) is an application (168), a user-level module of computer program instructions for carrying out particular data processing tasks requiring cloud computing services for application delivery to the cloud client or a user of the cloud client. Also stored in RAM (168) is an operating system (154). Operating systems useful data processing workload administration in a cloud computing environment according to embodiments of the present invention include UNIX™, Linux™, Microsoft XP™, AIX™, IBM's i5/OS™, and others as will occur to those of skill in the art. The application (168) and the operating system (154) in the example of
The cloud client (118) of
The example cloud client (118) of
The example cloud client (118) of
For further explanation of workload policy managers,
Also stored in RAM (168) in the example of
The server (114) of
The example server (114) of
For further explanation of job placement engines,
The server (108) running the job placement engine (110) in the example of
Also stored in RAM (168) in the example of
The server (108) of
The example server (108) of
For further explanation,
The method of
That is, in the method of
In view of the explanations set forth above, readers will recognize that the benefits of data processing workload administration in a cloud computing environment according to embodiments of the present invention include self-regulating or ‘autonomic’ adaptive redistribution of data processing workloads in cloud computing environments when workload execution policy requirements are not being met. Readers will also note that example embodiments of the present invention are described in this specification largely in the context of fully functional computer systems for data processing workload administration in a cloud computing environment. Readers of skill in the art will recognize, however, that the present invention also may be embodied in a computer program product disposed upon computer readable storage media for use with any suitable data processing system. Such computer readable storage media may be any storage medium for machine-readable information, including magnetic media, optical media, or other suitable media. Examples of such media include magnetic disks in hard drives or diskettes, compact disks for optical drives, magnetic tape, and others as will occur to those of skill in the art. Persons skilled in the art will immediately recognize that any computer system having suitable programming means will be capable of executing the steps of the method of the invention as embodied in a computer program product. Persons skilled in the art will recognize also that, although some of the example embodiments described in this specification are oriented to software installed and executing on computer hardware, nevertheless, alternative embodiments implemented as firmware or as hardware are well within the scope of the present invention.
It will be understood from the foregoing description that modifications and changes may be made in various embodiments of the present invention without departing from its true spirit. The descriptions in this specification are for purposes of illustration only and are not to be construed in a limiting sense. The scope of the present invention is limited only by the language of the following claims.
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