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原文传递 POCO-MOEA: Using Evolutionary Algorithms to Solve the Controller Placement Problem.

题名：	POCO-MOEA: Using Evolutionary Algorithms to Solve the Controller Placement Problem.
作者：	Harned, S. I.
关键词：	Application software, Computing system architectures, Pattern recognition, Test and evaluation, Computer communications, Network protocols, Network topology, Evolutionary algorithms, Reliability, Annealing, Simulations, Sdn(software defined network), Mop(multi-objective problem), Poco(pareto optimal controller placement), Moea(multi objective evolutionary algorithm), Psa(pareto simulated annealing)
摘要：	One of the central tenets of a Software Defined Network (SDN) is the use of controllers, which are responsible for managing how traffic flows through switches, routers, and other data passing devices on a computer network. Most modern SDNs use multiple controllers to divide responsibility for network switches while keeping communication latency low. A problem that has emerged since approximately 2011 is the decision of where to place these controllers to create the most 'optimum' network. This is known as the Controller Placement Problem (CPP). Such a decision is subject to multiple and sometimes conflicting goals, making the CPP a type of Multi-Objective Problem (MOP). The theory of this thesis is that an MOEA can produce solutions to the CPP which are 'nearly optimal' while keeping execution time low compared to an exhaustive 'optimal' search. This research extends a network modeling tool called the Pareto Optimal Controller Placement (POCO) Framework with custom designed MOEA, called POCO-MOEA.
报告类型：	科技报告