Multiobjective Optimization for Green Network Routing in Game Theoretical Perspective

• Published in: IEEE journal on selected areas in communications 2015-12, Vol.33 (12), p.2801-2814
• Main Authors: Zhang, Xiaoning, Wang, Sheng, Zhao, Yangming, Xu, Shizhong, Wang, Xiong, Gao, Xiujiao, Qiao, Chunming
• Format: Article
• Language: English
• Subjects: Computer networks; Energy consumption; Energy efficiency; Energy management; Game theory; Green Network; Linear programming; Load management; Mathematical models; Multi-Objective Optimization; Nash Bargaining; Negotiations; Networks; Objectives; Optimization; Pareto optimum; Routing; Routing (telecommunications); Sustainable development; Telecommunications
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2015.2481202

In this paper, we study the multiobjective optimization problem for green network routing. Although traditional commonly used multiobjective optimization methods can yield a Pareto efficient solution, they need to construct an aggregate objective function (AOF) or model one objective as a constraint in the optimization problem formulation. As a result, it is difficult to achieve a fair tradeoff among all objectives. Accordingly, we induce a Nash bargaining framework, which treats the two objectives as two virtual players in a game theoretic model, who negotiate how traffic should be routed to optimize both objectives. During the negotiation, each of them announces its performance threat value to reduce its cost, so the model is regarded as a threat value game. Our analysis shows that no agreement can be achieved if each player sets its threat value selfishly. To avoid such a negotiation break-down, we modify the threat value game to have a repeated process and design a mechanism to not only guarantee an agreement, but also generate a fair solution. Finally, to evaluate the efficiency of our proposed framework, we implement it into two multiobjective optimization cases for network green routing. The first case is load balancing and energy efficiency optimization for intradomain routing, and the second one is the energy efficiency optimization of two domains for interdomain routing.