Download Big Data Computing and Communications: Second International by Yu Wang, Ge Yu, Yanyong Zhang, Zhu Han, Guoren Wang PDF

By Yu Wang, Ge Yu, Yanyong Zhang, Zhu Han, Guoren Wang

This ebook constitutes the complaints of the second one foreign convention on substantial facts Computing and Communications, BigCom 2016, held in Shenyang, China, in July 2016. The 39 papers awarded during this quantity have been conscientiously reviewed and chosen from ninety submissions.

BigCom is a world symposium devoted to addressing the demanding situations rising from sizeable info comparable computing and networking.

The convention is concentrated to draw researchers and practitioners who're attracted to sizeable facts analytics, administration, safeguard and privateness, conversation and excessive functionality computing in its broadest sense.

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Read Online or Download Big Data Computing and Communications: Second International Conference, BigCom 2016, Shenyang, China, July 29-31, 2016. Proceedings PDF

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Additional info for Big Data Computing and Communications: Second International Conference, BigCom 2016, Shenyang, China, July 29-31, 2016. Proceedings

Example text

Theorem 4. Our combinatorial auction mechanism guarantees the bid privacy preserving. Proof. Due to page limits, the proof is referred to [18]. 5 Simulation Results We evaluate the computation and communication overhead of our approximation algorithms with privacy preserving. Since computation overhead is dominated by the auctioneer and the agent in both auction models, we do not plot the bidders’ computation overhead. As shown in Fig. 1, the auctioneer spends more time in the identical auction model than in the combinatorial auction model.

2 illustrates a forwarding tree: The numbered cycles are media servers in numbered VDC, the cycle with s is IP camera s, and the squares represent end users. Cv = prvs denotes media server cost to open a media server at VDC v. Cuv = B × prvb represents bandwidth cost from the data center v to end user u, in which B is the bandwidth to forward one video stream. Similarly, Ce = B × prvb denotes the bandwidth cost of edge e = {v, v } and Cvv = 0. Let xij be an indicator to describe whether edge from participant i to j is chosen: xij = 1 when edge {i, j} is selected.

G. -E. Sun et al. Auction model, CA model). In the IA model, we assume that there exist a set of identical items denoted as I = {I1 , I2 , . . , Ih }, and m bidders denoted by B = {1, 2, . . , m} in the market. Each bidder i is only interested in a fixed number of items, denoted by Ni , and is willing to pay no more than vi for all of them. In the CA model, the items in the market are distinct, and each bidder i ∈ B wants to buy the items in a specified subset ci ⊆ I. Note that both in the IA model and the CA model, the demand of each bidder is inseparable, which means that bidder i will get all the items that he wants to buy if he wins.

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