Paper Info
Title
Automated Performance Evaluation For Multi-Tier Cloud Service Systems Subject To Mixed Workloads
Abstract
In multi-tier cloud service systems, performance evaluation relies on numerous experiments in order to collect key metrics such as resources usage. The approach may result in highly time-consuming in practice. In this paper, we propose an automated framework for performance tracking, data management and analysis to minimize human intervention in multi tier cloud service systems. The framework support tine-grained analysis of the mixed workloads through the Discrete-time Markov-modulated Poisson process (DMMPP). A general multi tier application is theoretically formulated as a queueing network to evaluate the performance. The effectiveness of the model has been validated through extensive experiments conducted in the RUBiS benchmark system.
Year
DOI
Venue
2017
10.1109/ICDCS.2017.326
2017 IEEE 37TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2017)
Field
DocType
ISSN
Computer science,Server,Computer network,Queueing theory,Throughput,Poisson process,Data management,Distributed computing,Cloud computing
Conference
1063-6927
Citations 
PageRank 
References 
0
0.34
5
Authors
6
Name
Order
Citations
PageRank
Xudong Zhao122.06
Jiwei Huang217725.99
Lei (Chris) Liu396.95
Shijun Liu412033.80
Calton Pu55377877.83
Li-zhen Cui628271.41