Task Popularity-Based Energy Minimized Computation Offloading for Fog Computing Wireless Networks

Kim, Junsung; Ha, Tae Youl; Yoo, Wonsuk; Chung, Jong-Moon

doi:10.1109/lwc.2019.2911521

Cited by 24 publications

(11 citation statements)

References 10 publications

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“…We denote the communication capacity between the fog device f j and the central cloud by R j . Similar to (13), the ofoading time by the fog device f j is calculated as follows:…”

Section: E L U Imentioning

confidence: 99%

“…So far, extensive research has been conducted on ofloading optimization in fog computing. Some of the essential methods used in the literature are game theory [10], auction theory [1,11,12], probabilistic modeling [13], heuristic [14], and metaheuristic [15]. Recently, the use of machine learning methods, especially reinforcement learning (RL) [16] to optimize ofoading has received much attention from the research community.…”

Section: Introductionmentioning

confidence: 99%

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An Auction-Based Bid Prediction Mechanism for Fog-Cloud Offloading Using Q-Learning

2023

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In the fog computing paradigm, if the computing resources of an end device are insufficient, the user’s tasks can be offloaded to nearby devices or the central cloud. In addition, due to the limited energy of mobile devices, optimal offloading is crucial. The method presented in this paper is based on the auction theory, which has been used in recent studies to optimize computation offloading. We propose a bid prediction mechanism using Q-learning. Nodes participating in the auction announce a bid value to the auctioneer entity, and the node with the highest bid value is the auction winner. Then, only the winning node has the right to offload the tasks on its upstream (parent) node. The main idea behind Q-learning is that it is stateless and only considers the current state to perform an action. The evaluation results show that the bid values predicted by the Q-learning method are near-optimal. On average, the proposed method consumes less energy than traditional and state-of-the-art techniques. Also, it reduces the execution time of tasks and leads to less consumption of network resources.

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“…We denote the communication capacity between the fog device f j and the central cloud by R j . Similar to (13), the ofoading time by the fog device f j is calculated as follows:…”

Section: E L U Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Auction-Based Bid Prediction Mechanism for Fog-Cloud Offloading Using Q-Learning

2023

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“…[15] also considered a 5G fog radio access network to tackle offloading decision challenge and minimize energy and delay. Taking the energy minimization factor into consideration, the authors in [16] has come up with a significant probabilistic fogcloud computation model based on popularity distribution of cloud tasks. Delay optimization in task offloading makes the fog network more scalable and efficient, serving the primary purpose of latency reduction for fog computing.…”

Section: Related Workmentioning

confidence: 99%

ioFog: Prediction-based Fog Computing Architecture for Offline IoT

Alam¹,

Ahmed²,

Matam³

et al. 2021

Preprint

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<div>Due to the multi-hop, long-distance, and wireless backbone connectivity, provisioning critical and diverse services face challenges such as low latency and reliability. This paper proposes ioFog, an offline fog architecture for achieving reliability and low latency in a large backbone network. Our solution uses a Markov chain-based task prediction model to offer dynamic service requirements with minimal dependency on the Internet. The proposed architecture considers a central Fog Controller (FC) to (i) provide a global status overview and (ii) predict upcoming tasks of Fog Nodes for intelligent offloading decisions. The FC also has the current status of the existing fog nodes in terms of their processing and storage capabilities. Accordingly, it can schedule the possible future offline computations and task allocations. ioFog considers the requirements of individual IoT applications and enables improved fog computing decisions. As compared to the existing offline IoT solutions, ioFog improves service time significantly and service delivery ratio up to 23%.</div>

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“…The advantage of the JUFO scheme comes from using the profile of each cloud task in the optimized fog server offloading control scheme. Simulation results show that the JUFO scheme can provide a significant savings in energy consumption while supporting real-time service requirements in regions with burdening workloads [14].…”

Section: Related Workmentioning

confidence: 99%

“…The Joint User equipment and Fog Optimization (JUFO) scheme is designed to minimize the energy consumption of the user's equipment and fog system based on the priority distribution of cloud tasks while maintaining service time constraints [14]. It is based on the popularity distribution of cloud tasks and energy consumption model.…”

Section: Related Workmentioning

confidence: 99%

New Application Task Offloading Algorithms for Edge, Fog, and Cloud Computing Paradigms

Kim

2020

Wireless Communications and Mobile Computing

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In the last few years, we have seen an exponential increase in the number of computation-intensive applications, which have resulted in the popularity of fog and cloud computing paradigms among smart-chip-embedded mobile devices. These devices can partially offload computation tasks either using the fog system or using the cloud system. In this study, we design a new task offloading scheme by considering the challenges of future edge, fog and cloud computing paradigms. To provide an effective solution toward an appropriate task offloading problem, we focus on two cooperative bargaining game solutions—Tempered Aspirations Bargaining Solution (TABS) and Gupta-Livne Bargaining Solution (GLBS). To maximize the application service quality, a proper bargaining solution should be properly selected. In the proposed scheme, the TABS method is used for time-sensitive offloading services, and the GLBS method is applied to ensure computation-oriented offloading services. The primary advantage of our bargaining-based approach is to provide an axiom-based strategic solution for the task offloading problem while dynamically responding to the current network environments. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme, and the superior performance over existing schemes is observed. Finally, we show prime directions for future work and potential research issues.

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Task Popularity-Based Energy Minimized Computation Offloading for Fog Computing Wireless Networks

Cited by 24 publications

References 10 publications

An Auction-Based Bid Prediction Mechanism for Fog-Cloud Offloading Using Q-Learning

An Auction-Based Bid Prediction Mechanism for Fog-Cloud Offloading Using Q-Learning

ioFog: Prediction-based Fog Computing Architecture for Offline IoT

New Application Task Offloading Algorithms for Edge, Fog, and Cloud Computing Paradigms

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