Solving Task Scheduling Problem in Cloud Computing Environment Using Orthogonal Taguchi-Cat Algorithm

In cloud computing datacenter, task execution delay is no longer accidental. In recent times, a number of artificial intelligence scheduling techniques are proposed and applied to reduce task execution delay. In this study, we proposed an algorithm called Orthogonal Taguchi Based-Cat Swarm Optimization (OTB-CSO) to minimize total task execution time. In our proposed algorithm Taguchi Orthogonal approach was incorporated at CSO tracing mode for best task mapping on VMs with minimum execution time. The proposed algorithm was implemented on CloudSim tool and evaluated based on makespan metric. Experimental results showed for 20VMs used, proposed OTB-CSO was able to minimize makespan of total tasks scheduled across VMs with 42.86%, 34.57% and 2.58% improvement over Minimum and Maximum Job First (Min-Max), Particle Swarm Optimization with Linear Descending Inertia Weight (PSO-LDIW) and Hybrid Particle Swarm Optimization with Simulated Annealing (HPSO-SA) algorithms. Results obtained showed OTB-CSO is effective to optimize task scheduling and improve overall cloud computing performance with better system utilization

Application of a Modified ACO Algorithm for Optimizing Routes and Externality Effect of Solid Waste Management

To improve solid waste management and maintain its sustainability, it is important to reduce both the solid waste operational cost which includes the monetary value of distances covered and the externality effects of solid waste management. Therefore, this paper presents an application of a modified Ant Colony System algorithm to a bi-objective model for solid waste management in the Shama District in the Western Region of Ghana. The objective is to optimize route lengths and externality effects of solid waste management. Data on route lengths and population of communities along the routes were collected from 20 communities in the Shama Distric. Externality effect was measured by considering the population of the communities along the routes, the cost of treating a common cold subject to the assumption of two percent of the population being affected by the externality effect. The implemented algorithm has demonstrated the bi-objective optimal solution of route length (km) and externality effect (GHS) of (11, 2100) achievable on the path , which respectively represents a path linking the following communities: Aboadze, Abuesi Assorko Essaman, Beposo, Bosomdo and Fawomanye. There is therefore the need to ensure that the communities involved are linked with good roads

Scheduling Batch Processing Machine Using Max–Min Ant System Algorithm Improved by a Local Search Method

The problem of minimizing the makespan on single batch processing machine is studied in this paper. Both job sizes and processing time are nonidentical and the processing time of each batch is determined by the job with the longest processing time in the batch. Max–Min Ant System (MMAS) algorithm is developed to solve the problem. A local search method MJE (Multiple Jobs Exchange) is proposed to improve the performance of the algorithm by adjusting jobs between batches. Preliminary experiment is conducted to determine the parameters of MMAS. The performance of the proposed MMAS algorithm is compared with CPLEX as well as several other algorithms including ant cycle (AC) algorithm, genetic algorithm (GA), and two heuristics, First Fit Longest Processing Time (FFLPT) and Best Fit Longest Processing Time (BFLPT), through numerical experiment. The experiment results show that MMAS outperformed others especially for large population size

Hybrid Ant Colony Optimization For Fuzzy Unrelated Parallel Machine Scheduling Problems

This study extends the best hybrid ant colony optimization variant developed by Liao et al. (2014) for crisp unrelated parallel machine scheduling problems to solve fuzzy unrelated parallel machine scheduling problems in consideration of trapezoidal fuzzy processing times, trapezoidal fuzzy sequencing dependent setup times and trapezoidal fuzzy release times. The objective is to find the best schedule taking minimum fuzzy makespan in completing all jobs. In this study, fuzzy arithmetic is used to determine fuzzy completion times of jobs and defuzzification function is used to convert fuzzy numbers back to crisp numbers for ranking. Eight fuzzy ranking methods are tested to find the most feasible one to be employed in this study. The fuzzy arithmetic testing includes four different cases and each case with the following operations separately, i.e., addition, subtraction, multiplication and division, to investigate the spread of fuzziness as fuzzy numbers are subject to more and more number of operations. The effect of fuzzy ranking methods on hybrid ant colony optimization (hACO) is investigated. To prove the correctness of our methodology and coding, unrelated parallel machine scheduling with fuzzy numbers and crisp numbers are compared based on scheduling problems up to 15 machines and 200 jobs. Relative percentage deviation (RPD) is used to evaluate the performance of hACO in solving fuzzy unrelated parallel machine scheduling problems. A numerical study on large-scale scheduling problems up to 20 machines and 200 jobs is conducted to assess the performance of the hACO algorithm. For comparison, a discrete particle swarm optimization (dPSO) algorithm is implemented for fuzzy unrelated parallel machine scheduling problem as well. The results show that the hACO has better performance than dPSO not only in solution quality in terms of RPD value, but also in computational time