ForkMV: Mean-and-Variance Estimation of Fork-Join Queuing Networks for Datacenter Applications*

2022 IEEE International Conference on Networking, Architecture and Storage (NAS)(2022)

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摘要
The Fork-Join structure underlays many distributed computing applications in data centers. In this paper, we develop a technique, called ForkMV, to estimate the mean and variance of request response time for Fork-Join queuing networks (FJQNs) with both short-tailed and long-tailed service time distributions and arbitrary request fanout degrees (i.e., the number of Fork nodes). Specifically, for an FJQN with any given service time distribution of practical interests, ForkMV is able to estimate the mean and variance of request response time, accurate enough to facilitate effective resource allocation for data center applications. The test results indicate that in the entire range of the fanout degrees being tested (i.e., [1], [4000]), ForkMV is able to estimate the mean response time within 5 % and 15 % and variance response time within 15% and 10% of the simulation results for short-tailed exponential service distribution and long-tailed truncated Pareto distribution, respectively, at the 90 % load or higher.
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关键词
Mean-latency,Mean-latency with bounded vari-ance,Tail latency,Fork-Join queuing networks
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