A <scp>PSO</scp>‐based task scheduling algorithm improved using a load‐balancing technique for the cloud computing environment

Ebadifard, Fatemeh; Babamir, Seyed Morteza

doi:10.1002/cpe.4368

Cited by 126 publications

(64 citation statements)

References 37 publications

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“…In the task‐VM mapping phase, an adapted queuing model is used in order to minimize the waiting time and completion time. Ebadifard and Babamir have proposed a static task scheduling method for non‐preemptive and independent tasks in cloud computing based on the particle swarm optimization (PSO) algorithm. In the work of Shao et al, a novel virtual node–based distributed load‐balancing algorithm for range‐queriable cloud storage is proposed.…”

Section: Related Workmentioning

confidence: 99%

Load balancing in cloud computing environments based on adaptive starvation threshold

Semmoud

Hakem

Benmammar

et al. 2020

Concurrency and Computation

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Clouds provide to users on-demand access to large computing and storing resources and offer over on premise IT infrastructures many advantages in terms of cost, flexibility, and availability.However, this new paradigm still faces many challenges, and in this paper, we address the load balancing problem. Even though many approaches have been proposed to balance the load among the servers, most of them are too sensitive to the fluctuation in the clouds load and produce unstable systems. In this paper, we propose a new distributed load balancing algorithm, based on adaptive starvation threshold. It tries to balance the load between the servers while minimizing the response time of the cloud, maximizing the utilization rate of the servers, decreasing the overall migration cost, and maintaining the stability of the system.The performance of the proposed algorithm was compared to a well-known load balancing algorithm, inspired from the honey bee behavior (HBB). The experimental results showed that the application of the proposed load balancing algorithm gives considerable performance gains and a significant reduction in number of migrations when compared to the performance of the HBB algorithm. KEYWORDScloud computing, distributed systems, load balancing, virtualization INTRODUCTIONCloud computing is a very promising technology which enables users on-demand network access to a shared set of computing and storing resources. The Infrastructure as a Service (IaaS) is the lowest-level cloud service paradigm where the cloud provides shared hardware and software to meet users' demands. In IaaS, a virtual machine (VM) is endowed with processing power, memory, and permanent storage to execute its assigned tasks within a reasonable time. 1 Since in a Cloud, VMs might be heterogeneous and the job arrival time is not predictable, an efficient and user-transparent workload control of the VMs is essential to improve the overall performance of the cloud.In this paper, it is assumed that tasks of random size arrive at random intervals and get randomly allocated to data centers. Therefore, the heterogeneous VMs composing the cloud do not get the same load and an efficient load balancing algorithm must be applied to dynamically balance the load between the VMs according to their capacities. It should also improve the total occupancy of the computing resources and thus speed up the execution time of the user-submitted applications. Moreover, it should guarantee the good quality of service (QoS) of the cloud by reducing its response time and ensuring its scalability and fault tolerance regardless of the heterogeneity of its VMs.A typical load balancing algorithm includes the information, location, transfer, and selection policies. 2,3 The information policy is responsible for updating the load information and specifying how it should be collected. The location policy selects the appropriate resources for a job transfer.The transfer policy uses the resources load information to decide when to perform load balancing and to determine when a...

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Section: Related Workmentioning

confidence: 99%

Load balancing in cloud computing environments based on adaptive starvation threshold

Semmoud

Hakem

Benmammar

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

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“…It is simple to implement and has fast convergences. Despite its advantages, it gets trapped in local optimum for the complex problems [25].…”

Section: Related Workmentioning

confidence: 99%

“…The complete description of total execution time (makespan) and total execution cost is explained in the following sub-sections:-1) Total execution time (makespan): The total execution time (makespan) is the maximum completion time taken by tasks in the workflow. In other words, makespan is the time required for finishing all the tasks allotted to different virtual machines [25]. Mathematically, the makespan of the workflow can be derived using equation 2.…”

Section: A Fitness Functionmentioning

confidence: 99%

HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing

Arora¹,

Banyal²

2020

IJACSA

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Virtualization is one of the key features of cloud computing, where the physical machines are virtually divided into several virtual machines in the cloud. The user's tasks are run on these virtual resources as per the requirements. When the user requests the services to the cloud, the user's tasks are allotted to the virtual resources depending on their needs. An efficient scheduling mechanism is required for optimizing the involved parameters. Scientific workflows deals with a large amount of data with dependency constraints and is used to simplify the applications in the diverse scientific domains. Scheduling the workflow in cloud computing is a well-known NP-hard problem. Deploying such data-and compute-intensive workflow on the cloud needs an efficient scheduling algorithm. In this paper, we have proposed a multi-objective model based hybrid algorithm (HPSOGWO), which combines the desirable characteristics of two well-known algorithms, particle swarm optimization (PSO), and grey wolf optimization (GWO). The results are analyzed under complex real-world scientific workflows such as Montage, CyberShake, Inspiral, and Sipht. We have considered the two essential parameters: total execution time and total execution cost while working in the cloud environment. The simulation results show that the proposed algorithm performs well compared to other state-of-the-art algorithms such as round-robin (RR), ant colony optimization (ACO), heterogeneous earliest time first (HEFT), and particle swarm optimization (PSO).

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“…They overcome the limitation of the population size by using the tournament selection method to select the best chromosomes. Researchers in [106] presented a static task scheduling technique based on PSO algorithm.…”

Section: Schedulingmentioning

confidence: 99%

Task scheduling in cloud computing based on metaheuristic techniques: A review paper

Al-Arasi¹,

Saif²

2020

EAI Endorsed Transactions on Cloud Systems

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Cloud computing delivers computing resources like software and hardware as a service to the users through a network. Due to the scale of the modern datacentres and their dynamic resources provisioning nature, we need efficient scheduling techniques to manage these resources. The main objective of scheduling is to assign tasks to adequate resources in order to achieve one or more optimization criteria. Scheduling is a challenging issue in the cloud environment, therefore many researchers have attempted to explore an optimal solution for task scheduling in the cloud environment. They have shown that traditional scheduling is not efficient in solving this problem and produce an optimal solution with polynomial time in the cloud environment. However, they introduced sub-optimal solutions within a short period of time. Meta-heuristic techniques have provided near-optimal or optimal solutions within an acceptable time for such problems. In this work, we have introduced the major concepts of resource scheduling and provided a comparative analysis of many task scheduling techniques based on different optimization criteria.

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A PSO‐based task scheduling algorithm improved using a load‐balancing technique for the cloud computing environment

Cited by 126 publications

References 37 publications

Load balancing in cloud computing environments based on adaptive starvation threshold

Load balancing in cloud computing environments based on adaptive starvation threshold

HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing

Task scheduling in cloud computing based on metaheuristic techniques: A review paper

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