Paper Info
Title
Statistics-driven workload modeling for the Cloud
Abstract
A recent trend for data-intensive computations is to use pay-as-you-go execution environments that scale transparently to the user. However, providers of such environments must tackle the challenge of configuring their system to provide maximal performance while minimizing the cost of resources used. In this paper, we use statistical models to predict resource requirements for Cloud computing applications. Such a prediction framework can guide system design and deployment decisions such as scale, scheduling, and capacity. In addition, we present initial design of a workload generator that can be used to evaluate alternative configurations without the overhead of reproducing a real workload. This paper focuses on statistical modeling and its application to data-intensive workloads.
Year
DOI
Venue
2010
10.1109/ICDEW.2010.5452742
Data Engineering Workshops
Keywords
Field
DocType
Internet,data analysis,statistical analysis,cloud computing applications,data-intensive computations,statistical modeling,statistics-driven workload modeling,workload generator
Data mining,Software deployment,Workload,Scheduling (computing),Computer science,Systems design,Statistical model,Database,Cloud computing,The Internet,Computation
Conference
ISSN
ISBN
Citations 
1943-2895
978-1-4244-6521-7
53
PageRank 
References 
Authors
3.04
3
5
Name
Order
Citations
PageRank
Archana Ganapathi186054.96
Yanpei Chen291741.46
Armando Fox36238524.64
Randy H. Katz4168193018.89
David A. Patterson5110931925.05