Modified Shortest Job First for Load Balancing in Cloud-Fog Computing

Nazar, Tooba; Javaid, Nadeem; Waheed, Moomina; Fatima, Aisha; Bano, Hamida; Nouman, Ahmed

doi:10.1007/978-3-030-02613-4_6

Cited by 18 publications

(9 citation statements)

References 15 publications

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“…In [10], authors introduces a novel load balancing algorithm Abbreviation of the first task (MSJF) for regulating the load across many Virtual Machines (VMs) on Fog servers. A lot of factors influence the algorithm's level (e.g.…”

Section: Related Studiesmentioning

confidence: 99%

“…Fog computing, which enables and links rich services at the network edge, requires low latency in applications. Fog nodes can evaluate and respond to information faster than cloud service or centralized data center as they are often co-located with smart user devices [10]. • Geographical distribution: Fog computing provides services to geographically identifiable, distributed deployments, as opposed to a more centralized cloud.…”

mentioning

confidence: 99%

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Hybrid Load Balancing Algorithm for Fog Computing Environment

Abuhamdah¹,

Al-Shabi²

2022

IJSECS

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Fog computing has become a new trend in the Internet of things domain and cloud computing applications. It is a novel model to achieve the availability, flexibility and better responding time. In spite of that, there is so many challenges facing computing environments such as the misuse of the resources and load-balancing between them, which has a major effect on performance. The requirement of effective and robust load-balancing algorithms is one of the most significant interest in this field. Many researchers suggested various load-balancing algorithms in fog computing, but there is still inefficiency in the system performance and misalignment in load -balancing. This paper will provide a description of numerous concepts such as computing fog, fog nodes, load balancing and then we recommend a load-balancing algorithm to enhance the fog-computing environment performance, which is a hybrid algorithm benefits from the optimizing processing time (OPT) algorithms. In order to explore the proposed algorithm performance, a comparison made with other algorithms. Results indicates that using the proposed optimizing processing time algorithm in load-balancing algorithm has superior response and processing time than the compared algorithms to user requests, and the data total cost centre’s as well.

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Section: Related Studiesmentioning

confidence: 99%

mentioning

confidence: 99%

Hybrid Load Balancing Algorithm for Fog Computing Environment

Abuhamdah¹,

Al-Shabi²

2022

IJSECS

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“…A new convenient and dynamic scheduling algorithm is required in the cloud environment for efficient resource utilization. Some of the existing static algorithms include first in first out (FIFO) [36], shortest job first (SJF) [37], round robin (RR) [38], etc. In contrast, dynamic algorithms do not require any advanced information about VMs and tasks; however, the VM requires constant monitoring.…”

Section: Workflow Schedulingmentioning

confidence: 99%

Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing

2021

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Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources.

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“…The efficiency of the method has also been measured regarding the different broker policies, like closet DC (CDC), optimize response time (ORT), reconfigure dynamically with load (RDL), and advance service broker policy (ASP). Nazar et al [105] proposed architecture for the energy management system based on cloud and fog computing. The load management has been carried out using virtual machines on fog servers using a modified shortest job first (MSJF) algorithm.…”

Section: Edge Computing and Fog Computing-based Techniques In Smart Hmentioning

confidence: 99%

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

et al. 2019

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In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique is to maintain a balance between user comfort and energy requirements, such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gaps in the literature are due to advancements in technology, the drawbacks of optimization algorithms, and the introduction of new optimization algorithms. Further, many newly proposed optimization algorithms have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. Detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

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Modified Shortest Job First for Load Balancing in Cloud-Fog Computing

Cited by 18 publications

References 15 publications

Hybrid Load Balancing Algorithm for Fog Computing Environment

Hybrid Load Balancing Algorithm for Fog Computing Environment

Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

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