Abstract:Cloud computing is the development of distributed computing, parallel computing, and grid computing, or defined as a commercial implementation of such computer science concepts. One of the main issues in a cloud computing environment is Task scheduling (TS). In Cloud task scheduling, many Non deterministic Polynomial time-hard optimization problem, and many meta-heuristic (MH) algorithms have been proposed to solve it. A task scheduler should adapt its scheduling strategy to changing environment and variable tasks. This paper amends a cloud task scheduling policy based on Modified Ant Colony Optimization (MACO) algorithm. The main contribution of recommended method is to minimize makespan and to perform Multi Objective Task Scheduling (MOTS) process by assigning pheromone amount relative to corresponding virtual machine efficiency. MACO algorithm improves the performance of task scheduling by reducing makespan and degree of imbalance comparatively lower than a basic ACO algorithm by its multi-objective and deliberate nature. Experimental outcomes have shown that proposed MACO to have makespan 350 milliseconds and average utilization of 0.51 for a set of 100 tasks.

Workflow scheduling is the recent researching area in the cloud environment, in which user satisfaction based on the cost and bandwidth is the most challenging task. Several research methods are devised to minimize the execution time and cost, which compromises the attributes. Hence, this research introduces an effective task scheduling mechanism in a cloud environment utilizing the Regressive Whale Water Optimization (RWWO) algorithm, which is derived by the integration of Regressive Whale Optimization (RWO) and Water Cycle Algorithm (WCA). The fitness parameters utilized are Quality of Service (QoS), resource utilization, and predicted energy. However, predicted energy is determined using Deep Maxout Network. Moreover, the proposed RWWO + Deep Maxout Network achieved a minimum task scheduling time of 0.0208, minimum task scheduling cost of 0.0017, minimum predicted energy of 0.1971, and maximum resource utilization of 0.9999.

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Cloud computing is the advancing technology that aims at providing services to the customers with the available resources in the cloud environment. When the multiple users request service from the cloud server, there is a need of the proper scheduling of the resources to attain good customer satisfaction. Therefore, this paper proposes the Regressive Whale Optimization (RWO) algorithm for workflow scheduling in the cloud computing environment. RWO is the meta-heuristic algorithm, which schedules the task depending on a fitness function. Here, the fitness function is defined based on three major constraints, such as resource utilization, energy, and the Quality of Service (QoS). Therefore, the proposed task scheduling requires minimum time and cost for executing the task in the virtual machines. The performance of the proposed method is analyzed using the four experimental setups, and the results of the analysis prove that the proposed multi-objective task scheduling method performs well than the existing methods. The evaluation metrics considered for analyzing the performance of the proposed workflow scheduling method are resource utilization, energy, cost, and time. Resource utilization is the process of making the most of the resources available for performing tasks. Energy is the quantitative property of the resource to perform tasks. The proposed method attains the maximum resource utilization at a rate of 0.0334, minimal rate of energy, scheduling cost, and time as 0.2291, 0.0181, and 0.0007, respectively.

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