By Frederic Magoules, Jie Pan, Fei Teng

As increasingly more facts is generated at a faster-than-ever expense, processing huge volumes of knowledge is turning into a problem for info research software program. Addressing functionality concerns, Cloud Computing: Data-Intensive Computing and Scheduling explores the evolution of classical recommendations and describes thoroughly new tools and cutting edge algorithms. The publication delineates many ideas, types, 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 projects and useful algorithms for consumer bidding and auctioneer pricing. It subsequent explains ways to information analytical question processing, together with pre-computing, info indexing, and knowledge partitioning. functions of MapReduce, a brand new parallel programming version, are then awarded. The authors additionally speak about the right way to optimize a number of group-by question processing and introduce a MapReduce real-time scheduling algorithm.

A important reference for learning and utilizing MapReduce and cloud computing systems, this e-book offers quite a few applied sciences that display how cloud computing can meet company standards and function the infrastructure of multidimensional facts research functions.

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Extra info for Cloud Computing: Data-Intensive Computing and Scheduling

Example text

In auction-based schedulers, price is decided according to the given rules, which beneÀts consumers by expressing their real requirement strategically, rather than waiting for price adjustment in a passive manner. Auction-based schedulers are distinguished from each other by several characteristics. 1 Strategy types Number of participants. According to different numbers of bidders, auctions are classiÀed into the demand auction, supply auction and double auction. The English auction is an example of demand auction, in which n buyers bid for one service.

Moreover, a natural evolution from grid to cloud is important, because effort and time can be saved by technology reuse. As the migration to cloud is inevitable, programming and accessing cloud platforms should perform seamlessly and efÀciently. In the future, computational platforms will have a huge number of processing nodes, so traditional parallelization models such as batch processing and message passing models are not scalable enough to deal with large scale distributed computing. 2 Cloud Computing: Data-Intensive Computing and Scheduling Data management Data storage in the new cloud platform has changed.

There- Overview of cloud computing 17 fore, formal guidelines for contract description have to be standardized. Besides the technical strengths of cloud computing, users decide to adopt clouds for the economic reasons, so the business model of cloud computing should be more Áexible, offering clients scalable price options. For example, Amazon customers can choose purchasing models among on-demand, reserved, spot, and even free tier according to their own preferences. With more and more cloud solutions emerging, business models must be reformed to maintain customer loyalty or attract new attention.

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