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Cloud Computing: Data-Intensive Computing and Scheduling by Frédéric Magoulès, Jie Pan, Fei Teng

By Frédéric Magoulès, Jie Pan, Fei Teng

As progressively more information is generated at a faster-than-ever price, 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 options and describes thoroughly new tools and leading edge algorithms. The ebook delineates many thoughts, types, equipment, algorithms, and software program utilized in cloud computing.

After a basic advent to the sphere, the textual content covers source administration, together with scheduling algorithms for real-time projects and useful algorithms for person bidding and auctioneer pricing. It subsequent explains methods to information analytical question processing, together with pre-computing, info indexing, and information partitioning. purposes of MapReduce, a brand new parallel programming version, are then offered. The authors additionally talk about easy methods to optimize a number of group-by question processing and introduce a MapReduce real-time scheduling algorithm.

A helpful reference for learning and utilizing MapReduce and cloud computing systems, this booklet offers numerous applied sciences that reveal how cloud computing can meet company necessities and function the infrastructure of multidimensional information research functions.

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

Example text

Communication efÀciency. Communication efÀciency evaluates the communication overhead to capture a desirable global solution. Message passing adds communication overhead to transactions, so additional time is spent on allocation, rather than on computation. A good scheduling mechanism Ànds a near-optimum solution efÀciently. 2 Auction strategies Unlike in market-based models, an auction-based scheduler is a rule maker, rather than a price maker. The rules include how the users bid for services, how the sale price is determined, who the winning bidder is, how the resource is allocated, whether there are limits on time or proposal price, etc.

In other words, the autonomic system uses high-level rules to check and optimize its status and automatically adapt itself to changing conditions. According to the above introductions to the three computing paradigms, we can summarize the relationship among them. Utility computing is concerned with whether the packing computing resources can be used as a metered service on the basis of the user’s needs. It is indifferent to the organization of the resources, both in the centralized and distributed system.

Realizing a transaction on a shared-nothing architecture involves complex distributed locking, which is nontrivial work. The advantage of scalability with shared-nothing architecture is not an urgent need for transactional data management. Analytical data management applications are commonly used in business planning problem solving, and decision support. Data involved in analytical data management is often historical data. Historical data is usually large in size, and is read-mostly (or read-only), and occasionally batch updated.

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