An Efficient Resource Monitoring Service for Fog Computing Environments

Battula, Sudheer Kumar; Garg, Saurabh; Montgomery, James; Kang, Byeong Ho

doi:10.1109/tsc.2019.2962682

Cited by 35 publications

(11 citation statements)

References 40 publications

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“…Offloading ML computations across devices makes Deep Learning via Edge Computing a realistic concept [13], as tasks can be scheduled on FNs or at the edge is beneficial [14]. Instead of having the IoT device access the Cloud each time, the computation can be distributed to multiple FNs.…”

Section: B Computation Offloading and Resource Allocationmentioning

confidence: 99%

Loop-the-Loops: Fragmented Learning Over Networks for Constrained IoT Devices

Deb

Mukherjee

Singh

et al. 2023

IEEE Trans. Parallel Distrib. Syst.

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In this work, we propose Timed Loop Gears (TLG), as a distributed method for enabling fragmented learning in Resource-Constrained networked IoT edge devices. TLG identifies atomic operations (gears), such as feed-forward and backpropagation, necessary for training Machine Learning (ML) models. Each of these gears executes on a Fog Node (FN) exclusively for each data point at a time rather than the whole dataset in its entirety. Additionally, the networked Edge Devices (EDs) offload the training data to the fog layer using the Message Queuing Telemetry Transport (MQTT) protocol such that the participating FNs subscribe to incoming training data and store them based on topics, simplifying data sharing. TLG enables the FN to then transfer the partially learned weights to the next suitable FN for further training. This looping of weights is repeated across FNs until the training is complete. Through extensive analysis, we observe that, compared to existing distributed ML training approaches, for n devices, TLG reduces the probability of disruption due to device failure by n 2 times. Implementation results of our fragmented learning method demonstrate that, although TLG negligibly increases the memory consumption of the IoT devices by 0.8%, it reduces CPU usage by almost 90%. The proposed method proves beneficial for developing and hosting ML models, even on constrained IoT devices, in contrast to existing lightweight ML methods.

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Section: B Computation Offloading and Resource Allocationmentioning

confidence: 99%

Loop-the-Loops: Fragmented Learning Over Networks for Constrained IoT Devices

Deb

Mukherjee

Singh

et al. 2023

IEEE Trans. Parallel Distrib. Syst.

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“…In this paper, 71 the author has proposed a SCB technique which optimizes resource utilization in the resource monitoring service. The implementation work of the proposed technique is done with the use of nectar cloud instance and developed own java‐based emulator for resource monitoring purposes.…”

Section: Technical Classification Of Task Schedulingmentioning

confidence: 99%

A systematic review of task scheduling approaches in fog computing

Bansal

Aggarwal

2022

Trans Emerging Tel Tech

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Due to increased use of IoT devices and data sensors a huge amount of data is being produced for processing in real-time. Fog computing has evolved as a solution for fast processing of data. To complete the data processing as per requirement of user, it is processed at fog nodes which are near the user. To complete work in a specified time with limited resources, task scheduling is performed. With the increased amount of data to be processed, the completion of task within given time in fog computing is a major challenge. So, scheduling of tasks and resources is a very important issue. A lot of research has been undertaken in recent years. In this survey, authors have reviewed various task scheduling algorithms suggested by researchers to meet the user requirements.The focus of this article is on diverse scheduling techniques being deployed in fog computing. An effort has been made to classify existing approaches, research issues and determine significant issues existing in this field. There are four major scheduling categories that are used by researchers namely, static, dynamic, heuristic, and hybrid. As per this study, 17% researchers deployed static, 23% dynamic, 47% heuristic, and 13% hybrid approaches, respectively.Analysis shows that for QoS (Quality of Service) parameters 19% researchers are focused on response time, 18% on cost and energy consumption, and 16% on makespan. For QoS parameter other factors have much smaller contribution in comparison to the above factors. In the tools used, researchers observed that 40% of the researches have used iFogSim, according to literature. Besides, this research article also discusses open issues and future work in the field of fog computing. This article also underlines various open issues and future directions in the field of fog computing.

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“…The scheduling mechanisms in fog computing service take care of resource allocation. Battula et al [21] implemented a novel technique for efficient resource utilization in fog computing namely Support and Confidence (SC) technique. The proposed technique was deployed with real time traffic and it is inferred that resource consumption is lesser than the traditional methods.…”

Section: Literature Surveymentioning

confidence: 99%

Efficient Resource Allocation in Fog Computing Using QTCS Model

Iyapparaja¹,

Alshammari²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

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Infrastructure of fog is a complex system due to the large number of heterogeneous resources that need to be shared. The embedded devices deployed with the Internet of Things (IoT) technology have increased since the past few years, and these devices generate huge amount of data. The devices in IoT can be remotely connected and might be placed in different locations which add to the network delay. Real time applications require high bandwidth with reduced latency to ensure Quality of Service (QoS). To achieve this, fog computing plays a vital role in processing the request locally with the nearest available resources by reduced latency. One of the major issues to focus on in a fog service is managing and allocating resources. Queuing theory is one of the most popular mechanisms for task allocation. In this work, an efficient model is designed to improve QoS with the efficacy of resource allocation based on a Queuing Theory based Cuckoo Search (QTCS) model which will optimize the overall resource management process.

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An Efficient Resource Monitoring Service for Fog Computing Environments

Cited by 35 publications

References 40 publications

Loop-the-Loops: Fragmented Learning Over Networks for Constrained IoT Devices

Loop-the-Loops: Fragmented Learning Over Networks for Constrained IoT Devices

A systematic review of task scheduling approaches in fog computing

Efficient Resource Allocation in Fog Computing Using QTCS Model

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