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Heterogeneous Task Co-location in Containerized Cloud Computing Environments

机译：容器化云计算环境中的异构任务共置

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摘要

Although cloud computing became a mainstream industrial computing paradigm, low resource utilization remains a common problem that most warehouse-scale datacenters suffer from. This leads to a significant waste of hardware resources, infrastructure investment, and energy consumption. As the diversity in application workloads grows into an essential characteristic in modern datacenters, task co-location of different workloads to the same compute cluster has gained immense popularity as a heuristic solution for resource utilization optimization. Although the existing co-location methodologies manage to improve resource efficiency to a certain degree, application QoS is usually sacrificed as a trade-off when dealing with resource interference between different applications. This paper proposes a containerized task co-location (CTCL) scheduler to improve resource utilization and minimize task eviction rate. Our CTCL scheduler (1) applies an elastic task co-location strategy to improve resource utilization; and (2) supports a dynamic task rescheduling mechanism to prevent severe QoS degradation from frequent task evictions. We evaluate our approach in terms of resource efficiency and rescheduling cost through the ContainerCloudSim simulator. Our experiments with the Alibaba 2018 workload traces demonstrate that CTCL could improve overall resource efficiency and reduce rescheduling rate by 38% and 99% respectively.
机译：尽管云计算已成为主流的工业计算范例，但是资源利用率低仍然是大多数仓库规模的数据中心所遭受的普遍问题。这导致硬件资源，基础设施投资和能源消耗的大量浪费。随着应用程序工作负载的多样性逐渐成为现代数据中心的基本特征，不同工作负载在同一计算群集上的任务共置已成为一种启发式解决方案，用于资源利用优化，因此非常受欢迎。尽管现有的共置托管方法设法在一定程度上提高资源效率，但是在处理不同应用程序之间的资源干扰时，通常会牺牲应用程序QoS作为折衷方案。本文提出了一种容器化任务共置（CTCL）调度程序，以提高资源利用率并最小化任务逐出率。我们的CTCL调度程序（1）应用弹性任务共置策略以提高资源利用率; （2）支持动态任务重新调度机制，以防止频繁的任务逐出而导致严重的QoS降低。我们通过ContainerCloudSim模拟器在资源效率和重新安排成本方面评估了我们的方法。我们对阿里巴巴2018年工作负载跟踪的实验表明，CTCL可以提高整体资源效率，并将重新计划率分别降低38％和99％。

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