A Comparison between Task Distribution Strategies for Load Balancing Using a Multiagent System

Abstract

This work presents a comparison between several task distribution methods for load balancing with the help of an original implementation of a solution based on a multi-agent system. Among the original contributions, one can mention the design and implementation of the agent-based solution and the proposal of various scenarios, strategies and metrics that are further analyzed in the experimental case studies. The best strategy depends on the context. When the objective is to use the processors at their highest processing potential, the agents preferences strategy produces the best usage of the processing resources with an aggregated load per turn for all PAs up to four times higher than the rest of the strategies. When one needs to have a balance between the loads of the processing elements, the maximum availability strategy is better than the rest of the examined strategies, producing the lowest imbalance rate between PAs out of all the strategies in most scenarios. The random distribution strategy produces the lowest average load especially for tasks with higher required processing time, and thus, it should generally be avoided.

Optimizing Task Execution: The Impact of Dynamic Time Quantum and Priorities on Round Robin Scheduling

“…The performance of the traditional priority scheduling algorithm was enhanced by the proposed algorithm, which introduced an aging mechanism to prevent low-priority processes from being indefinitely postponed. Several variations of RR scheduling algorithms, such as shortest job first, preemptive, non-preemptive, and First Come, First Served algorithms, are analyzed in [8][9][10][11] to improve performance, CPU utilization, throughput, and context switching. The Dynamic Time Slice (DTS) algorithm, designed by the authors in [12], aims to minimize time costs by prioritizing short processes and minimizing scheduling overhead.…”