Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Upgrading and Repairing Networks
Upgrading and Repairing Networks
Digital and Analog Communication Systems
Digital and Analog Communication Systems
On-Line Realignment of Clients in Networked Databases
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
Autonomic behaviour of opportunistic network routing
International Journal of Autonomous and Adaptive Communications Systems
A Survey of Opportunistic Networks
AINAW '08 Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops
Pervasive and Mobile Computing
Networks Consolidation through Soft Computing
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Dynamic cache reconfiguration strategies for cluster-based streaming proxy
Computer Communications
Network redesign through clusters consolidation
SPECTS'09 Proceedings of the 12th international conference on Symposium on Performance Evaluation of Computer & Telecommunication Systems
Opportunistic resource utilization networks-A new paradigm for specialized ad hoc networks
Computers and Electrical Engineering
UKSIM '10 Proceedings of the 2010 12th International Conference on Computer Modelling and Simulation
Cooling-aware and thermal-aware workload placement for green HPC data centers
GREENCOMP '10 Proceedings of the International Conference on Green Computing
Opportunistic Reclustering: An Approach to Improve the Network Performance
ITNG '11 Proceedings of the 2011 Eighth International Conference on Information Technology: New Generations
Opportunistic networking: data forwarding in disconnected mobile ad hoc networks
IEEE Communications Magazine
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We have viewed an enterprise information network as a flexible topology, where each node seeks the opportunity to join a cluster having high association with its nodes to reduce the external bandwidth and thereby producing less carbon. The opportunistic clustering is modeled as an optimization problem and we utilized heuristics such as genetic algorithm (GA) and simulated annealing (SA) to search for the best opportunistic topology. We have proposed redesign operations such as move and swap nodes to change the existing topology into an opportunistic topology. Also, we defined an opportunistic factor ranging from +1 to –1 to measure the gain in bandwidth and the amount of reduction in carbon emissions. Our simulation results demonstrate positive opportunistic factor in individual clusters from 0.4 to 0.7 in move operation, thereby showing a total bandwidth gain of 21% and 19% within GA and SA, respectively. In the swap operation, the opportunistic factor reached a maximum of 0.3, thereby showing a bandwidth gain of 9.7% and 5.1% within GA and SA, respectively. It is also observed that the optimization within GA outperforms SA in offsetting carbon emission with a maximum of 22%. Copyright © 2012 John Wiley & Sons, Ltd.