A Taxonomy of Performance Prediction Systems in the Parallel and Distributed Computing Grids

TL;DR

This paper presents a comprehensive taxonomy for classifying performance prediction systems in grid computing, aiding understanding and highlighting research gaps in resource management.

Contribution

It introduces a taxonomy framework for PPS architecture in grid computing and categorizes existing approaches to identify unexplored research areas.

Findings

Most PPS approaches focus on resource management optimization

Existing PPS implementations vary widely in architecture and methodology

Research gaps identified in handling heterogeneity and scalability

Abstract

As Grids are loosely-coupled congregations of geographically distributed heterogeneous resources, the efficient utilization of the resources requires the support of a sound Performance Prediction System (PPS). The performance prediction of grid resources is helpful for both Resource Management Systems and grid users to make optimized resource usage decisions. There have been many PPS projects that span over several grid resources in several dimensions. In this paper the taxonomy for describing the PPS architecture is discussed. The taxonomy is used to categorize and identify approaches which are followed in the implementation of the existing PPSs for Grids. The taxonomy and the survey results are used to identify approaches and issues that have not been fully explored in research.