Genetic Algorithm Based Resource Minimization in Network Code Based Peer-to-Peer Network
M. Anandaraj, K. Selvaraj, P. Ganeshkumar, K. Rajkumar, S. Sriram

TL;DR
This paper introduces a genetic algorithm approach to minimize resource usage in network coding for P2P networks, effectively reducing coding nodes and links while maintaining high download rates.
Contribution
It proposes a new NP-hard network code minimization model considering dynamic topology and develops a specialized genetic algorithm with problem-specific operators.
Findings
Proposed GA outperforms random selection in resource minimization.
System achieves faster download times and higher throughput.
Effective handling of network topology changes in resource optimization.
Abstract
Block scheduling is difficult to implement in P2P network since there is no central coordinator. This problem can be solved by employing network coding technique which allows intermediate nodes to perform the coding operation instead of conventional store and forward the received data. There is a general assumption in this area of research so far that a target download rate is always attainable at every peer as long as coding operation is performed at all the nodes in the network. An interesting study is made that a maximum download rate can be attained by performing the coding operation at relatively small portion of the network. The problem of finding the minimal set of node to perform the coding operation and links to carry the coded data is called as a network code minimization problem (NCMP). It is proved to be NP hard problem. It can be solved using genetic algorithm (GA) because…
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Taxonomy
TopicsCooperative Communication and Network Coding · Wireless Networks and Protocols · Advanced Wireless Network Optimization
