DocumentCode
720559
Title
Predicting the Performance of Parallel Computing Models Using Queuing System
Author
Shen Chao ; Tong Weiqin ; Kausar, Samina
Author_Institution
Sch. of Comput. Eng. & Sci., Shanghai Univ., Shanghai, China
fYear
2015
fDate
4-7 May 2015
Firstpage
757
Lastpage
760
Abstract
Computing models provide the parallel and distributed algorithms for cloud. The ability to estimate the performance of parallel computing models for efficient resource scheduling is critical. Current techniques for predicting the performance are mostly based on analyzing and simulating. The behavior of parallel computing model directly leads to the diversity of mathematical model. Without a general prediction model, it is very hard to compare fairly different parallel computing models in several critical aspects, including computing capacity, resource configuration, scalability, fault tolerance and so on. In this paper, we design a mathematical model for predicting the performance by using queuing system. We make various computing models as a service system for shielding the diversity. The performance can be accurately estimated with the job waiting time and the job performing time. The heterogeneity of computing nodes may also be considered.
Keywords
cloud computing; parallel processing; queueing theory; resource allocation; software fault tolerance; software performance evaluation; cloud; distributed algorithms; fault tolerance; job performing time; job waiting time; parallel algorithms; parallel computing models; queuing system; resource configuration; resource scheduling; scalability; service system; Analytical models; Computational modeling; Computers; Data models; Mathematical model; Parallel processing; Predictive models; parallel computing model; performance prediction; queuing system; service time;
fLanguage
English
Publisher
ieee
Conference_Titel
Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on
Conference_Location
Shenzhen
Type
conf
DOI
10.1109/CCGrid.2015.92
Filename
7152550
Link To Document