Cloud Computing: Data-Intensive Computing and Scheduling by Frederic Magoules, Jie Pan, Fei Teng

By Frederic Magoules, Jie Pan, Fei Teng

As increasingly more info is generated at a faster-than-ever fee, processing huge volumes of information is changing into a problem for facts research software program. Addressing functionality concerns, Cloud Computing: Data-Intensive Computing and Scheduling explores the evolution of classical options and describes thoroughly new tools and leading edge algorithms. The ebook delineates many techniques, versions, equipment, algorithms, and software program utilized in cloud computing.

After a common creation to the sector, the textual content covers source administration, together with scheduling algorithms for real-time initiatives and functional algorithms for person bidding and auctioneer pricing. It subsequent explains techniques to info analytical question processing, together with pre-computing, facts indexing, and knowledge partitioning. functions of MapReduce, a brand new parallel programming version, are then offered. The authors additionally talk about how one can optimize a number of group-by question processing and introduce a MapReduce real-time scheduling algorithm.

A precious reference for learning and utilizing MapReduce and cloud computing systems, this ebook offers a variety of applied sciences that exhibit how cloud computing can meet company specifications and function the infrastructure of multidimensional facts research purposes.

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Example text

Stanford Peers [Stanford University, 2011] is a peer-to-peer data trading framework, in which both auction and bartering models are applied. A local site wishing to replicate its collection holds an auction to solicit bids from remote sites by Àrst announcing its request for storage space. Each interested remote site then returns a bid, and the site with the lowest bid for maximum beneÀt is selected by the local site. Besides that, a bartering system supports a cooperative trading environment for producer and consumer participants, so that sites exchange free storage spaces to beneÀt both themselves and others.

Overview of cloud computing 5 Through pre-deÀned APIs, users can access, conÀgure and program cloud services. Qualitative aspects. Qualitative characteristics refer to qualities or properties of cloud computing, rather than speciÀc technological requirements. One qualitative feature can be realized in multiple ways depending on different providers. • Elasticity. Elasticity means that the provision of services is elastic and adaptable, which allows the users to request the service near real-time without engineering for peak loads.

Tasks belonging to different users are no longer distinguished one from another. The scheduling problem in such a context is match- 28 Cloud Computing: Data-Intensive Computing and Scheduling ing multi tasks to multi machines. As mentioned in the previous section, the optimal matching is an optimization problem, generally with NP-complete complexity. Heuristics are often applied as a sub-optimal algorithm to obtain relatively good solutions. This section intensively researches two types of strategies, static and dynamic heuristics.

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