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Evolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems (Paperback, 2012 ed.)
Loot Price: R2,934
Discovery Miles 29 340
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Evolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems (Paperback, 2012 ed.)
Series: Studies in Computational Intelligence, 419
Expected to ship within 10 - 15 working days
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One of the most challenging issues in modelling today's large-scale
computational systems is to effectively manage highly parametrised
distributed environments such as computational grids, clouds, ad
hoc networks and P2P networks. Next-generation computational grids
must provide a wide range of services and high performance
computing infrastructures. Various types of information and data
processed in the large-scale dynamic grid environment may be
incomplete, imprecise, and fragmented, which complicates the
specification of proper evaluation criteria and which affects both
the availability of resources and the final collective decisions of
users. The complexity of grid architectures and grid management may
also contribute towards higher energy consumption. All of these
issues necessitate the development of intelligent resource
management techniques, which are capable of capturing all of this
complexity and optimising meaningful metrics for a wide range of
grid applications. This book covers hot topics in the design,
administration and management of dynamic grid environments with a
special emphasis on the preferences and autonomous decisions of
system users, secure access to the processed data and services, and
application of green technologies. It features advanced research
related to scalable genetic-based heuristic approaches to grid
scheduling, whereby new scheduling criteria, such as system
reliability, security, and energy consumption are incorporated into
a general scheduling model. This book may be a valuable reference
for students, researchers, and practitioners who work on - or who
are interested in joining -- interdisciplinary research efforts in
the areas of distributed and evolutionary computation.
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