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 e-book constitutes the refereed court cases of the second one overseas convention on Cloud Computing and large info, CloudCom-Asia 2015, held in Huangshan, China, in June 2015.

The 29 complete papers and keynote speeches have been rigorously reviewed and chosen from 106 submissions. The papers are equipped in topical sections on cloud structure; purposes; monstrous info and social community; safety and privacy.

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Read 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 resources for Cloud Computing and Big Data: Second International Conference, CloudCom-Asia 2015, Huangshan, China, June 17-19, 2015, Revised Selected Papers

Example text

To define a legal insert operation, we introduce a concept of slot first. A slot is a time fragment in a resource rl . Assuming the position of slot in resource rl is pos(slot), the start time st(slot) and finish time f t(slot) are defined as: 1. If Trl = N U LL, meaning rl is a new resource without any task assigned to it, then st(slot) = 0, f t(slot) = +∞, and pos(slot) = 0; 2. else, (a) if pos(slot) = 0, then st(slot) = max{LF T (ti )−lt(rl ), 0}, and f t(slot) = LST (tj ), where pos(ti ) = |Trl | − 1 and pos(tj ) = pos(slot); (b) else if pos(slot) > 0 and pos(slot) < |Trl |, then st(slot) = EF T (ti ), and f t(slot) = LST (tj ), where pos(ti ) = pos(slot)−1 and pos(tj ) = pos(slot); (c) else pos(slot) = |Trl |, then st(slot) = EF T (ti ) and f t(slot) = EST (tj ) + lt(rl ), where pos(ti ) = |Trl | − 1 and pos(tj ) = 0.

Our algorithm recursively searches the best group, comprising more VM depending on the similarity rate and criteria selected until all VMs belong to a group. A VM has an atypical behavior if it’s alone in its group or the VM number in the group is less than or equal to an arbitrary value. 40 GHz with 4 GB of RAM. 37. Computation time depend on records number into the database. 96 s on a large database (DC08). Figures 3, 4 and 5 show percentages of VM with pre-defined behaviors for 8 data centers selected in this paper.

Here, we have atypical VM, not belonging to any group. To compare with the K-means algorithm, let’s look Fig. 6(b), (c), (d), (e) and (h). Regarding DC04, the K-means algorithm returns the same result that our algorithm. That is to say it has also isolated the same point (the atypical VM). In contrast, for DC02,DC03, DC05 and DC08, the K-means algorithm has included these virtual machines in groups. By observing these groups carefully, there are points (VM) having different consumption profiles. For example, in DC05, the virtual machine that we consider as atypical (coordinates 677,160) is in a group of VM whose CPU usage is extended from 300 to 700 permil.

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