Resource Allocation Strategy in Fog Computing Based on Priced Timed Petri Nets

Ni, Lina; Zhang, Jinquan; Jiang, Changjun; Yan, Chao; Yu, Kan

doi:10.1109/jiot.2017.2709814

Cited by 207 publications

(109 citation statements)

References 33 publications

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“…Jobs are scheduled to achieve minimum completion time along with better user experience. Ni et al presented a dynamic resource allocation strategy on the basis of job's completion time and the credibility of Fog nodes by using Priced Timed Petri Nets (PTPNs) to improve resource utilization and user's QoS requirements. Another job allocation algorithm was proposed by Pooranian et al in which they presented a heuristic‐based algorithm for resource allocation to optimize energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…In case of penalty, the server is not allocated for some iterations. Ni et al proposed an adaptive double fitness Genetic Task Scheduling algorithm for smart cities to optimize task make‐span and communication cost. The algorithm considers computing capability, communication cost and delay requirements of Fog devices to achieve better performance.…”

Section: Related Workmentioning

confidence: 99%

“…In case of penalty, the server is not allocated for some iterations. Ni et al 15 algorithm for job scheduling using iFogSim simulator. 24 They applied their algorithm on video surveillance (VSOT) and EEG tractor beam game (EEGTBG).…”

Section: Related Workmentioning

confidence: 99%

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

Jamil

Shojafar

Ahmed

et al. 2019

Concurrency and Computation

103

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Jamil

Shojafar

Ahmed

et al. 2019

Concurrency and Computation

103

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show abstract

“…As a result, fog computing became an extension of cloud computing. Also, known as clouds at the edge, it uses nodes within the boundaries of the network to carry out computations [21]. Fog computing is a computing paradigm that integrates with the cloud infrastructure.…”

Section: Overview Of Fog Computing With Iotmentioning

confidence: 99%

A scalable IoT-fog framework for urban sound sensing

Baucas

Spachos

2020

Computer Communications

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Internet of Things (IoT) is a system of interrelated devices that can be used to allow large-scale collection and analysis of data. However, as it grew, IoT networks were not capable of managing the data from these services. As a result, cloud computing was introduced to address the need for datacentres for IoT networks. As the technology evolved, the demand for a proper means of supporting and managing crowdsensing and real-time data increased, and cloud servers could no longer keep up with the large volumes of incoming data. This demand brought rise to fog computing. It became an extension to the cloud and allowed resources to be allocated around the network effectively. Its integration to IoT reduced the strain towards the cloud servers. However, issues in high power consumption at the end device and data management constraints surfaced. This paper proposes two approaches to alleviate these issues to keep fog computing remain as a reliable option for IoT-related applications. We created an IoT-based sensing framework that used an urban sound classification model. Through active low and high power states and resource reallocation, we created a network configuration. We tested this configuration against IoT frameworks that use the default fog and cloud setups. The results improved the framework's end device power consumption and server latency. Overall, with the proposed framework, fog computing was proven to be capable of supporting a scalable IoT framework for urban sound sensing.

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“…QoS is a measure to quantify the overall performance level of the system or service. Ni et al proposed a Priced Timed Petri Net (PTPN)‐based dynamic resource allocation and scheduling strategy for fog computing environments. He et al developed QoS‐aware admission control, offloading, and resource allocation schemes to support data analytics services aimed at maximizing the analytics service utilities.…”

Section: Fog Computing Aspectsmentioning

confidence: 99%

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Martin

Kandasamy

Chandrasekaran

et al. 2019

Int J Communication

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Summary The evolutionary advancements in the field of technology have led to the instigation of cloud computing. The Internet of Things paradigm stimulated the extensive use of sensors distributed across the network edges. The cloud datacenters are assigned the responsibility for processing the collected sensor data. Recently, fog computing was conceptuated as a solution for the overwhelmed narrow bandwidth. The fog acts as a complementary layer that interplays with the cloud and edge computing layers, for processing the data streams. The fog paradigm, as any distributed paradigm, has its set of inherent challenges. The fog environment necessitates the development of management platforms that effectuates the orchestration of fog entities. Owing to the plenitude of research efforts directed toward these issues in a relatively young field, there is a need to organize the different research works. In this study, we provide a compendious review of the research approaches in the domain, with special emphasis on the approaches for orchestration and propose a multilevel taxonomy to classify the existing research. The study also highlights the application realms of fog computing and delineates the open research challenges in the domain.

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Resource Allocation Strategy in Fog Computing Based on Priced Timed Petri Nets

Cited by 207 publications

References 33 publications

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

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

A scalable IoT-fog framework for urban sound sensing

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

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