SpongeNet: Towards bandwidth guarantees of cloud datacenter with two-phase VM placement

Hui-Feng Yu, Jiahai Yang, Cong Xu, Hui Wang, Zi Liang
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引用次数: 3

Abstract

In today's production-grade cloud datacenter, cloud service providers do not offer any bandwidth guarantees between VMs, which results in unpredictable performance of tenants' applications. To address this issue, we present SpongeNet, a solution that provides bandwidth guarantees for tenants with a novel network abstraction model and a two-phase VM placement algorithm. Prior solutions have significant limitations: 1) the existing coarse-grained network abstraction models cannot fully express tenants' network requirements and waste a lot of bandwidth resources in demand level; 2) the prior VM placement algorithms, take neither the two scheduling phases nor the tenants' requirements into consideration. As an extension of the existing studies, the proposed network abstraction model in this paper, called Fine-grained Virtual Cluster or FGVC, provides a more precise and flexible way for tenants to specify network requirements and realizes bandwidth saving. SpongeNet also proposes a novel two-phase VM placement algorithm that provides the optimal combinations of ordering policies and dispatching policies in consideration of different goals. Extensive simulations based on real application traces and 3-level tree topology show that SpongeNet provides 48% bandwidth saving than the state-of-art solutions (e.g., the Oktopus system), while significantly improving the throughput rates by 18% and response times by 92%.
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SpongeNet:通过两阶段虚拟机布局实现云数据中心的带宽保证
在今天的生产级云数据中心中,云服务提供商不提供任何虚拟机之间的带宽保证,这导致租户应用程序的性能不可预测。为了解决这个问题,我们提出了SpongeNet,这是一个通过新颖的网络抽象模型和两阶段虚拟机放置算法为租户提供带宽保证的解决方案。现有的解决方案存在明显的局限性:1)现有的粗粒度网络抽象模型不能完全表达租户的网络需求,在需求层浪费了大量的带宽资源;2)先前的虚拟机放置算法,既没有考虑两个调度阶段,也没有考虑租户的需求。本文提出的网络抽象模型是对已有研究的扩展,称为细粒度虚拟集群(Fine-grained Virtual Cluster, FGVC),为租户指定网络需求和实现带宽节约提供了更精确、更灵活的方式。SpongeNet还提出了一种新的两阶段虚拟机放置算法,该算法提供了考虑不同目标的排序策略和调度策略的最优组合。基于实际应用轨迹和3级树拓扑的广泛模拟表明,与最先进的解决方案(例如Oktopus系统)相比,SpongeNet提供了48%的带宽节省,同时显着提高了18%的吞吐率和92%的响应时间。
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