Controller placement algorithm to alleviate burdens on communication nodes

Ishigaki, Genya; Shinomiya, Norihiko

doi:10.1109/iccnc.2016.7440618

Cited by 13 publications

(8 citation statements)

References 17 publications

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“…Some attempts were made in the literature to guarantee “survivability” by increasing the number of supporting switch‐controller paths 35 . This task has been done by decreasing the load of middle nodes involved in the shortest control paths 36 ; by placing the controller where the largest number of separate paths would be built between the controller and switches 37 ; and by maximizing the multipath connections between target components 38 …”

Section: Related Workmentioning

confidence: 99%

Reliable controller placement in software‐defined networks using reliability covering graph and irregular cellular learning automata

Gholamrezaei

Mirjalily

Emadi

2021

Int J Communication

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Summary Software‐defined networking is a new paradigm in network management that has received much attention in recent years. One of the important challenges in designing software‐defined networks (SDNs) is the reliable controller placement problem (RCPP) that is the problem of finding the number and locations of controllers to satisfy the reliability requirements. The high complexity of exact reliability measurement led researchers to use estimation techniques that results in overestimating the number of controllers and incorrect placement. In this paper, we introduce a new concept named reliability covering graph (RCG) that enables us to use the exact reliability measurement (ERM) methods in solving the RCPP with desired level of complexity. To construct the RCG, for each candidate node, a subgraph with the adjustable radius r is extracted from the network graph. In the next step, the RCG is developed by calculating the exact reliability between the central node and any other nodes in the resulting subgraphs. In the last step, the RCG is converted to a machine learning tool named irregular cellular learning automata (ICLA) that determines the number and locations of controllers as well as the switches they cover. To study the efficiency of the proposed method, its performance is compared with the optimal results obtained from the ILP solver. The simulation results of 800 case studies on 100 standard topologies show that the average difference in results is 0.00125. Thanks to our innovative structure RCG, the number of controllers required is significantly reduced compared to previous methods.

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Section: Related Workmentioning

confidence: 99%

Reliable controller placement in software‐defined networks using reliability covering graph and irregular cellular learning automata

Gholamrezaei

Mirjalily

Emadi

2021

Int J Communication

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“…The objective is to achieve maximum utilization by every controller while taking load balancing into account. Another network partitioning based algorithm for CPP is proposed by Ishigaki et al, and they discuss the load on the communication nodes ( switches or controllers). When network size is too large, loads on the communication nodes play an important role.…”

Section: Controller Placement Problemmentioning

confidence: 99%

A survey and classification of controller placement problem in SDN

2018

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Summary Software defined networking emerges as a promising paradigm shift that decouples the control plane from the data plane. It has the ability to centrally monitor and control the network through softwarization, ie, controller. Deploying a single controller is inefficient to handle large network traffic; thereby, making multiple controllers are a necessity of current software defined networking in wide area networks. Placing multiple controllers in an optimum way, ie, controller placement is a vibrant research problem. Controller placement problem (CPP) is of twofold: the minimum number of controllers to be placed in a network and locations of these controllers. Numerous researchers in the last 5 years (2012 to November 2017) have proposed solutions for the CPP, which is an NP‐hard problem. In general, solutions are based on objective functions and their optimum solutions considering various factors (such as propagation latency between switches and controllers, and intercontrollers) and constraints (such as the capacity of controllers and switches). To the best of our knowledge, this is the first attempt of state‐of‐the‐art review on CPP. This paper classifies the CPP, critically analyzes the existing solutions, and finds limitations and future scope existing, which will help potential researchers in this area to innovate new solutions for CPP lying on this information.

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“…The authors in [45] consider burdens of all communication nodes. The burden is defined from the load of each node based on the number of edge disjoint paths.…”

Section: Rn Placement For Different Purposesmentioning

confidence: 99%