Paper Info
Title
Modeling the Slowdown of Data-Parallel Applications in Homogeneous and Heterogeneous Clusters of Workstations
Abstract
Data-parallel applications executing in multi-user clustered environments share resources with other applications. Since this sharing of resources dramatically affects the performance of individual applications, it is critical to estimate its effect, i.e., the application slowdown, in order to predict application behavior. In this paper, we develop a new approach for predicting the slowdown imposed on data-parallel applications executing on homogeneous and heterogeneous clusters of workstations. Our model synthesizes the slowdown on each machine used by an application into a contention measure - the aggregate slowdown factor - used to adjust the execution time of the application to account for the aggregate load.The model is parameterized by the work (or data) partitioning policy employed by the targeted application, the local slowdown (due to contention from other users) present in each node of the cluster, and the relative weight (capacity) associated with each node in the cluster. This model provides a basis for predicting realistic execution times for distributed data-parallel applications in production clustered environments.
Year
DOI
Venue
1998
10.1109/HCW.1998.666548
Heterogeneous Computing Workshop
Keywords
DocType
ISBN
aggregate load,heterogeneous clusters,application behavior,environments share resource,individual application,application slowdown,data-parallel applications,contention measure,data-parallel application,local slowdown,targeted application,aggregate slowdown factor,parallel processing,local area networks,time measurement,workstations,production,interference,resource sharing,predictive models
Conference
0-8186-8365-1
Citations 
PageRank 
References 
1
0.38
12
Authors
2
Name
Order
Citations
PageRank
Silvia M. Figueira132075.28
Francine Berman22251220.90