A Simulation-Based Optimization Framework for Online Adaptation of Networks
A Simulation-Based Optimization Framework for Online Adaptation of Networks
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DOI：
10.1007/978-3-030-72792-5_41
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发表时间：
2020
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影响因子：
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通讯作者：
Stefan Herrnleben;Johannes Grohmann;Piotr Rygielski;Veronika Lesch;Christian Krupitzer;Samuel Kounev
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文献类型：
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作者：
Stefan Herrnleben;Johannes Grohmann;Piotr Rygielski;Veronika Lesch;Christian Krupitzer;Samuel Kounev
今天的数据中心面临着不断的变化，包括部署的服务、日益增长的复杂性和不断增长的性能需求。客户不仅期望托管服务的全天候可用性，而且还期望高响应性。除了优化软件架构和部署外，网络还必须适应不断变化和不稳定的需求。来自自适应系统的方法可以优化数据中心网络，以不断满足数据中心运营商和客户之间的服务水平协议（sla）。然而，现有的方法只关注特定的目标，如拓扑设计、功率优化或流量工程。在本文中，我们提出了一个可扩展的框架，该框架使用不同类型的仿真分析网络，并使用各种适应技术使其适应多个目标。分析各种建议的自适应，确保网络持续满足性能需求和sla。我们评估我们的框架w.r.t.寻找帕累托最优解决方案，考虑多维成本模型和典型数据中心网络上的可扩展性。评估表明，我们的方法可以正确地检测瓶颈和违反的sla，输出有效且成本最优的适应，并且即使在网络规模增加和可选配置数量增加的情况下，也能保持适应过程的运行时不变。
Today’s data centers face continuous changes, including deployed services, growing complexity, and increasing performance requirements. Customers expect not only round-the-clock availability of the hosted services but also high responsiveness. Besides optimizing software architectures and deployments, networks have to be adapted to handle the changing and volatile demands. Approaches from self-adaptive systems can optimize data center networks to continuously meet Service Level Agreements (SLAs) between data center operators and customers. However, existing approaches focus only on specific objectives like topology design, power optimization, or traffic engineering.In this paper, we present an extensible framework that analyzes networks using different types of simulation and adapts them subject to multiple objectives using various adaptation techniques. Analyzing each suggested adaptation ensures that the network continuously meets the performance requirements and SLAs. We evaluate our framework w.r.t. finding Pareto-optimal solutions considering a multi-dimensional cost model, and scalability on a typical data center network. The evaluation shows that our approach detects the bottlenecks and the violated SLAs correctly, outputs valid and cost-optimal adaptations, and keeps the runtime for the adaptation process constant even with increasing network size and an increasing number of alternative configurations.