Predicting Efficiency in master-slave grid computing systems

TL;DR

This paper analyzes how network topology influences the efficiency of master-slave distributed computing systems across different complex network models, identifying key topological predictors of performance.

Contribution

It introduces a quantitative method to predict distributed computing efficiency using topological measurements, highlighting closeness centrality as the most effective predictor.

Findings

Closeness centrality best predicts execution time.

Network size impacts efficiency predictions.

Different network models exhibit varying efficiency characteristics.

Abstract

This work reports a quantitative analysis to predicting the efficiency of distributed computing running in three models of complex networks: Barab\'asi-Albert, Erd\H{o}s-R\'enyi and Watts-Strogatz. A master/slave computing model is simulated. A node is selected as master and distributes tasks among the other nodes (the clients). Topological measurements associated with the master node (e.g. its degree or betwenness centrality) are extracted and considered as predictors of the total execution time. It is found that the closeness centrality provides the best alternative. The effect of network size was also investigated.