A2Cloud: An Analytical Model for Application-to-Cloud Matching to Empower Scientific Computing
Document Type
Conference Presentation
Department
Electrical and Computer Engineering
Conference Title
IEEE International Conference on Cloud Computing (CLOUD)
Date of Presentation
9-7-2018
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.
ISSN
2159-6182
Volume
2018-July
First Page
548
Last Page
555
DOI
10.1109/CLOUD.2018.00076
Recommended Citation
Balos, C.,
De La Vega, D.,
Abuelhaj, Z.,
El Kari, C.,
Mueller, D.,
&
Pallipuram, V. K.
(2018).
A2Cloud: An Analytical Model for Application-to-Cloud Matching to Empower Scientific Computing.
Paper presented at IEEE International Conference on Cloud Computing (CLOUD).
https://scholarlycommons.pacific.edu/soecs-facpres/471
