QoS Evaluation of End-to-End Services in Virtualized Computing Environments

Author:

Yan Guofeng1,Peng Yuxing2,Chen Shuhong3,You Pengfei2

Affiliation:

1. School of Computer and Communication, Hunan Institute of Engineering, Hunan, China & School of Computer, National University of Defense Science and Technology, Changsha, China

2. School of Computer, National University of Defense Science and Technology, Changsha, China

3. School of Information Science and Engineering, Central South University, Changsha, China

Abstract

Quality of service (QoS) optimization for end-to-end (e2e) services always depends on performance analysis in cloud-based service delivery industry. However, performance analysis of e2e services becomes difficult as the scale and complexity of virtualized computing environments increase. In this paper, the authors present a novel hierarchical stochastic approach to evaluate the QoS of e2e virtualized cloud services using Quasi-Birth Death structures, where jobs arrive according to a stochastic process and request virtual machines (VMs), which are specified in terms of resources, i.e., VM-configuration. To reduce the complexity of performance evaluation, the overall virtualized cloud services are partitioned into three sub-hierarchies. The authors analyze each individual sub-hierarchy using stochastic queueing approach. Thus, the key performance metrics of e2e cloud service QoS, such as acceptance probability and e2e response delay incurred on user requests, are obtained.

Publisher

IGI Global

Subject

Computer Networks and Communications,Information Systems,Software

Cited by 4 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. A Software Engineering Perspective for Development of Enterprise Applications;Advances in Computer and Electrical Engineering;2018

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3. An Intelligent QoS Identification for Untrustworthy Web Services via Two-Phase Neural Networks;2016 IEEE International Conference on Web Services (ICWS);2016-06

4. Cloud computing system risk estimation and service selection approach based on cloud focus theory;Neural Computing and Applications;2016-01-11

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