Evolutionary Algorithms to Optimize Task Scheduling Problem for the IoT Based Bag-of-Tasks Application in Cloud–Fog Computing Environment

Nguyen, Binh Minh; Bình, Huỳnh Thị Thanh; Anh, Tran The; Do, Son

doi:10.3390/app9091730

Cited by 169 publications

(92 citation statements)

References 24 publications

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“…In [23], a genetic algorithm is proposed to schedule IoT tasks on the fog nodes with the aim of achieving a trade-off between computation time and the operational cost. A similar work is proposed in [24] using a PSO algorithm. However, none of these studies considers the communication cost.…”

Section: Background and Related Workmentioning

confidence: 99%

Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

2020

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The current thinking concerning computations required by Internet of Things (IoT) applications is shifting toward fog computing instead of cloud computing, thereby achieving most of the required computations at the network edge of the IoT devices. Fog computing can thus improve the quality of service of delay-sensitive applications by allowing such applications to take advantage of the low latency provided by fog computing rather than the high latency of the cloud. Therefore, tasks in various IoT applications must be effectively distributed over the fog nodes to improve the quality of service, specifically the task response time. In this paper, two nature-inspired meta-heuristic schedulers, namely ant colony optimization (ACO) and particle swarm optimization (PSO), are used to propose two different scheduling algorithms to effectively load balance IoT tasks over the fog nodes under communication cost and response time considerations. The experimental results of the proposed algorithms are compared with those of the round robin (RR) algorithm. The evaluations show that the proposed ACO-based scheduler achieves an improvement in the response times of IoT applications compared to the proposed PSO-based and RR algorithms and effectively load balances the fog nodes. INDEX TERMS Fog computing, Internet of Things, quality of service, task offloading and scheduling.

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Section: Background and Related Workmentioning

confidence: 99%

Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

2020

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“…Nguyen et al 86 introduced a new approach to the optimization of the task scheduling problem for bag‐of‐tasks applications in fog–cloud environment on the basis of the execution time and operating costs. Their proposed algorithm, called Time‐Cost aware Scheduling (TCaS), is based on the evolutionary GA and attempts to achieve a proper trade‐off between the execution time and monetary cost for completing bag‐of‐tasks in the fog–cloud system.…”

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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“…Rectified Linear Units were used as activation function instead of Sigmoid function to prevent gradient vanishing. Nguyen et al proposed a genetic algorithm for optimization of job scheduling in Cloud‐Fog environment. Their aim was to reduce execution time of jobs.…”

Section: Related Workmentioning

confidence: 99%

A job scheduling algorithm for delay and performance optimization in fog computing

Jamil

Shojafar

Ahmed

et al. 2019

Concurrency and Computation

107

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Summary Due to an ever‐increasing number of Internet of Everything (IoE) devices, massive amounts of data are produced daily. Cloud computing offers storage, processing, and analysis services for handling of such large quantities of data. The increased latency and bandwidth consumption is not acceptable to real‐time applications like online gaming, smart health, video surveillance, etc. Fog computing has emerged to overcome the increase in latency and bandwidth consumption in Cloud computing. Fog Computing provides storage, processing, networking, and analytical services at the edge of a network. As Fog Computing is still in its infancy, its significant challenges include resource‐allocation and job‐scheduling. The Fog devices at the edge of the network are resource‐constrained. Therefore, it is important to decide the assignment and scheduling of a job on a Fog node. An efficient job scheduling algorithm can reduce energy consumption and response time of an application request. In this paper, we propose a novel Fog computing scheduler that supports service‐provisioning for Internet of Everything, which optimizes delay and network usage. We present a case study to optimally schedule the requests of Internet of Everything devices on Fog devices and efficiently address their demands on available resources on every Fog device. We consider delay and energy consumption as performance metrics and evaluate the proposed scheduling algorithm using iFogSim in comparison with existing approaches. The results show that the delay and network usage of the proposed scheduler improve by 32% and 16%, respectively, in comparison with FCFS approach.

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Evolutionary Algorithms to Optimize Task Scheduling Problem for the IoT Based Bag-of-Tasks Application in Cloud–Fog Computing Environment

Cited by 169 publications

References 24 publications

Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

Task scheduling approaches in fog computing: A systematic review

A job scheduling algorithm for delay and performance optimization in fog computing

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