Paper Info
Title
An adaptive framework for utility-based optimization of scientific applications in the cloud
Abstract
Cloud computing plays an increasingly important role in realizing scientific applications by offering virtualized compute and storage infrastructures that can scale on demand. This paper presents a self-configuring adaptive framework optimizing resource utilization for scientific applications on top of Cloud technologies. The proposed approach relies on the concept of utility, i.e., measuring the usefulness, and leverages the well-established principle from autonomic computing, namely the MAPE-K loop, in order to adaptively configure scientific applications. Therein, the process of maximizing the utility of specific configurations takes into account the Cloud stack: the application layer, the execution environment layer, and the resource layer, which is supported by the defined Cloud stack configuration model. The proposed framework self-configures the layers by evaluating monitored resources, analyzing their state, and generating an execution plan on a per job basis. Evaluating configurations is based on historical data and a utility function that ranks them according to the costs incurred. The proposed adaptive framework has been integrated into the Vienna Cloud Environment (VCE) and the evaluation by means of a data-intensive application is presented herein.
Year
DOI
Venue
2014
10.1186/2192-113X-3-4
J. Cloud Computing
Keywords
Field
DocType
Cloud, Cloud stack, Adaptive, Autonomic computing, Utility
Autonomic computing,Application layer,On demand,Abstraction,Computer science,Computer communication networks,Theoretical computer science,Cloud computing,Distributed computing
Journal
Volume
Issue
ISSN
3
1
2192-113X
Citations 
PageRank 
References 
1
0.35
12
Authors
1
Name
Order
Citations
PageRank
Martin Koehler1568.05