A Genetic Based Improved Load Balanced Min-Min Task Scheduling Algorithm for Load Balancing in Cloud Computing

Rajput, Shyam Singh; Kushwah, Virendra Singh

doi:10.1109/cicn.2016.139

Cited by 39 publications

(15 citation statements)

References 9 publications

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“…At the same time, PSO does not give any guarantees on determining the most optimal solution. The paper [10] proposed an improved load balanced Min-Min (ILBMM) algorithm using genetic algorithm (GA) in order to minimize the makespan and increase the utilization of resource. The implementation of proposed algorithm has been completed using CloudSim simulator.…”

Section: Related Workmentioning

confidence: 99%

Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm

Abdulhammed

2021

J Supercomput

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IoT is one of the most important technologies that are used over the past few years, where everything is connected to the Internet; it is used in many fields; one of these fields is healthcare system that includes mobile health and remote patient monitoring (patients with kidney, heart disease, cancer, blood pressure, diabetes, respiratory disease and stroke). Integration of IoT and cloud computing can improve the performance of healthcare system and the development of the innovative applications in future. One of the major problems that cannot be ignored in cloud computing system is load balancing. Solving that problem leads to reduce the response time, power consumption, cost and increase server availability. This paper is comprised of two parts which are creating and implementing healthcare system by using IoT, and solving the problem of load balancing of the cloud computing by using intelligent algorithm called sparrow search algorithm (SSA). The SSA is used to select the best virtual machine (VM) among a group of VMs depending on the its fitness value; also many and varied tasks are scheduled with priority and assign to the best VMs depending on the its instruction millions (IM), where the task that has high IM is assigned to the best VM that has high fitness value. The outcomes demonstrated that the proposed method focuses to reduce the latency and packet loss while maximizing the throughput in healthcare systems; also the SSA has proved its robustness, efficiency and success when compared to other methods in terms of reducing makespan time, total processing time and provides load balancing among VMs, where the value of makespan time, processing time and degree of imbalance has decreased into (23.05), (899.8979) and (177.7675), respectively, in case of applying 500 tasks.

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Section: Related Workmentioning

confidence: 99%

Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm

Abdulhammed

2021

J Supercomput

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“…In addition, it does not consider the workload of the machines prior to the scheduling. Although this job increases the total system throughput, longer tasks should wait for the completion of shorter ones, which is challenging for tasks with higher priorities or with short deadlines 50–52 . Max–Min algorithm acts similar to the Min–Min one, but it selects the biggest tasks for execution at first, and the tasks are executed on machines, which leads to the shortest execution times, just like the previous algorithm.…”

Section: Organization Of the Task Schedulingmentioning

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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“…Makasarwala et al [19] also uses genetic algorithm for load balancing by considering the priority of request based on their time for population initialization. Rajput et al [20] proposed Load Balanced Min-Min (ILBMM) algorithm, which uses genetic algorithm to minimize the makespan and increasing the resource utilization. Liu et al [21]'s improved genetic algorithm by improving the selection and crossover operators in the genetic process yields a better optimization accuracy compared to the traditional algorithms such as FIFO, Round Robing scheduling algorithm, and genetic algorithm for load balancing on virtual machines (VMs).…”

Section: Fig 1 Load Balancing Modelmentioning

confidence: 99%

Multi-Agent Genetic Algorithm for Efficient Load Balancing in Cloud Computing

Jayswal*¹,

Saxena²

2020

IJITEE

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Cloud computing, one of the fastest growing fields, is the the delivery of computing resources and services. Load balancing is a key problem in cloud computing (CC) that deals with the even distribution of work load across multiple virtual machines to ensure that no machine is overloaded or underutilized during the task computation. The load balancing optimization problem is an NP-hard problem, hence, for the optimal usage of available resources, we propose a new efficient user-priority multi-agent genetic algorithm (GA). Our algorithm takes the “users’ priority and earliest job finishing time” into consideration for minimizing the response time and energy. We simulate our algorithm using Cloud-Analyst and show that our algorithm outperforms the existing algorithms for load balancing.

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A Genetic Based Improved Load Balanced Min-Min Task Scheduling Algorithm for Load Balancing in Cloud Computing

Cited by 39 publications

References 9 publications

Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm

Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm

Task scheduling approaches in fog computing: A systematic review

Multi-Agent Genetic Algorithm for Efficient Load Balancing in Cloud Computing

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