Task rescheduling optimization to minimize network resource consumption

doi:10.1007/s11042-015-2549-x

2015-03-25

PRE

DOI：10.1007/s11042-015-2549-x

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摘要

En 中文

An increasing number of big-data services are being deployed in a cloud computing environment, attracted by the on-demand service, rapid elasticity, and low maintenance costs. As a result, ensuring the quality of service has become an important research problem. Traditionally, task rescheduling is used to ensure a consistent quality of service in the event of failure of a virtual machine. However, the network resource consumption of different rescheduling methods varies. To address this problem, we propose a task rescheduling method that minimizes network resource consumption.The method includes three algorithms. The first obtains a set of good virtual machines from the large quantity of service-providing virtual machines using the skyline operation. A ranking algorithm then fuses the data size and the task emergency to identify significant tasks. Finally, we present an algorithm that automatically determines the optimal insertion point for each task. To verify the effectiveness of the proposed method, we extend the renowned simulator CloudSim and conduct a series of experiments. The results show that our method is more efficient than other methods in terms of network resource consumption.

Keyword:

Cloud computing

Reliability

Big data analysis

Resource consumption

Task rescheduling

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期刊

Multimedia Tools and Applications

IF:

论文数:

2.0W

被引数:

3.2W

机构

beijing university of posts & telecommunications

学者数:

1.4W

论文数: 1.2W

被引数: 9

Chung Hua University

学者数:

868

论文数: 994

被引数: 316

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