Power‐efficient routing for SDN with discrete link rates and size‐limited flow tables: A tree‐based particle swarm optimization approach

Abstract

Summary Software‐defined networking is a promising networking paradigm for achieving programmability and centralized control in communication networks. These features simplify network management and enable innovation in network applications and services such as routing, virtual machine migration, load balancing, security, access control, and traffic engineering. The routing application can be optimized for power efficiency by routing flows and coalescing them such that the least number of links is activated with the lowest link rates. However, in practice, flow coalescing can generally overflow the flow tables, which are implemented in a size‐limited and power‐hungry ternary content addressable memory (TCAM). In this paper, a set of practical constraints is imposed to the software‐defined networking routing problem, namely, size‐limited flow table and discrete link rate constraints, to ensure applicability in real networks. Because the problem is NP‐hard and difficult to approximate, a low‐complexity particle swarm optimization–based and power‐efficient routing (PSOPR) heuristic is proposed. Performance evaluation results revealed that PSOPR achieves more than 90% of the optimal network power consumption while requiring only 0.0045% to 0.9% of the optimal computation time in real‐network topologies. In addition, PSOPR generates shorter routes than the optimal routes generated by CPLEX.

FANET optimization: a destination path flow model

“…Thus, the multicast transmission model, which is the link capacity optimization objectives can be stated as in (11). Table 2, The bandwidth parameters in Table 3 were obtained from Netflix standard bandwidth requirement for video streaming while PSO and SAO optimization parameters were obtained from the research paper presented in [29][30][31].…”

“…This position which represents the overall global information flow is called the global best (gbest). The mathematical model and details information on PSO implementation can be found in [31].…”

Artificial intelligence enabled software‐defined networking: a comprehensive overview

“…A large variety of meta-heuristic algorithms such as ACO [183][184][185][186][187][188][189][190][191], EAs [192][193][194], GAs [195][196][197][198][199][200][201][202][203], PSO [204][205][206][207][208][209], SA [210][211][212], bee colony optimisation-based [213,214], whale optimisation [215,216], FFO [217], BA [85], TLBO [87] and GWO [207] were used in SDN.…”

“…It is used for routing [204,205], resource management [206], multi-tenant virtual network customisation [218], multi-class routing [208] and detection of DDoS attacks [209].…”

“…Awad et al . [204] proposed, PSOPR, a PSO‐based power‐efficient routing approach for solving the problem of flow table overflow. The experimental results showed that PSOPR can achieve more than 90% of the optimal network power consumption while it requires only 0.0045–0.9% of the optimal computation time in real‐network topologies obtained from SNDlib (a library of test instances for Survivable fixed telecommunication Network Design).…”