Download e-book for kindle: Computation and Storage in the Cloud: Understanding the by Dong Yuan, Yun Yang, Jinjun Chen

By Dong Yuan, Yun Yang, Jinjun Chen

ISBN-10: 0124077676

ISBN-13: 9780124077676

Computation and garage within the Cloud is the 1st complete and systematic paintings investigating the problem of computation and garage trade-off within the cloud with a purpose to decrease the general software price. medical purposes tend to be computation and knowledge in depth, the place advanced computation initiatives take decades for execution and the generated datasets are usually terabytes or petabytes in dimension. Storing useful generated program datasets can retailer their regeneration fee after they are reused, let alone the ready time because of regeneration. besides the fact that, the big measurement of the medical datasets is a giant problem for his or her garage. through featuring cutting edge innovations, theorems and algorithms, this ebook may also help carry the price down dramatically for either cloud clients and repair services to run computation and information in depth medical purposes within the cloud.

• Covers price versions and benchmarking that specify the mandatory tradeoffs for either cloud services and users
• Describes numerous novel ideas for storing program datasets within the cloud
• comprises real-world case stories of medical examine applications
• Covers rate types and benchmarking that specify the mandatory tradeoffs for either cloud services and users
• Describes numerous novel thoughts for storing program datasets within the cloud
• comprises real-world case stories of clinical learn purposes

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Extra info for Computation and Storage in the Cloud: Understanding the Trade-Offs

Example text

2 presents a novel dynamic on-the-fly minimum cost benchmarking approach. This approach is suitable for situations in which more frequent benchmarking is requested at runtime. In this approach, by saving and utilising the pre-calculated results, whenever the application cost changes in the cloud, we can quickly calculate the new minimum cost benchmark. By keeping the benchmark dynamically updated, benchmarking requests can be instantly responded to on the fly. 1 Static On-Demand Minimum Cost Benchmarking Approach In this section, we present our on-demand minimum cost benchmarking approach.

The initial CTT (lines 1À21), where we need to recursively call the general CTTSP algorithm in calculating the weights for over-block edges (lines 11À14). Then we start the Dijkstra algorithm (lines 22À50). Whenever an in-block edge is found, we construct a new CTT with the following steps. First, we create a copy of the current CTT, in which the in-block edge is found (line 31). Next, we update the weights of edges: lines 32À34 are for updating the weights of in-block edges and lines 35À49 are for updating the weights of out-block edges.

Dj ... ... dh ... dk ... 7 CTT for general DDG. the initial CTT (lines 1À21), where we need to recursively call the general CTTSP algorithm in calculating the weights for over-block edges (lines 11À14). Then we start the Dijkstra algorithm (lines 22À50). Whenever an in-block edge is found, we construct a new CTT with the following steps. First, we create a copy of the current CTT, in which the in-block edge is found (line 31). Next, we update the weights of edges: lines 32À34 are for updating the weights of in-block edges and lines 35À49 are for updating the weights of out-block edges.

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Computation and Storage in the Cloud: Understanding the Trade-Offs by Dong Yuan, Yun Yang, Jinjun Chen


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