Elsevier

Computer Communications

Volume 187, 1 April 2022, Pages 35-44
Computer Communications

Bee optimization based random double adaptive whale optimization model for task scheduling in cloud computing environment

https://doi.org/10.1016/j.comcom.2022.01.016Get rights and content

Highlights

  • To solve the multi-objective task scheduling problems in cloud computing environments.

  • Minimizing the value of the objective function such as execution time and the execution cost.

  • Converges faster than any other approaches for large search spaces for the large scheduling problems.

  • HWOA based MBA algorithm effectively minimizes the task completion time and also execution time.

Abstract

Cloud computing is the computing technology that offers dynamically scalable and flexible computing resources. Task scheduling in the cloud system is the major problem that needs to be tackled for enhancing the system performance and cloud customer satisfaction level. The task scheduling scheme directly affects the execution time as well as the execution cost of the system. To overcome the above-stated issue, the novel hybrid Whale optimization algorithm-based MBA algorithm is proposed for solving the multi-objective task scheduling problems in cloud computing environments. In the hybrid WOA based MBA algorithm, the multi-objective behavior decreases the makespan by maximizing the resource utilization. The output of the Random double adaptive whale optimization algorithm (RDWOA) is enhanced by utilizing the mutation operator of the Bees algorithm. The performance evaluation is conducted and compared with other algorithms using the platform of Cloudsim tool kit for various measures such as completion, time, and computational cost. The results are analyzed for the performance measures such as makespan, execution time, resource utilization and computational cost and the analysis proves that the proposed algorithm performs better than other algorithms such as IWC, MALO, BA-ABC and MGGS. The proposed HWOA based MBA algorithm converged faster than any other approach for large search spaces and makes it appropriate for large scheduling problems. The experimental results reveal that the HWOA based MBA algorithm effectively minimizes the task completion time and also execution time.

Introduction

Cloud computing is the growing computational technique that depends upon the virtualization equipment in response to the user’s request via the internet and dynamic distribution of the resources. This cloud computing virtualization decreases the maintenance cost and enhances the ease of access. In cloud computing, when the user is requesting the service, task scheduling becomes a significant issue in the effective resource assortment [1]. The purpose of the scheduling is to find the optimal mapping for the tasks set to the resources set. If there are only a small number of tasks and resources, then the scheduling is easy. If users transmit various demands for obtaining the appropriate service quality, then the scheduling is complicated [2].

With the growth and applications of the cloud computing environments, the scheduling problems have achieved gradually more research focuses in recent years. The jobs suggested by the users are allocated to the devices and every job consists of various consecutive tasks which are processed in a particular sequence on various or identical devices [3]. Task scheduling is the main issue in the cloud computing region by considering several features such as total execution cost, fault tolerance, resource utilization, execution time and energy consumption. The significant measure in task scheduling is load balancing [4]. Load balancing is an important method to enhance the scalability and flexibility of the data centers in cloud computing environments. The load balancing improves the performance and utilizes the entire resources more effectively [5].

Task scheduling is considered as the process of scheduling the task computation as well as the resource allocation under particular restrictions. This procedure is regarded as the mapping among the task computation as well as the cloud computing resources depending on specific optimization goals. The physical characteristics of the resources and the task execution attributes are combined to complete the task scheduling on time and at a logical cost [6]. Hence, the proper task scheduling algorithm is selected to enhance the cloud computing utilization rate by maintaining the QoS requirements. The virtual machines are the main processing components and are utilized for processing the tasks. Every virtual machine has its independent attributes, whether it is homogeneous or heterogeneous in nature. Then the scheduling algorithm wants to choose the proper virtual machine for the task. In the case of selecting the scheduling algorithm, the researchers need to concentrate on decreasing the task execution time and makespan value [7].

The virtual machine plan utilization requests the physical machine (PM) for a certain data point. When the cloud environment suffers from alterations in the entire workload, the static load balancing algorithm is utilized. Because of the alterations in the workload over time in the cloud computing environment, the static algorithms cannot able to operate; thereby it requires dynamic schemes for balancing the workload among the virtual machines [8]. Due to the dynamic assets of the cloud computing environment and heterogeneity, task scheduling is considered as the NP-hard problem. When the dynamic scheduling schemes are utilized, it enhances the efficiency because of the increased communication overheads and their difficulties, causing troubles for the service providers [9].

The optimization problem is solved by using metaheuristic algorithms which have gained rising concentration in recent days. The reason for this is that they are exposed to be very efficient and discover the optimal solutions in polynomial time other than the exponential time when compared with traditional schemes [10], [11]. Several metaheuristics and their alterations are utilized to solve issues in various fields that comprise cloud computing [12]. The most famous metaheuristic algorithms utilized for the task scheduling problems are genetic algorithm (GA) [13], ant colony optimization (ACO) [14], particle swarm optimization (PSO) [15], BAT [16] algorithm, cuckoo search algorithm (CSO) [17], tabu search [18] and symbiotic organism search (SOS) [19]. Even though few of these algorithms depicted the enhancements in computing the global best solution for the task scheduling problem, but it is suffered from the premature convergence so trouble to conquer the local minima when faced with the large solution space. These drawbacks lead to suboptimal solutions that affect the performance of the system and also abuse the quality-of-service guarantees. This represents that there is a requirement for the new flexible as well as the effective algorithms for computing the global best solution for the task scheduling algorithm in the cloud computing environment [20], [21], [22], [23], [24], [25].

The whale optimization algorithm is the metaheuristic algorithm inspired by the humpback whales. Due to this exclusive optimization scheme, WOA offers the excellent search ability that makes it popular in several issues. So WOA is utilized for the multi-objective task scheduling optimization issue in the cloud computing environment. Here, the optimization algorithm is mainly focussed on minimizing the execution time and operational cost of the cloud computing system for the specified tasks. At first, the Random double adaptive whale optimization algorithm (RDWOA) is used for solving the multi-objective task scheduling problems. The HWOA-mBA algorithm is formed by the RDWOA algorithm and mutation operators of the Bees algorithm to obtain the optimal solution for the multi-objective task scheduling problem. The contribution of this paper is as follows.

  • To enhance the efficiency of the task execution in the cloud computing environment, multi-objective optimization is applied for the process of the task scheduling process.

  • Obtaining the optimal solution for the multi-objective task scheduling problems by employing the mBA mutation strategy in the RDWOA algorithm.

  • Minimizing the value of the objective function such as execution time and the execution cost by using the proposed hybrid WOA-mBA algorithm.

The rest of the paper is arranged as follows. Section 2 describes the summary of existing works regarding the task scheduling problems. The system model is provided in Section 3. The problem definition and the objective function are described in Section 4. The proposed HWOA based mBA algorithm for solving the task scheduling problem is presented in Section 5. Section 6 describes the experimental results and discussions. Finally, Section 7 concludes the paper.

Section snippets

Summary of existing works

There are several algorithms to deal with the issue of task scheduling in cloud computing that was proposed with the aim to decrease the average response time, makespan, decrease the overall execution time and obtain the load balance. Table 1 shows the related works regarding the task scheduling problems. Attiya I et al. [26] proposed the modified Harris hawk’s optimization (HHO) algorithm that depends upon the simulated annealing (SA) for scheduling the jobs in the cloud computing environment.

System model

The task scheduling policy affects the resource utilization efficiency for various schemes. Hence, the input tasks allocation to the computing resources becomes the major problem for the task scheduling in the cloud computing environment. The task scheduling performance of the several scheduling is logically independent of each other. The task scheduling procedure consists of three subsequent steps. Initially, depending upon the detailed data of the input tasks and the fundamental accessible

Problem definition and objective function

This section describes the problem definition regarding task scheduling for the problem of reducing the operational cost and execution time. The problem definition and objective function are described in the following section.

HWOA based mBA algorithm for the optimal task scheduling

Fig. 1 describes the structure of the task scheduling scheme in the cloud computing environment. In this, the user provides the tasks to the cloud system where the cloud system is composed of three components: task manager, scheduler and resource manager. The cloud system transmits the tasks to the task manager for processing the tasks and achieves the information such as the task size. The resource manager controls the entire virtual machine and achieves the computation speed of the virtual

Results and discussions

In this section, the experimental setup, performance evaluation, and comparative analysis of the proposed multi-objective hybrid whale optimization are explained.

Conclusions

In this paper, the hybrid WOA based mBA algorithm for solving the multi-objective task scheduling problems is proposed in the cloud computing environment aims to enhance the performance of the system by the specified computing resources. The proposed algorithm helps to decrease the computation cost and the execution time when compared to other algorithms. Makespan, resource utilization, energy consumption, execution time, computation cost and degree of workload balance are computed for the

CRediT authorship contribution statement

N. Manikandan: Conceptualization, Methodology, Software. N. Gobalakrishnan: Data curation, Writing – original draft. K. Pradeep: Visualization, Investigation, Writing – review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Compliance with ethical standards

Human and Animal Rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed Consent

For this type of study informed consent is not required

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