Journal Article
Machine Learning Empowered Intelligent Data Center Networking: A Survey
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TL;DR: This paper comprehensively investigate the application of machine learning to data center networking, and provides a general overview and in-depth analysis of the recent works, covering flow prediction, flow classification, load balancing, resource management, routing optimization, and congestion control.
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Abstract: To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization. To address these challenges, both academia and industry turn to artificial intelligence technology to realize network intelligence. To this end, a considerable number of novel and creative machine learning-based (ML-based) research works have been put forward in recent few years. Nevertheless, there are still enormous challenges faced by the intelligent optimization of data center networks (DCNs), especially in the scenario of online real-time dynamic processing of massive heterogeneous services and traffic data. To best of our knowledge, there is a lack of systematic and original comprehensively investigations with in-depth analysis on intelligent DCN. To this end, in this paper, we comprehensively investigate the application of machine learning to data center networking, and provide a general overview and in-depth analysis of the recent works, covering flow prediction, flow classification, load balancing, resource management, routing optimization, and congestion control. In order to provide a multi-dimensional and multi-perspective comparison of various solutions, we design a quality assessment criteria called REBEL-3S to impartially measure the strengths and weaknesses of these research works. Moreover, we also present unique insights into the technology evolution of the fusion of data center network and machine learning, together with some challenges and potential future research opportunities.
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Citations
Energy-Efficient Data Center Network Infrastructure With Network Switch Refresh Model
TL;DR: In this article , a model has been proposed that works on algorithms to recommend network switch replacements, which considers parameters impacting the performance and energy consumption of the switches, in addition to technical parameters, the proposed model has also evaluated the cost impacts of the replacement.
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Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation
Mathumohan Swamidoss,Duraimurugan Samiayya,Manikandan Gunasekar +2 more
TL;DR: The DQ-RGK algorithm optimizes energy consumption and resource allocation in optical networks by dynamically adjusting resources based on network traffic and environmental conditions. It tackles challenges such as static resource allocation, limited adaptability, inefficient power usage, and scalability issues. The DQ-RGK utilizes cluster head dynamic placement and Dynamic Quality of Service (QoS) to optimize network performance and resource allocation.
Blocking Island Paradigm Enhanced Intelligent Coordinated Virtual Network Embedding Based on Deep Reinforcement Learning
Ting-Yuan Wang,Peng Yang,Zhihao Wang,Haibin Cai +3 more
- 20 Sep 2022
TL;DR: A novel deep reinforcement learning (DRL) based coordinated VNE algorithm, called Intelligent Coordinated Embedding (ICE), which adopts an efficient resource abstraction model, Blocking Island (BI), which greatly reduces the search space and outperforms both the traditional non-DRL- based approach and the state-of-the-art DRL-based approach.
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