Supporting Sustainable Virtual Network Mutations With Mystique
Supporting Sustainable Virtual Network Mutations With Mystique
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DOI：
10.1109/tnsm.2021.3059647
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发表时间：
2021-09
影响因子：
5.3
通讯作者：
Alessio Sacco;Matteo Flocco;Flavio Esposito;G. Marchetto
中科院分区：
文献类型：
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作者：
Alessio Sacco;Matteo Flocco;Flavio Esposito;G. Marchetto
自动化的持久尝试也渗透到网络中，通过对环境变化（例如需求）的反应，以自动化的方式测量、分析和控制自己的能力。当具备这些功能时，网络通常被贴上“自动驾驶”或“自主”的标签。在这方面，物理或虚拟资源的提供和编排对于边缘/云计算环境中的服务质量（QoS）保证和成本管理至关重要。为了有效地管理这些资源的生命周期，一个自动伸缩机制是必不可少的。然而，传统的基于阈值的策略和最近的基于机器学习（ML）的策略往往无法解决网络日益增长的复杂性，因为它们采用集中的方法。基于多智能体强化学习，我们提出了一种基于链路负载学习建立最小活动网络资源集的解决方案Mystique。随着交通需求的起伏，我们的自适应和自动驾驶解决方案可以伸缩，并以全自动、灵活和高效的方式对故障做出反应。我们的结果表明，所提出的解决方案可以减少网络能耗，同时提供足够的服务水平，优于其他基准自动扩展方法。
The abiding attempt of automation has also permeated the networks, with the ability to measure, analyze, and control themselves in an automated manner, by reacting to changes in the environment (e.g., demand). When provided with these features, networks are often labeled as “self-driving” or “autonomous”. In this regard, the provision and orchestration of physical or virtual resources are crucial for both Quality of Service (QoS) guarantees and cost management in the edge/cloud computing environment. To effectively manage the lifecycle of these resources, an auto-scaling mechanism is essential. However, traditional threshold-based and recent Machine Learning (ML)-based policies are often unable to address the soaring complexity of networks due to their centralized approach. By relying on multi-agent reinforcement learning, we propose Mystique, a solution that learns from the load on links to establish the minimal set of active network resources. As traffic demands ebb and flow, our adaptive and self-driving solution can scale up and down and also react to failures in a fully automated, flexible, and efficient manner. Our results demonstrate that the presented solution can reduce network energy consumption while providing an adequate service level, outperforming other benchmark auto-scaling approaches.