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<p>Software-defined network (SDN) orchestration, the problem of integrating and deploying multiple network control functions (NCFs) while minimizing suboptimal network states that can result from competing NCF proposals, is a challenging open problem. In this work, we formulate SDN orchestration as a multiobjective optimization problem, present an evolutionary algorithm designed to explore the NCF tradeoff space comprehensively and avoid local optima, and propose a new application-aware approach that explicitly models resource preferences of individual application workloads. Further, we propose a new logical application workload (LAW) abstraction to enable precomputation of the required relative positioning of an application's virtual machines (VMs) and allocation of these VMs in a single atomic step, leading to online algorithms that are one order of magnitude faster than existing solutions for placing data center workloads. For an instance of the SDN orchestration problem subject to four independent NCFs attempting to optimize network survivability, bandwidth efficiency, power conservation, and computational contention, we demonstrate that our approach enumerates a wider range of, and potentially better, solutions than current orchestrators, for data centers with hundreds of switches, thousands of servers, and tens of thousands of VM slots.
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<br>Subjects: software-defined networking; network state; data center network; genetic algorithms; virtual machine placement</div>
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Bairley, Alan M.</div>
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