Hybrid Nearest-Neighbor Ant Colony Optimization Algorithm for Enhancing Load Balancing Task Management

Fatma Mbarek,Volodymyr Mosorov

doi:10.3390/app112210807

Abstract

Many computer problems that arise from real-world circumstances are NP-hard, while, in the worst case, these problems are generally assumed to be intractable. Existing distributed computing systems are commonly used for a range of large-scale complex problems, adding advantages to many areas of research. Dynamic load balancing is feasible in distributed computing systems since it is a significant key to maintaining stability of heterogeneous distributed computing systems (HDCS). The challenge of load balancing is an objective function of optimization with exponential complexity of solutions. The problem of dynamic load balancing raises with the scale of the HDCS and it is hard to tackle effectively. The solution to this unsolvable issue is being explored under a particular algorithm paradigm. A new codification strategy, namely hybrid nearest-neighbor ant colony optimization (ACO-NN), which, based on the metaheuristic ant colony optimization (ACO) and an approximate nearest-neighbor (NN) approaches, has been developed to establish a dynamic load balancing algorithm for distributed systems. Several experiments have been conducted to explore the efficiency of this stochastic iterative load balancing algorithm; it is tested with task and nodes accessibility and proved to be effective with diverse performance metrics.

Highlights

Distributed computing platforms are becoming increasingly important as cost-effective options to conventional high-performance computing platforms
The results obtained by the nearest-neighbor ant colony optimization (ACO-NN) algorithm are compared to renowned metaheuristic algorithms such as artificial bee colony (ABC), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), camel herd algorithm (CHA), black hole (BH), greedy randomized adaptive search procedure (GRASP), particle swarm optimization (PSO), traveling salesman problem based on simulated annealing and gene expression programming (TSP-SAGEP), simulated-annealingbased symbiotic organisms search (SOS-SA), multioffspring genetic algorithm (MO-GA), and discrete tree-seed algorithm (DTSA) with their variants (DTSA0, DTSAI, DTSAII)
The research proposed a ACO-NN approach for load balancing in distributed computing systems to enhance load scheduling mechanism

Summary

Introduction

Distributed computing platforms are becoming increasingly important as cost-effective options to conventional high-performance computing platforms. The distributed systems keep going to expand in size, heterogeneity, and diversity of network resources [1,2]. In such complicated platforms, workload management and load balancing become critical factors in keeping business activities afloat. One of the most complex concerns in real-time optimization problems is the robust management of a wide range of workload patterns. Load balancing is an important element in distributed and parallel environments [6,7], as it is used to achieve maximum use of resources, to avoid node overload, to reduce response time, to avoid network bottlenecks, and to ensure system scalability. The challenge of dynamic load balancing persists as a difficult issue of global optimization because of system structure heterogeneity, requisitions of Quality of Service (QoS) per application and administration of computational resources [8,9]

Methods

Results

Discussion

Conclusion