Bayesian Adaptive Path Allocation Techniques for Intra-Datacenter Workloads

Malik, Ali; Fréin, Ruairí de; Ke, Chih-Heng; Alyasiri, Hasanen; Izima, Obinna

doi:10.1109/icccn52240.2021.9522253

Cited by 2 publications

(1 citation statement)

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“…The approaches considered above look to deliver high QoS whilst achieving energy efficiency improvements. There exist methods that consider QoS from the perspective of link failure probability and the frequency of usage of links [28] but that do not consider energy efficiency. Finally, the authors in [1] introduced fast heuristic methods as well as iterative heuristic algorithms to select optimal controller placement and switch assignment to minimize total control traffic in SDN-based IoT networks with the goal of reducing power consumption.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks

et al. 2021

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Data Center Networks (DCNs) form the backbone of many Internet applications and services that have become necessary in daily life. Energy consumption causes both economic and environmental issues. It is reported that 10% of global energy consumption is due to ICT and network usage. Computer networking equipment is designed to accommodate network traffic; however, the level of use of the equipment is not necessarily proportional to the power consumed by it. For example, DCNs do not always run at full capacity yet the fact that they are supporting a lighter load is not mirrored by a reduction in energy consumption. DCNs have been shown to unnecessarily over-consume energy when they are not fully loaded. In this paper, we propose a new framework that reduces power consumption in software-defined DCNs. The proposed approach is composed of a new Integer Programming model and a heuristic link utility-based algorithm that strikes a balance between energy consumption and performance. We evaluate the proposed framework using an experimental platform, which consists of an optimization tool called LinGo for solving convex and non-convex optimization problems, the POX controller and the Mininet network emulator. Compared with the state-of-the-art approach, the equal cost multi-path algorithm, the results show that the proposed method reduces the power consumption by up to 10% when the network is experiencing a high traffic load and 63.3% when the traffic load is low. Based on these results, we outline how machine learning approaches could be used to further improve our approach in future work.

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Section: Related Workmentioning

confidence: 99%