FGPGA: An Efficient Genetic Approach for Producing Feasible Graph Partitions

TL;DR

This paper presents FGPGA, a lightweight genetic algorithm that efficiently produces balanced and feasible graph partitions, outperforming existing methods, with applications in distributed computing and mobile cloud deployment.

Contribution

Introduction of FGPGA, a novel genetic algorithm that ensures feasible, balanced graph partitions considering heterogeneity and capacity constraints, suitable for cloud and mobile applications.

Findings

FGPGA significantly outperforms state-of-the-art methods in partition quality.

The approach guarantees feasibility by avoiding oversized partitions during initialization and search.

Demonstrated effectiveness on standard benchmark datasets.

Abstract

Graph partitioning, a well studied problem of parallel computing has many applications in diversified fields such as distributed computing, social network analysis, data mining and many other domains. In this paper, we introduce FGPGA, an efficient genetic approach for producing feasible graph partitions. Our method takes into account the heterogeneity and capacity constraints of the partitions to ensure balanced partitioning. Such approach has various applications in mobile cloud computing that include feasible deployment of software applications on the more resourceful infrastructure in the cloud instead of mobile hand set. Our proposed approach is light weight and hence suitable for use in cloud architecture. We ensure feasibility of the partitions generated by not allowing over-sized partitions to be generated during the initialization and search. Our proposed method tested on standard benchmark datasets significantly outperforms the state-of-the-art methods in terms of quality of partitions and feasibility of the solutions.