Distributed Task Scheduling in Cloud Platform: A Survey

Hazra, Debojyoti; Roy, Asmita; Midya, Sadip; Majumder, Koushik

doi:10.1007/978-981-10-5544-7_19

Cited by 14 publications

(3 citation statements)

References 7 publications

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“…Initially, many researchers focused on heuristic algorithms for solving task scheduling problem in the cloud computing environment. The heuristic algorithms mainly focus on finding an optimal or near optimal solution [16]. These algorithms explore search space for finding the best solution by using features of the problem.…”

Section: Related Workmentioning

confidence: 99%

A Metaheuristic Framework for Dynamic Virtual Machine Allocation With Optimized Task Scheduling in Cloud Data Centers

Alsadie

2021

IEEE Access

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Optimal allocation of virtual machines in a cloud computing environment for user-submitted tasks is a challenging task. Finding an optimal task scheduling solution is considered as NP-hard problem specifically for large task sizes in the cloud environment. The best solution involves scheduling the tasks to virtual machines data centre while minimizing the essential, influential and cost effective parameters such as energy usage, makespan and cost. In this direction, this work presents a metaheuristic framework called MDVMA for dynamic virtual machine allocation with optimized task scheduling in a cloud computing environment. The MDVMA focuses on developing a multi-objective scheduling method using non dominated sorting genetic algorithm (NSGA)-II algorithm-based metaheuristic algorithm for optimizing task scheduling with the aim of minimizing energy usage, makespan and cost simultaneously to provide tradeoff to the cloud service providers as per their requirements. To evaluate the performance of the MDVMA approach, we compared the performances of two different scenarios of benchmark real-world workload data sets using the existing approaches, namely, Artificial Bee Colony (ABC) algorithm, Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) algorithm. Simulation results demonstrate that optimizing task scheduling leads to better overall results in terms of minimizing energy usage, makespan and cost of the cloud data center. Finally, the paper concludes metaheuristic algorithms as a promising method for task scheduling in a cloud computing environment.INDEX TERMS Cloud computing, energy consumption, task scheduling, meta-heuristics algorithm, Optimization.

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Section: Related Workmentioning

confidence: 99%

A Metaheuristic Framework for Dynamic Virtual Machine Allocation With Optimized Task Scheduling in Cloud Data Centers

Alsadie

2021

IEEE Access

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“…When scheduling a task for a particular resource, different parameters are considered, such as the task deadline, waiting time, input time, the cost for performing a job, and the energy consumption of a resource for running a task. The objective of a task scheduler is to assign tasks to resources in a way that it leads to the optimization of resource utilization 34 . In other words, scheduling means to assign start time and finish time to a set of tasks considering certain existing limitations.…”

Section: Conceptmentioning

confidence: 99%

Task scheduling approaches in fog computing: A systematic review

Alizadeh

Khajehvand

Rahmani

et al. 2020

Int J Communication

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SummaryThe Internet of Things (IoT) interconnects billions of physical objects to collect and exchange information and makes available various applications. Despite all the advantages of the IoT, some of its applications are not feasible because of the existing restrictions in the IoT sensors. To overcome these restrictions, cloud computing has been developed in recent years. Although cloud computing has removed some of these restrictions, the cloud itself has faced some other challenges. One of these challenges is the distance between the cloud and the end‐devices, which is not ideal for delay‐sensitive applications and also leads to high communication costs and security problems. To handle these challenges, the fog computing has been introduced in literature, which places the resources and services at the edge of the network, close to the end‐devices. On the other hand, fog nodes suffer from heterogeneity, uncertainty, and dispersion of resources, and also processing, storage, and memory limitations. Thus, there is need for a proper task scheduling approach to use these resources optimally. In this study, the task scheduling algorithms proposed by different researchers for the cloud‐fog environment, their advantages and disadvantages, and also various tools and issues regarding the scheduling methods and their restrictions were discussed. The results indicated that about 58% of the scheduling algorithms use static scheduling and that the other 42% use dynamic scheduling, and the delay metric is the most noteworthy parameter considered in most studies with a share of nearly 17%. Finally, we identified the open issues related to the task scheduling based on cloud‐fog computing and provided some directions for future works.

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“…These services include Infrastruc-A c c e p t e d M a n u s c r i p t Fig. 2: Types of Cloud Task Schedulers ture, Platform, and Software which are known as Software-as-Services (SaaS), Platform-as-Services (PaaS), and Infrastructure-as-Services (IaaS) [3,15]. For better delivery of cloud services, efficient utilization of cloud resources is essential [17].…”

Section: Introductionmentioning

confidence: 99%

RADL: a resource and deadline-aware dynamic load-balancer for cloud tasks

et al. 2022

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Cloud service providers acquire the computing resources and allocate them to their clients. To effectively utilize the resources and achieve higher user satisfaction, efficient task scheduling algorithms play a very pivotal role.A number of task scheduling technique have been proposed in the literature. However, majority of these scheduling algorithms fail to achieve efficient resource utilization that causes them to miss tasks deadlines. This is because these algorithms are not resource and deadline-aware. In this research, a Resource and deadline Aware Dynamic Load-balancer (RADL) for Cloud tasks has been presented. The proposed scheduling scheme evenly distribute the incoming workload of compute-intensive and independent tasks at run-time. In addition, RADL approach has the capability to accommodate the newly arrived tasks (with shorter deadlines) efficiently and reduce task rejection. The proposed scheduler monitors/updates the task and VM status at run-time. Experimental results shows that the proposed technique has attained up to 67.74%, 303.57%, 259.2%, 146.13%, 405.06%, and 259.14% improvement for average resource utilization, meeting tasks deadlines, lower makespan, task response time, penalty cost, and task execution cost respec-

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Distributed Task Scheduling in Cloud Platform: A Survey

Cited by 14 publications

References 7 publications

A Metaheuristic Framework for Dynamic Virtual Machine Allocation With Optimized Task Scheduling in Cloud Data Centers

A Metaheuristic Framework for Dynamic Virtual Machine Allocation With Optimized Task Scheduling in Cloud Data Centers

Task scheduling approaches in fog computing: A systematic review

RADL: a resource and deadline-aware dynamic load-balancer for cloud tasks

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