一种云环境下的科学工作流均衡调度算法

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一种云环境下的科学工作流均衡调度算法

更新时间：2026-09-04

- 一种云环境下的科学工作流均衡调度算法
- A Scientific Workflow Trade-off Scheduling Algorithm in Cloud Environment
- 微电子学与计算机 2018年35卷第10期页码：121-126
- 作者机构：
  
  福州外语外贸学院机构信息系,福建,福州,350202
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目 61562063
- DOI：
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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吴文兵. 一种云环境下的科学工作流均衡调度算法[J]. 微电子学与计算机, 2018, 35(10): 121-126.

WU Wen-bing. A Scientific Workflow Trade-off Scheduling Algorithm in Cloud Environment[J]. 2018, 35(10): 121-126.
吴文兵. 一种云环境下的科学工作流均衡调度算法[J]. 微电子学与计算机, 2018, 35(10): 121-126. DOI：

WU Wen-bing. A Scientific Workflow Trade-off Scheduling Algorithm in Cloud Environment[J]. 2018, 35(10): 121-126. DOI：

摘要

为了优化云环境中预算约束下的科学工作流调度问题

提出一种工作流均衡调度算法BDWTS.算法以满足工作流预算约束并同步优化执行代价和执行时间为目标

将工作流调度划分为四个阶段:工作流分级、预算分割、任务选择和实例选择.工作流分级通过自顶向下的方式对所有工作流任务进行分级

预算分割中设计了六种用户预算在不同工作流分级上的子划分方法

任务选择通过最早开始时间原则选择优先的调度任务

实例选择则综合考虑时间因子和代价因子选择最优执行资源.通过仿真实验

证明了算法可以在预算约束下得到降低工作流执行时间和代价的均衡调度方案.

Abstract

For optimizing scientific workflow schedulimg with budget constraint in cloud

a Budget Division Workflow Trade-off Scheduling (BDWTS) algorithm is presented. With synchronous optimization of makespan and cost meeting budget constraint as the objective

the algorithm is divided into four stages during scheduling workflow:workflow leveling

budget division

task selection and instance selection. Workflow leveling partitions the workflow tasks into different levels by the top-down method

budget division allocates the user-defined budget to each defined levels by six strategies

task selection selects the scheduled-priority task by the earliest start time principle

instance selection selects the optimal execution resource by overall considering the time factor and cost factor. It is proved that our algorithm can obtain the trade-off scheduling scheme of the execution time and execution cost under meeting budget constraint by simulation experimental results.

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references

Arabnejad V, Bubendorfer K. Cost Effective and Deadline Constrained Scientific Workflow Scheduling for Commercial Clouds[C]. In International Symposium on Network Computing and Applications. IEEE, 2016: 106-113.

Wu C Q, Lin X, Yu D, et al. End-to-End Delay Minimization for Scientific Workflows in Clouds under Budget Constraint[J]. IEEE Transactions on Cloud Computing, 2015, 3(2):169-181. DOI: 169

Zeng L, Veeravalli B, Li X. Scalestar: Budget conscious scheduling precedence-constrained many-task workflow applications in cloud[C]. In 2012 IEEE 26th International Conference on Advanced Information Networking and Applications, 2012: 534-541.

Zheng W, Sakellariou R. Budget-Deadline Constrained Workflow Planning for Admission Control[J]. Journal of Grid Computing, 2013, 11(4):633-651. DOI: 633

Arabnejad V, Bubendorfer K, Ng b, et.al, A deadline constrained critical path heuristic for cost-effectively scheduling workflows[C]. In 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing, IEEE, 2015: 242-250.

Mao M, Humphrey M. Scaling and Scheduling to Maximize Application Performance within Budget Constraints in Cloud Workflows[C]. In International Symposium on Parallel % Distributed Processing. IEEE, 2013: 67-78.

Lin X, Wu C Q. On Scientific Workflow Scheduling in Clouds under Budget Constraint[C]. International Conference on Parallel Processing. IEEE, 2013: 90-99.

Juve G, Chervenak A, Deelman E, et al. Characterizing and profiling scientific workflows[J]. Future Generation Computer Systems, 2013, 29(3):682-692. DOI: 682

Wu F, Wu Q, Tan Y. Workflow scheduling in cloud: a survey[J]. Journal of Supercomputing, 2015, 71(9):1-46. DOI: 1

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