SUMMARYThe paper presents a survey on OpenFlow related technologies that have been proposed as a means for researchers, network service creators, and others to easily design, test, and deploy their innovative ideas in experimental or production networks to accelerate research activities on network technologies. Rather than having programmability within each network node, separated OpenFlow controllers provide network control through pluggable software modules; thus, it is easy to develop new network control functions in executable form and test them in production networks. The emergence of OpenFlow has started various research activities. The paper surveys these activities and their results.
Takuya KUWAHARA †a) , Takayuki KURODA †b) , Manabu NAKANOYA †c) , Yutaka YAKUWA †d) , Nonmembers, and Hideyuki SHIMONISHI †e) , Member SUMMARY As IT systems, including network systems using SDN/NFV technologies, become large-scaled and complicated, the cost of system management also increases rapidly. Network operators have to maintain their workflow in constructing and consistently updating such complex systems, and thus these management tasks in generating system update plan are desired to be automated. Declarative system update with state space search is a promising approach to enable this automation, however, the current methods is not enough scalable to practical systems. In this paper, we propose a novel heuristic approach to greatly reduce computation time to solve system update procedure for practical systems. Our heuristics accounts for structural bottleneck of the system update and advance search to resolve bottlenecks of current system states. This paper includes the following contributions: (1) formal definition of a novel heuristic function specialized to system update for A* search algorithm, (2) proofs that our heuristic function is consistent, i.e., A* algorithm with our heuristics returns a correct optimal solution and can omit repeatedly expansion of nodes in search spaces, and (3) results of performance evaluation of our heuristics. We evaluate the proposed algorithm in two cases; upgrading running hypervisor and rolling update of running VMs. The results show that computation time to solve system update plan for a system with 100 VMs does not exceed several minutes, whereas the conventional algorithm is only applicable for a very small system.
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