Modeling and Analysis of Performance under Interference in the Cloud

被引：6

作者：

Votke, Scott ^{[1
]}

Javadi, Seyyed Ahmad ^{[1
]}

Gandhi, Anshul ^{[1
]}

机构：

[1] SUNY Stony Brook, Stony Brook, NY 11794 USA

来源：

2017 IEEE 25TH INTERNATIONAL SYMPOSIUM ON MODELING, ANALYSIS, AND SIMULATION OF COMPUTER AND TELECOMMUNICATION SYSTEMS (MASCOTS) | 2017年

关键词：

D O I：

10.1109/MASCOTS.2017.11

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

One of the key performance challenges in cloud computing is the problem of interference, or resource contention, among colocated VMs. While prior work has empirically analyzed interference for specific workloads under specific settings, there is a need for a generic approach to estimate application performance under any interference condition. In this paper, we present an analytical model to estimate performance as a function of various workload, system, and interference conditions, including the intensity and length of interference, for single-and multi-VM systems. Comparisons with empirical results under various scenarios show that our model can provide accurate latency estimations (less than 5% error). We employ our model to analyze systems under interference, and derive useful results to aid practitioners.

引用

页码：232 / 243

页数：12

共 37 条

[11] A Feasibility Study of Host-Level Contention Detection by Guest Virtual Machines [J].