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

By Frederic Magoules, Jie Pan, Fei Teng

As an increasing number of facts is generated at a faster-than-ever cost, 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 suggestions and describes thoroughly new equipment and leading edge algorithms. The ebook delineates many innovations, versions, 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 initiatives and functional algorithms for person bidding and auctioneer pricing. It subsequent explains techniques 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 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 invaluable reference for learning and utilizing MapReduce and cloud computing structures, this publication offers quite a few applied sciences that show how cloud computing can meet company standards and function the infrastructure of multidimensional information research functions.

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From the provider’s point of view, large scale virtual machines need to be allocated to thousands of distributed users, dynamically, fairly, and most importantly, proÀtably. It’s very challenging considering that resource provisioning mechanisms in existing systems such as grids mainly focus on application performance. , 2009c]. For adequate resources, one user will compare the price among different providers. For a scarce resource, users themselves becomes competitors who will impact the future price directly, or indirectly.

Historical data is usually large in size, and is read-mostly (or read-only), and occasionally batch updated. Analytical data management applications can beneÀt from cloud data storage. Analytical data management applications are argued to be well-suited to run in a Cloud environment [Abadi, 2009], since analytical data management matches well with shared-nothing architecture, and ACID guarantees are not needed for it. , 2008] is the Àrst issue raised over cloud computing platforms. From the provider’s point of view, large scale virtual machines need to be allocated to thousands of distributed users, dynamically, fairly, and most importantly, proÀtably.

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. The services are measured in Àne-grain, so that the amount of offering can perfectly match a consumer’s usage.

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