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Latency based group discovery algorithm for network aware cloud scheduling

delete2014-02-01
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Sheheryar Malik *
D
Denis Caromel
DOI:10.1016/j.future.2013.09.004delete
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摘要

摘要

En 中文
Cloud computing is a big paradigm shift of computing mechanism. It provides high scalability and elasticity with a range of on-demand services. We can execute a variety of distributed applications on cloud's virtual machines (computing nodes). In a distributed application, virtual machine nodes need to communicate and coordinate with each other. This type of coordination requires that the inter-node latency should be minimal to improve the performance. But in the case of nodes belonging to different clusters of the same cloud or in a multi-cloud environment, there can be a problem of higher network latency. So it becomes more difficult to decide, which node(s) to choose for the distributed application execution, to keep inter-node latency at minimum. In this paper, we propose a solution for this problem. We propose a model for the grouping of nodes with respect to network latency.The application scheduling is done on the basis of network latency. This model is a part of our proposed Cloud Scheduler module, which helps the scheduler in scheduling decisions on the basis of different criteria. Network latency and resultant node grouping on the basis of this latency is one of those criteria. The main essence of the paper is that our proposed latency grouping algorithm not only has no additional network traffic overheads for algorithm computation but also works well with incomplete latency information and performs intelligent grouping on the basis of latency. This paper addresses an important problem in cloud computing, which is locating communicating virtual machines for minimum latency between them and group them with respect to inter-node latency. (C) 2013 Elsevier B.V. All rights reserved.
Keyword:
Cloud scheduling
Latency grouping
Group partitioning
Network measurement
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期刊

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Future Generation Computer Systems-The International Journal of eScience
IF:
6.1
论文数:
6.9K
被引数:
2.3W

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