首页|Monte Carlo Simulation-Based Robust Workflow Scheduling for Spot Instances in Cloud Environments

Monte Carlo Simulation-Based Robust Workflow Scheduling for Spot Instances in Cloud Environments

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When deploying workflows in cloud environments,the use of Spot Instances(Sis)is intriguing as they are much cheaper than on-demand ones.However,Sis are volatile and may be revoked at any time,which results in a more challenging scheduling problem involving execution interruption and hence hinders the successful handling of conventional cloud workflow scheduling techniques.Although some scheduling methods for Sis have been proposed,most of them are no more applicable to the latest Sis,as they have evolved by eliminating bidding and simplifying the pricing model.This study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile Sis in cloud environments.Based on Monte Carlo simulation and list scheduling,a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this problem.With the Monte Carlo simulation framework,MCLS employs sampled task execution time to build solutions via deadline distribution and list scheduling,and then returns the most robust solution from all the candidates with a specific evaluation mechanism and selection criteria.Experimental results show that the performance of MCLS is more competitive compared with traditional algorithms.

constrained optimizationMonte Carlo simulationrobustnessSpot Instances(Sis)workflow scheduling

Quanwang Wu、Jianzhao Fang、Jie Zeng、Junhao Wen、Fengji Luo

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College of Computer Science,Chongqing University,Chongqing 400044,China

National Experimental Teaching Demonstration Center,Chongqing University,Chongqing 400044,China

College of Big Data and Software Engineering,Chongqing University,Chongqing 400044,China

School of Civil Engineering,The University of Sydney,Sydney 2006,Australia

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of Chongqing

6217206562072060cstc2020jcyjmsxmX0137

2024

清华大学学报自然科学版(英文版)
清华大学

清华大学学报自然科学版(英文版)

CSTPCDEI
影响因子:0.474
ISSN:1007-0214
年,卷(期):2024.29(1)
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