Download Cloud Computing and Big Data: Second International by Weizhong Qiang, Xianghan Zheng, Ching-Hsien Hsu PDF
By Weizhong Qiang, Xianghan Zheng, Ching-Hsien Hsu
This booklet constitutes the refereed complaints of the second one foreign convention on Cloud Computing and large facts, CloudCom-Asia 2015, held in Huangshan, China, in June 2015.
The 29 complete papers and keynote speeches have been conscientiously reviewed and chosen from 106 submissions. The papers are geared up in topical sections on cloud structure; functions; mammoth information and social community; defense and privacy.
Read Online or Download Cloud Computing and Big Data: Second International Conference, CloudCom-Asia 2015, Huangshan, China, June 17-19, 2015, Revised Selected Papers PDF
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Extra info for Cloud Computing and Big Data: Second International Conference, CloudCom-Asia 2015, Huangshan, China, June 17-19, 2015, Revised Selected Papers
Example text
Therefore, we make significant savings in operating cost and make full use of various resources in the cloud data center. The algorithm has promising prospect in application. Keywords: Cloud resource scheduling Cloud data center Á VM live migration Á Energy-efficient Á VM placement Á 1 Introduction Cloud computing is a new business computing paradigm and service model which is following after the parallel computing, distributed computing and the gird computing. In terms of the computing resource providing, cloud computing is a computing paradigm that provide the computing resource to the users as a service through the network.
A split is a set of tasks covering one billing cycle of a resource in Rs . Each resource rl in Rs is divided into nb split(rl ) splits in (6). An index(rl ) parameter is set for every resource rl to record current split position. After compaction of one split in a resource, it is increased by one and point to the next split. The split start time and finish time are computed in (7) and (8). max r nb split(rl ) = st(split) = ∀ti :mt l ∈M {LF T (ti )} − i max r ∀ti :mt l ∈M min r ∀ti :mt l ∈M {EST (ti )} i τ {LF T (ti )} − τ × (nb split(rl ) − index(rl )) (6) (7) i f t(split) = st(split) + τ (8) For each compaction process, a nextSplit() procedure is called to select a next split.
2 Experimental Workflows Figure 1(a) and (b) shows the approximate structures of selected workflows we choose from [12] to evaluate the performance of our algorithm. They are: – Montage: astronomy; – CyberShake: earthquake science. They developed a workflow generator, which can create synthetic workflows. Using this workflow generator, they create different sizes for each workflow application in terms of total number of tasks. These workflows are available in DAX (Directed Acyclic Graph in XML) format from their website2 , from which we choose 6 sizes (50, 100, 200, 300, 400, 500) for each workflow.