Paper Info
Title
Selecting the best VM across multiple public clouds: a data-driven performance modeling approach.
Abstract
Users of cloud services are presented with a bewildering choice of VM types and the choice of VM can have significant implications on performance and cost. In this paper we address the fundamental problem of accurately and economically choosing the best VM for a given workload and user goals. To address the problem of optimal VM selection, we present PARIS, a data-driven system that uses a novel hybrid offline and online data collection and modeling framework to provide accurate performance estimates with minimal data collection. PARIS is able to predict workload performance for different user-specified metrics, and resulting costs for a wide range of VM types and workloads across multiple cloud providers. When compared to sophisticated baselines, including collaborative filtering and a linear interpolation model using measured workload performance on two VM types, PARIS produces significantly better estimates of performance. For instance, it reduces runtime prediction error by a factor of 4 for some workloads on both AWS and Azure. The increased accuracy translates into a 45% reduction in user cost while maintaining performance.
Year
DOI
Venue
2017
10.1145/3127479.3131614
SoCC '17: ACM Symposium on Cloud Computing Santa Clara California September, 2017
Keywords
Field
DocType
Cloud Computing, Resource Allocation, Performance Prediction, Data-Driven Modeling
Data collection,Data mining,Collaborative filtering,Data-driven,Computer science,Workload,Real-time computing,Resource allocation,Linear interpolation,Performance prediction,Cloud computing
Conference
ISBN
Citations 
PageRank 
978-1-4503-5028-0
35
1.33
References 
Authors
35
5
Name
Order
Citations
PageRank
Neeraja J. Yadwadkar1704.43
Bharath Hariharan2105265.90
Joseph E. Gonzalez32219102.68
Burton Smith4758114.99
Randy H. Katz5168193018.89