Run-time prediction of parallel applications on shared environments

Lee, Byoung-Dai; Schopf,

doi:10.1109/clustr.2003.1253355

Cited by 14 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lee et al presented a prediction model that predicts the execution time of the parallel applications by employing regression methods and filtering techniques. In order to predict the execution time, this technique establishes a relationship between the actual running times of the past application run history and the variables which affect the application running times (Lee & Schopf, 2003). Chen et al proposed a technique to predict the CPU and memory requirements to maintain the response time for the web servers (Chen et al, 2007).…”

Section: Related Workmentioning

confidence: 99%

An Analytical Model for Resource Characterization and Parameter Estimation for DAG-Based Jobs for Homogeneous Systems

Sajid

Raza

2015

International Journal of Distributed Systems and Technologies

View full text Add to dashboard Cite

High Performance Computing (HPC) systems demand and consume a significant amount of resources (e.g. server, storage, electrical energy) resulting in high operational costs, reduced reliability, and sometimes leading to waste of scarce natural resources. On one hand, the most important issue for these systems is achieving high performance, while on the other hand, the rapidly increasing resource costs appeal to effectively predict the resource requirements to ensure efficient services in the most optimized manner. The resource requirement prediction for a job thus becomes important for both the service providers as well as the consumers for ensuring resource management and to negotiate Service Level Agreements (SLAs), respectively, in order to help make better job allocation decisions. Moreover, the resource requirement prediction can even lead to improved scheduling performance while reducing the resource waste. This work presents an analytical model estimating the required resources for the modular job execution. The analysis identifies the number of processors required and the maximum and minimum bounds on the turnaround time and energy consumed. Simulation study reveals that the scheduling algorithms integrated with the proposed analytical model helps in improving the average throughput and the average energy consumption of the system. As the work predicts the resource requirements, it can even play an important role in Service-Oriented Architectures (SOA) like Cloud computing or Grid computing.

show abstract

Section: Related Workmentioning

confidence: 99%