Minimization of Memory Usage in Multicore System Using Ant Colony Optimization (ACO)

C.G Igiri; M. S. Udoh

doi:10.56201/ijcsmt.vol.12.no4.2026.pg272.286

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Minimization of Memory Usage in Multicore System Using Ant Colony Optimization (ACO)

C.G Igiri & M. S. Udoh

Abstract

The accelerated advancement of multicore processor architectures has significantly improved computational performance through parallel task execution. However, increased core integration has amplified challenges related to memory usage and cache contention limitations. Inefficient task-to-core mapping often results in excessive memory consumption, imbalance across cores, and degraded system performance. This paper addresses the problem of memory usage minimization in multicore systems by proposing a memory-aware task scheduling model based on Ant Colony Optimization . The study formulates the memory minimization problem as a combinatorial optimization model with the objective of reducing total memory usage while maintaining balanced memory distribution across processor cores and satisfying memory capacity constraints. A mathematical framework was developed incorporating decision variables, objective functions, and constraints that capture memory allocation behavior in multicore environments. Within the ACO framework, artificial ants iteratively construct feasible task–core assignments guided by pheromone trails and heuristic information. A memory-aware fitness function integrating total memory usage and memory imbalance is designed to evaluate solution quality. The proposed model was implemented in Python and evaluated using a worked numerical example and simulation experiments. Simulation result shows that fitness improve from 0.001370 to 0.00139 by iteration 3 and stabilizes thereafter indication convergence. The findings confirm that the ACO-based approach effectively minimizes memory usage, reduces imbalance, and satisfies architectural constraints in multicore systems. The study contributes a structured and computationally efficient framework for memory-aware scheduling and provides a foundation for further research in resource optimization for modern multicore architectures.

Keywords

Multicore SystemsMemory Usage MinimizationAnt Colony OptimizationTask SchedulingMemory ImbalanceResource Optimization.

References

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Minimization of Memory Usage in Multicore System Using Ant Colony Optimization (ACO)

Abstract

Keywords

References

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