Checkpoint and recovery methods in the ParaSol simulation system

Mascarenhas, Edward; Knop, Felipe; Pasquini, Reuben; Rego, Vernon

doi:10.1145/268437.268525

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…PARASOL is a multithreaded system for parallel simulation on shared-and distributedmemory environments, designed to support domainspecific Simulation Object Libraries. A brief overview of the PARASOL system appears in Mascarenhas et al (1997). Additional details on the PARASOL system can be found in Knop (1996) and Mascarenhas (1996).…”

Section: Introductionmentioning

confidence: 99%

Minimum cost adaptive synchronization

Mascarenhas

Knop²,

Rego

1997

Proceedings of the 29th Conference on Winter Simulation - WSC '97

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We present a novel adaptive synchronization algorithm, called the minimum average cost (MAC) algorithm, in the context of the PARASOL parallel simulation system. PARASOL is a multithreaded system for parallel simulation on shared-and distributedmemory environments, designed to support domainspecific Simulation Object Libraries. The proposed MAC algorithm is based on minimizing the cost of synchronization delay and rollback at a process, whenever its simulation driver must decide whether to either proceed optimistically or to delay processing. In the former case the risk is rollback cost, in the event of a straggler's arrival. In the latter case the risk is unnecessary delay, in the event a latecomer is not a straggler. In addition to the MAC algorithm and an optimal delay computation model, we report on some early experiments comparing the performance of MAC-based adaptive synchronization to optimistic synchronization.

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Section: Introductionmentioning

confidence: 99%

Minimum cost adaptive synchronization

Mascarenhas

Knop²,

Rego

1997

Proceedings of the 29th Conference on Winter Simulation - WSC '97

Self Cite

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Scheduling Parallel Jobs Using Migration and Consolidation in the Cloud

Liu

Chen

Qiu

et al. 2012

Mathematical Problems in Engineering

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An increasing number of high performance computing parallel applications leverages the power of the cloud for parallel processing. How to schedule the parallel applications to improve the quality of service is the key to the successful host of parallel applications in the cloud. The large scale of the cloud makes the parallel job scheduling more complicated as even simple parallel job scheduling problem is NP-complete. In this paper, we propose a parallel job scheduling algorithm named MEASY. MEASY adopts migration and consolidation to enhance the most popular EASY scheduling algorithm. Our extensive experiments on well-known workloads show that our algorithm takes very good care of the quality of service. For two common parallel job scheduling objectives, our algorithm produces an up to 41.1% and an average of 23.1% improvement on the average response time; an up to 82.9% and an average of 69.3% improvement on the average slowdown. Our algorithm is robust even in terms that it allows inaccurate CPU usage estimation and high migration cost. Our approach involves trivial modification on EASY and requires no additional technique; it is practical and effective in the cloud environment.

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Checkpoint and recovery methods in the ParaSol simulation system

Cited by 2 publications

References 9 publications

Minimum cost adaptive synchronization

Minimum cost adaptive synchronization

Scheduling Parallel Jobs Using Migration and Consolidation in the Cloud

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