By Weizhong Qiang, Xianghan Zheng, Ching-Hsien Hsu
This ebook constitutes the refereed complaints of the second one overseas 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 rigorously reviewed and chosen from 106 submissions. The papers are prepared in topical sections on cloud structure; functions; enormous information and social community; safeguard 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
An important parameter of the experiments is the billing cycle. To evaluate the impact of short and long billing cycles on our algorithms, we consider two diﬀerent billing cycles in the experiments: a long one equal to one hour, and a short one equal to ﬁve minutes. All costs are normalized by the cost of the schedule generate by HEFT  as baseline Cbase . We implement a modiﬁed HEFT algorithm for IaaS cloud environment. It always chooses the resource that ﬁnish selected task earliest. C . And then, the normalized cost of a schedule is deﬁned as: N C = Cbase Finally, to evaluate the impact of slackness degree of a schedule on our compaction and scheduling algorithms, we need to assign a deadline to each workﬂow.
Our aim is to determine VM with atypical behaviours, so we chose to study methods for associating a point to a single group. Partitioning methods can be classiﬁed into four classes deﬁned according to the membership function: a distance, a density, a grid or a hierarchical approach. The most widely used algorithm, K-MEANS , proposes to divide a set of points in k partitions to obtain a satisfactory similarity for all K partitions. This iterative approach seeks to determine K centroids, a point in space deﬁning the center of a partition.
3 Split Compaction Procedure For a selected split, it iteratively tries to insert tasks in the split to other resources. A success insert operation is deﬁned in Deﬁnition 6. The resources in Rc are selected ﬁrstly. If the insertion fails, resources in Rs are selected. 22 F. Wu et al. Algorithm 1. The IC- -SC Algorithm Input: S: the schedule to be compacted; makespan : the makespan of schedule S; D : the deadline, if required, for G. Output: S : a new schedule after compaction of S. 1: if deadline is required then 2: LST (texit ) = D 3: else 4: LST (texit ) = makespan 5: end if 6: update the LST s and LF T s of all tasks in T ; 7: while has an unprocessed split do 8: split ← nextSplit(); 9: call for compact(split); 10: end while After the insertion attempt of all tasks in selected split, there are 2 cases.