Journal Article10.1016/J.COMCOM.2018.04.008
Multi-objective optimization controller placement problem in internet-oriented software defined network
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TL;DR: This paper uses Adaptive Bacterial Foraging Optimization (ABFO) algorithm and redefine its computation rules to solve the Multi-objective Optimization Controller Placement (MOCP) problem, which focuses on achieving high network reliability, load balance among controllers, and low latency among controllers and switches.
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About: This article is published in Computer Communications. The article was published on 01 Jun 2018. The article focuses on the topics: Control theory & Multi-objective optimization.
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Citations
A Survey of Controller Placement Problem in Software-Defined Networking
TL;DR: The state of the art of controller placement problem is surveyed from the perspective of optimization objective and four aspects (latency, reliability, and cost and multi-objective) depending on their objective are classified.
Adopting elitism-based Genetic Algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment
TL;DR: In this article, the authors designed a two-level resource provisioning fog framework using Docker and containers and formulated the service placement problem in fog computing environment as a multi-objective optimization problem for minimizing the service time, cost, energy consumption and thus ensuring the QoS of IoT applications.
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Comprehensive Review of SDN Controller Placement Strategies
TL;DR: A comprehensive review of some of the existing approaches to identify the unique solutions offered, comprehend the different strategies and the challenges that exist as well as provide researchers with future directions aimed at improving the optimum location and allocation of controllers in software-Defined Networking application in wireless sensor network (WSN).
Optimizing the Response Time in SDN-Fog Environments for Time-Strict IoT Applications
TL;DR: DADO, a framework to identify the optimal deployment for distributed applications is proposed and implemented through the application of mixed-integer linear programming, and achieves scalable deployments over topologies of different sizes and growing user bases.
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