Estimating Multiclass Service Demand Distributions Using Markovian Arrival Processes

Author:

Wang Runan1ORCID,Casale Giuliano1ORCID,Filieri Antonio1ORCID

Affiliation:

1. Department of Computing, Imperial College London, London, UK

Abstract

Building performance models for software services in DevOps is costly and error-prone. Accurate service demand distribution estimation is critical to precisely modeling queueing behaviors and performance prediction. However, current estimation methods focus on capturing the mean service demand, disregarding higher-order moments of the distribution that still can largely affect prediction accuracy. To address this limitation, we propose to estimate higher moments of the service demand distribution for a microservice from monitoring traces. We first generate a closed queueing model to abstract software performance and use it to model the departure process of requests completed by the software service as a Markovian arrival process (MAP). This allows formulating the estimation of service demand into an optimization problem, which aims to find the first multiple moments of the service demand distribution that maximize the likelihood of the MAP using generated the measured inter-departure times. We then estimate the service demand distribution for different classes of service with a maximum likelihood algorithm and novel heuristics to mitigate the computational cost of the optimization process for scalability. We apply our method to real traces from a microservice-based application and demonstrate that its estimations lead to greater prediction accuracy than exponential distributions assumed in traditional service demand estimation approaches for software services.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Science Applications,Modeling and Simulation

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Efficiency Analysis of Microservices Based on Queueing Models;2023 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT);2023-12-14

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