The EASY — LoadLeveler API project

Skovira, Joseph; Chan, Waiman; Zhou, Honbo; Lifka, David

doi:10.1007/bfb0022286

Cited by 157 publications

(87 citation statements)

References 1 publication

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“…To overcome these problems, various backfilling policies have been proposed and implemented in several production schedulers [10]. There are two common approaches to backfilling: EASY backfilling (EASY) [11] and conservative backfilling (CBF) [12]. In EASY, only one job has a reservation at any point of time.…”

Section: Job Schedulingmentioning

confidence: 99%

Job Mapping and Scheduling on Free-Space Optical Networks

Fujiwara

Koibuchi

2016

IEICE Trans. Inf. & Syst.

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SUMMARYA number of parallel applications run on a highperformance computing (HPC) system simultaneously. Job mapping and scheduling become crucial to improve system utilization, because fragmentation prevents an incoming job from being assigned even if there are enough compute nodes unused. Wireless supercomputers and datacenters with free-space optical (FSO) terminals have been proposed to replace the conventional wired interconnection so that a diverse application workload can be better supported by changing their network topologies. In this study we firstly present an efficient job mapping by swapping the endpoints of FSO links in a wireless HPC system. Our evaluation shows that an FSOequipped wireless HPC system can achieve shorter average queuing length and queuing time for all the dispatched user jobs. Secondly, we consider the use of a more complicated and enhanced scheduling algorithm, which can further improve the system utilization over different host networks, as well as the average response time for all the dispatched user jobs. Finally, we present the performance advantages of the proposed wireless HPC system under more practical assumptions such as different cabinet capacities and diverse subtopology packings.

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Section: Job Schedulingmentioning

confidence: 99%

Job Mapping and Scheduling on Free-Space Optical Networks

Fujiwara

Koibuchi

2016

IEICE Trans. Inf. & Syst.

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“…Typically, this is called a reservation for the first job. This backfilling is the most basic backfilling policy proposed by Lifka et al in [20] and it is called EASY-Backfilling. Many variants of this first proposal have been described in several papers.…”

Section: Related Workmentioning

confidence: 99%

The Resource Usage Aware Backfilling

Guim

Rodero

Corbalan

2009

Job Scheduling Strategies for Parallel Processing

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Abstract. Job scheduling policies for HPC centers have been extensively studied in the last few years, especially backfilling based policies. Almost all of these studies have been done using simulation tools. All the existent simulators use the runtime (either estimated or real) provided in the workload as a basis of their simulations. In our previous work we analyzed the impact on system performance of considering the resource sharing (memory bandwidth) of running jobs including a new resource model in the Alvio simulator. Based on this studies we proposed the LessConsume and LessConsume Threshold resource selection policies. Both are oriented to reduce the saturation of the shared resources thus increasing the performance of the system. The results showed how both resource allocation policies shown how the performance of the system can be improved by considering where the jobs are finally allocated. Using the LessConsume Threshold Resource Selection Policy, we propose a new backfilling strategy : the Resource Usage Aware Backfilling job scheduling policy. This is a backfilling based scheduling policy where the algorithms which decide which job has to be executed and how jobs have to be backfilled are based on a different Threshold configurations. This backfilling variant that considers how the shared resources are used by the scheduled jobs. Rather than backfilling the first job that can moved to the run queue based on the job arrival time or job size, it looks ahead to the next queued jobs, and tries to allocate jobs that would experience lower penalized runtime caused by the resource sharing saturation. In the paper we demostrate how the exchange of scheduling information between the local resource manager and the scheduler can improve substantially the performance of the system when the resource sharing is considered. We show how it can achieve a close response time performance that the shorest job first Backfilling with First Fit (oriented to improve the start time for the allocated jobs) providing a qualitative improvement in the number of killed jobs and in the percentage of penalized runtime.

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“…These scheduling strategies can be classified into either space sharing, or time sharing. Because a time shared environment is more difficult to establish for parallel processing in a multiple processor system, currently most commercial parallel systems only adopt space sharing such as the LoadLeveler scheduler from IBM for the SP2 [9]. However, one major drawback of space sharing is the blockade situation, that is, small jobs can easily be blocked for a long time by large ones.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation Schemes for Gang Scheduling

Zhou

Walsh

Brent

2000

Job Scheduling Strategies for Parallel Processing

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Gang scheduling is currently the most popular scheduling scheme for parallel processing in a time shared environment. In this paper we first describe the ideas of job re-packing and workload tree for efficiently allocating resources to enhance the performance of gang scheduling. We then present some experimental results obtained by implementing four different resource allocation schemes. These results show how the ideas, such as re-packing jobs, running jobs in multiple slots and minimising the average number of time slots in the system, affect system and job performance when incorporated into the buddy based allocation scheme for gang scheduling.

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The EASY — LoadLeveler API project

Cited by 157 publications

References 1 publication

Job Mapping and Scheduling on Free-Space Optical Networks

Job Mapping and Scheduling on Free-Space Optical Networks

The Resource Usage Aware Backfilling

Resource Allocation Schemes for Gang Scheduling

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