Paper Info
Title
A2Cloud: An Analytical Model for Application-to-Cloud Matching to Empower Scientific Computing
Abstract
We present an analytical model that matches scientific applications to effective Cloud instances for high application performance. The model constructs two vectors namely, the application vector and the Cloud vector. The application vector consists of application performance components such as the number of single-precision (SP) floating-point operations (FLOPs) and double-precision (DP) FLOPs, main memory accesses, and disk accesses. The Cloud vector comprises corresponding Cloud instance performance components such as the benchmarked SP and DP floating-point operations per second (FLOPS), memory bandwidth, and disk bandwidth. The model performs an inner product of the two vectors to produce an Application-to-Cloud (A2Cloud) score, which quantifies the application-to-Cloud match. We encapsulate the A2Cloud model in a user-friendly A2Cloud framework that inputs a test application and a target Cloud instance, profiles them, and executes the A2Cloud model to generate the A2Cloud score. We demonstrate the model by conducting 162 application executions across nine Cloud instances. Our tests yield an average A2Cloud matching rate of 6 for every 9 application-instance pairs with a mean absolute difference of ±1.08 ranks.
Year
DOI
Venue
2018
10.1109/CLOUD.2018.00076
2018 IEEE 11th International Conference on Cloud Computing (CLOUD)
Keywords
Field
DocType
Cloud Computing,Scientific Computing,Cloud for Science and Engineering,High-performance computing,Cloud performance,EC2,Google Cloud,Azure
Mean difference,Memory bandwidth,FLOPS,Computer science,Computational science,Bandwidth (signal processing),Cloud computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-7236-5
0
0.34
References 
Authors
6
6
Name
Order
Citations
PageRank
Cody Balos100.34
David de la Vega2187.89
Zachariah Abuelhaj300.34
Chadi Kari411.79
David Mueller500.34
vivek k pallipuram6283.77