高吞吐量键值数据存储的分散准入控制

Young Ki Kim, M. HoseinyFarahabady, Young Choon Lee, Albert Y. Zomaya
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引用次数: 1

摘要

即使是高吞吐量的键值数据存储(如Cassandra、MongoDB和最近的Aerospike),工作负载激增也会严重阻碍其性能。在本文中,我们提出了一种用于高吞吐量键值数据存储的分散准入控制器。该控制器考虑到不同的服务质量(QoS)类别,显式地动态调节传入请求的释放时间。特别是,将这种控制器的实例分配给每个客户端,以实现其特定于客户端QoS需求的自主准入控制。这些控制器以分散的方式运行,只有本地性能指标、响应时间和队列等待时间。尽管使用了这种“最小”的运行时状态信息,我们的分散式许可控制器能够处理符合QoS要求的工作负载激增。在不同工作负载强度下的测试平台集群中,将所提出的接纳控制器与Aerospike的默认调度策略进行了性能评估。实验结果证实,在高速率工作负载下,与Aerospike的控制器相比,所提出的控制器在端到端响应时间方面的QoS满意度平均提高了近12倍。结果还显示,在高速工作负载的工作负载激增(峰值负载)期间,延迟的平均和标准偏差分别减少了31%和50%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decentralized Admission Control for High-Throughput Key-Value Data Stores
Workload surges are a serious hindrance to per-formance of even high-throughput key-value data stores, such as Cassandra, MongoDB, and more recently Aerospike. In this paper, we present a decentralized admission controller for high-throughput key-value data stores. The proposed controller dynamically regulates the release time of incoming requests explicitly taking into account different Quality of Service (QoS) classes. In particular, an instance of such controller is assigned to each client for its autonomous admission control specific to the client's QoS requirements. These controllers operate in a decentralized manner with only local performance metrics, response time and queue waiting time. Despite the use of such "minimal" run-time state information, our decentralized admission controller is capable of coping with workload surges respecting QoS requirements. The performance evaluation is carried out by comparing the proposed admission controller with the default scheduling policy of Aerospike, in a testbed cluster under various workload intensity rates. Experimental results confirm that the proposed controller improves QoS satisfaction in terms of end-to-end response time by nearly 12 times, on average, compared with that of Aerospike's, in high-rate workload. Results also show decreases of the average and standard deviation of latency up to 31% and 50%, respectively, during workload surges (peak load) in high-rate workload.
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