Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Abstract

The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

Multiobjective Parallel Algorithms for Solving Biobjective Open Shop Scheduling Problem

“…Genetic algorithms (GAs) are a class of generalpurpose search strategies based on natural selection and genetics. GAs have been used effectively to a wide array of optimization challenges [19,25,26]. In contrast to local search algorithms such as SA, which are often focused on manipulating a single viable solution and are extremely quick, GAs retain and modify a population of possible solutions.…”

“…In this paper, we use the roulette wheel selection approach, according to which individuals with higher fitness have a better chance of mating and this is in fact the basis of Darwin's theory. In order to apply the roulette wheel selection, two parameters are required: individual selection probability and individual cumulative probability, which are obtained from equations ( 18) and (19), respectively:…”

“…Task scheduling has been studied in much different research works with an array of different applications from task scheduling in cloud computing [19] to the flow shops in factories [20,21]. Most studies on scheduling problems have been conducted under the basic assumption that machines are available on all planning horizons, while this is not the case in the real world.…”

Hybrid Gravitational Search Algorithm to Solve the Task Scheduling Problem of Two-Machine Flow Shop

“…There is no significant difference 𝑇 𝑖+1 = 0.95 × 𝑇 𝑖 (26) The second method of temperature reduction is a combination of the result in the final solution of a temperature (Rtemp2). In this case, after terminating the number of allowed iterations at one temperature, the temperature of the algorithm decreases according to Equation (27). Using this method of temperature reduction has the feature that if temperature iterations i can be the best answer to improve that temperature 𝑍 𝑖 * < 𝑍 𝐺 * then the temperature decreases with less speed and it is even possible to increase it so that if there is a better answer, there is an opportunity to search further.…”

“…For example, in factories where conveyors are used as transportation equipment, and in assembly lines that assemble the final product, sequential scheduling is used. As we mentioned in our previous work [27], the main goal of task scheduling is to map several tasks to proper processors so that it could optimize one or more objectives at an acceptable time. In this paper, the problem of scheduling works with different delivery dates in a two-machine sequential flow shop environment to minimize the cost of delays and advances is considered.…”