Online Scheduling with Migration Cost

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum Pub Date : 2012-05-21 DOI:10.1109/IPDPSW.2012.268

Shuangquan Yang

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引用次数: 5

Abstract

In this paper, we consider a new variation of the classical online scheduling problem. In our model, an online scheduler is allowed to migrate the assigned jobs to different machines. Live migration is a powerful tool for load balancing. However, migration will incur additional cost in the destination machines. In this paper, we study the scheduling problem with migration cost model. Suppose that a job with processing time p which is already scheduled on the machine A is removed and transferred to the machine B, the load of the machine A will decrease p, but the load of the machine B will increase (1+ r) p, where 0 ≤ r ≤ 1 is a constant and it is called the migration factor. First, we propose an approximation algorithm for arbitrary machines. Then we give an improved algorithm for the case of two machines. Both algorithms are better than list scheduling algorithm [2] if the migration factor is smaller than a certain value. Finally, we implement our algorithms both in real data and random data. The experimental results indicate that the performances of algorithms are very close to the optimal algorithm.

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带迁移成本的在线调度

本文考虑了经典在线调度问题的一个新变体。在我们的模型中，允许在线调度器将分配的作业迁移到不同的机器上。动态迁移是实现负载平衡的强大工具。但是，迁移将在目标机器中产生额外的成本。本文研究了具有迁移成本模型的调度问题。假设一个加工时间为p的作业已经安排在机器a上，把它移走，转移到机器B上，机器a的负荷会减少p，而机器B的负荷会增加(1+ r) p，其中0≤r≤1为常数，称为迁移因子。首先，我们提出了一种任意机器的近似算法。在此基础上，提出了一种适用于两台机器的改进算法。当迁移因子小于某一值时，两种算法都优于列表调度算法[2]。最后，我们在真实数据和随机数据中实现了我们的算法。实验结果表明，算法的性能与最优算法非常接近。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum

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