FBRC: Optimization of task Scheduling in Fog-Based Region and Cloud

Hoang, Doan B.; Dang, Thanh Dat

doi:10.1109/trustcom/bigdatase/icess.2017.360

Cited by 51 publications

(32 citation statements)

References 11 publications

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“…Dang and Hoang 69 proposed a method for optimization of task scheduling in fog–cloud computing in order to minimize the completion time of tasks. The problem space is divided into some areas, each including a physical place where required services are provided for users of that area.…”

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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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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“…The algorithm considers computing capability, communication cost and delay requirements of Fog devices to achieve better performance. Another heuristic based job scheduling algorithm was designed by Hoang and Dang to achieve low latency and better performance. They propose a Fog‐based region architecture to assign jobs to Fog regions and Cloud.…”

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

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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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“…They formulated a workload allocation problem and approached this problem by decomposing the primal problem into three subproblems. Hoang et al proposed the fog-based region and cloud (FBRC) framework [28]. The energy consumption formula was taken as the objective function, and the maximum delay was set as the constraint condition.…”

Section: Related Workmentioning

confidence: 99%

Energy Consumption Optimization With a Delay Threshold in Cloud-Fog Cooperation Computing

Yan

Chen

et al. 2019

IEEE Access

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With the rapid development of the Internet of Things (IoT), the number of mobile terminal devices is increasing. Massive data are generated by mobile terminal devices, resulting in high delay and high energy consumption. In most cases, however, a low delay means high energy consumption. To balance energy consumption and delay, we adopt a tradeoff strategy that can realize optimal energy consumption with a delay threshold in this paper. First, we introduce the role of the delay threshold in reducing delay. Then, we describe the delay and energy consumption of the mobile terminal layer, fog node layer and cloud server layer with queue theory. Nonlinear programming is used to solve the energy optimization problem by calculating the optimal workload of each layer. We design a cloud-fog cooperation scheduling algorithm to reduce energy consumption. A task offloading algorithm is also designed to complete tasks when their nodes leave. The experimental results show that the energy consumption is reduced by approximately 22%, while the delay is 12.5% less than the first come first served (FCFS) approach. INDEX TERMS Energy consumption optimization, fog computing, tasks scheduling.

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FBRC: Optimization of task Scheduling in Fog-Based Region and Cloud

Cited by 51 publications

References 11 publications

Task scheduling approaches in fog computing: A systematic review

Task scheduling approaches in fog computing: A systematic review

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

Energy Consumption Optimization With a Delay Threshold in Cloud-Fog Cooperation Computing

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