LNCS 0558 -Job Scheduling Strategies for Parallel Processing

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Core Algorithms of the Maui Scheduler David Jackson, Quinn Snell, and Mark Clement Brigham Young University, Provo, Utah 84602 jacksond@supercluster.org, {snell, clement}@cs.byu.edu Abstract. The Maui scheduler has received wide acceptance in the HPC community as a highly configurable and effective batch scheduler. It is currently in use on hundreds of SP, O2K, and Linux cluster systems throughout the world including a high percentage of the largest and most cutting edge research sites. While the algorithms used within Maui have proven themselves effective, nothing has been published to date documenting these algorithms nor the configurable aspects they support. This paper focuses on three areas of Maui scheduling, specifically, backfill, job prioritization, and fairshare. It briefly discusses the goals of each component, the issues and corresponding design decisions, and the algorithms enabling the Maui policies. It also covers the configurable aspects of each algorithm and the impact of various parameter selections. 1 Introduction The Maui scheduler [1] has received wide acceptance in the HPC communityas an highlyconfigurable and effective batch scheduler. It is currentlyin use on hundreds of IBM SP-2, SGI Origin 2000, and Linux cluster systems throughout the world including a high percentage of the largest and most cutting edge research sites. While Maui was initiallyknown for its advance reservation and backfill scheduling capabilities, it also possesses manyadditional optimizations and job management features. There are manyaspects of the scheduling decision which must be addressed. This paper documents the underlying algorithms associated with the Maui scheduler. While Maui originated as a project designed to purelymaximize system utilization, it rapidlyevolved into a tool with a goal of maximizing scheduling performance while supporting an extensive arrayof policytools. The words performance and policy go a long wayto complicating this problem. 2 Overview Maui, like other batch schedulers [2,3,4], determines when and where submitted jobs should be run. Jobs are selected and started in such a wayas to not only enforce a site’s mission goals, but also to intelligentlyimprove resource usage and minimize average job turnaround time. Mission goals are expressed via a combination of policies which constrain how jobs will be started. A number of base concepts require review to set the groundwork for a detailed discussion of the algorithms. D.G. Feitelson and L. Rudolph (Eds.): JSSPP 2001, LNCS 2221, pp. 87–102, 2001. c○ Springer-Verlag Berlin Heidelberg 2001
88 D. Jackson, Q. Snell, and M. Clement 2.1 Scheduling Iteration Like most schedulers, Maui schedules on a iterative basis, scheduling, followed bya period of sleeping or processing external commands. Maui will start a new iteration when one or more of the following conditions is met: – a job or resource state-change (i.e. job termination, node failure) event occurs – a reservation boundaryevent occurs – the scheduler is instructed to resume scheduling via an external command – a configurable timer expires 2.2 Job Class Maui supports the concept of a job class, also known as a job queue. Each class mayhave an associated set of constraints determining what types of jobs can be submitted to it. These constraints can limit the size or length of the job and can be associated with certain default job attributes, such as memoryrequired per job. Constraints can also be set on a per-class basis specifying which users, groups, etc., can submit to the class. Further, each class can optionallybe set up to onlybe allowed access to a particular subset of nodes. Within Maui, all jobs are associated with a class. If no class is specified, a default class is assigned to the job. 2.3 QoS Maui also supports the concept of qualityof service (QoS) levels. These QoS levels maybe configured to allow manytypes of special privileges including adjusted job priorities, improved queue time and expansion factor targets, access to additional resources, or exemptions from certain policies. Each QoS level is assigned an access control list (ACL) to determine which users, groups, accounts, or job classes mayaccess the associated privileges. In cases where a job may possess access to multiple QoS levels, the user submitting the job mayspecify the desired QoS. All jobs within Maui are associated with a QoS level. If no QoS is specified, a default QoS is assigned. 2.4 Job Credentials Each batch job submitted to Maui is associated with a number of keyattributes or credentials describing job ownership. These credentials include the standard user and group ID of the submitting user. However, theyalso include an optional account, or project, ID for use in conjunction with allocation management systems. Additionally, as mentioned above, each job is also associated with a job class and QoS credential.
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Lecture Notes in Computer Science 2
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Dror G. Feitelson Larry Rudolph (Ed
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Preface This volume contains the pa
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Metrics for Parallel Job Scheduling
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