Resource Allocation in Public Cluster with Extended Optimization Algorithm

TL;DR

This paper presents an extended genetic algorithm for optimizing resource allocation in public clusters, aiming to improve efficiency and enable automatic decision-making.

Contribution

It introduces a modified genetic algorithm tailored for public cluster resource management, with detailed analysis and comparison to exact calculations.

Findings

The extended algorithm outperforms traditional methods in resource allocation efficiency.

The approach enables automatic decision-making in public clusters.

Results demonstrate improved utilization and management of cluster resources.

Abstract

We introduce an optimization algorithm for resource allocation in the LIPI Public Cluster to optimize its usage according to incoming requests from users. The tool is an extended and modified genetic algorithm developed to match specific natures of public cluster. We present a detail analysis of optimization, and compare the results with the exact calculation. We show that it would be very useful and could realize an automatic decision making system for public clusters.