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2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (2018)
Washington, DC, USA
May 1, 2018 to May 4, 2018
ISBN: 978-1-5386-5815-4
pp: 503-512
ABSTRACT
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.
INDEX TERMS
client-server systems, decentralised control, quality of service, queueing theory, telecommunication congestion control, telecommunication scheduling, telecommunication traffic
CITATION

Y. K. Kim, M. R. HoseinyFarahabady, Y. C. Lee and A. Y. Zomaya, "Decentralized Admission Control for High-Throughput Key-Value Data Stores," 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Washington, DC, USA, 2018, pp. 503-512.
doi:10.1109/CCGRID.2018.00077
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