Autonomic Provisioning and Application Mapping on Spot Cloud Resources

被引：10

作者：

Dubois, Daniel J. ^{[1
]}

Casale, Giuliano ^{[1
]}

机构：

[1] Imperial Coll London, Dept Comp, London, England

来源：

2015 INTERNATIONAL CONFERENCE ON CLOUD AND AUTONOMIC COMPUTING (ICCAC) | 2015年

基金：

英国工程与自然科学研究理事会;

关键词：

cloud provisioning; fluid-approximated queueing networks; spot cloud; resource allocation; random environment; application mapping; bidding strategy;

D O I：

10.1109/ICCAC.2015.21

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The spot instance model is a virtual machine pricing scheme in which unused resources of cloud providers are offered to the highest bidder. This leads to the formation of a spot price, whose fluctuations can determine customers to be overbid by other users and lose the virtual machine they rented. In this paper we propose a heuristic to automate the decision on: (i) which and how many resources to rent in order to run a cloud application, (ii) how to map the application components to the rented resources, and (iii) what spot price bids to use in order to minimize the total bid price while maintaining an acceptable level of performance. To drive the decision making, our algorithm combines a multi-class queueing network model of the application with a Markov model that describes the stochastic evolution of the spot price and its influence on virtual machine reliability. We show, using a model developed for a real enterprise application and historical traces of the Amazon EC2 spot instance prices, that our heuristic finds low cost solutions that indeed guarantee the required levels of performance. The performance of our heuristic method is compared to that of nonlinear programming and shown to markedly accelerate the finding of low-cost optimal solutions.

引用

页码：57 / 68

页数：12

共 30 条

[1]

Andrzejak Artur, 2010, Proceedings 18th IEEE/ACM International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS 2010), P257, DOI 10.1109/MASCOTS.2010.34

[2] The Palladio component model for model-driven performance prediction [J].