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Interval graph multi-coloring-based resource reservation for energy-efficient containerized cloud data centers

机译:节能图的节能集装箱化云数据中心的多色资源预留

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Containerized deployment of microservices has quickly become a well-known virtualization technology due to its higher portability, scalability, good isolation, and lightweight solutions. However, it faces several challenges in terms of the capital and operational expenses in large-scale data centers. In particular, services in cloud are usually instantiated as a group of containers, which continuously trigger frequent communication workloads and hence significantly degrades the service performance due to inefficient allocation of containers. Thus to deploy microservices, service providers must consider different types of objectives, such as optimizing the communication cost or the operational cost, which are joint objectives that have previously been studied independently. In this paper, we study the problem of communication-aware container-based advance reservation to optimize the energy and communication cost for microservices deployment. We applied the interval graph model to map the container reservation scenario of microservices and derived various performance bounds. Then, we propose greedy graph multi-coloring-based centralized and distributed algorithms to find an efficient allocation of containers. Through theoretical analysis and extensive experimental results, we demonstrate that the proposed approaches can decrease the total cost by up to 31% compared to the current state-of-the-art methods.
机译:由于其较高的便携性,可扩展性,良好的隔离和轻量级解决方案,Containized MicroServices的部署已迅速成为知名虚拟化技术。但是,它在大规模数据中心的资本和业务费用方面面临了几个挑战。特别是,云中的服务通常被实例化为一组容器,该容器连续触发频繁的通信工作负载,因此由于容器的低效分配而显着降低了服务性能。因此,为了部署微服务,服务提供商必须考虑不同类型的目标,例如优化通信成本或操作成本,这是先前研究过的联合目标。在本文中,我们研究了基于通信感知容器的提前预订问题,优化了微服务部署的能量和通信成本。我们应用了间隔图模型来映射微服务的容器预留方案并导出各种性能界限。然后,我们提出了贪婪的图形基于多色的集中和分布式算法,以找到容器的有效分配。通过理论分析和广泛的实验结果,我们证明,与目前最先进的方法相比,所提出的方法可以将总成本降低至31%。

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