Incentive-Aware Micro Computing Cluster Formation for Cooperative Fog Computing

Luo, Siqi; Chen, Xu; Zhou, Zhi; Chen, Xiang; Wu, Weigang

doi:10.1109/twc.2020.2967371

Cited by 48 publications

(14 citation statements)

References 38 publications

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“…A large body of recent research worked on addressing the challenges in offloading. The four offload methods were proposed in [15]. 1) Local Mobile Execution, 2) D2D Offloaded Execution, 3) Direct Fog Offloaded Execution, 4) D2D-Assisted Fog Offloaded Execution.…”

Section: Related Workmentioning

confidence: 99%

Balanced Computing Offloading for Selfish IoT Devices in Fog Computing

2022

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Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. Offloading tasks to the fog that is closer to IoT users for processing has become a means to ensure that tasks are completed quickly. Fog computing can not only reduce the congestion of the backbone network but also ensure that the task is completed within the specified time. Since fog resources are limited, there will be resource competition among IoT devices. How to quickly and efficiently make an optimal computation offloading decision for individual selfish IoT devices is a fundamental research issue. This article regards the process of multiple IoT devices competing for fog devices as a game and proposes a distributed computation offloading algorithm. The goal is to optimize the balance of computation delay, energy consumption, and cost for fog nodes. The competition between IoT nodes eventually reaches an equilibrium point, that is the Nash equilibrium point. We prove the existence of Nash equilibrium by Weighted Potential Game. In addition, if a large number of IoT devices select the same node for offloading, which will cause the fog node to run out of power and make some networks unable to work normally. Further, causing part of the network to be paralyzed. Therefore, the paper considers the fairness of offloading to extend the network life cycle. A calculation rate adjustment algorithm is designed for the fairness of offloading to ensure that fog nodes do not run out of power and fail. This paper not only fully considers the performance of the IoT device, but also considers the fairness of the fog. Numerous experiments proved the effectiveness of the proposed algorithm.

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Section: Related Workmentioning

confidence: 99%

Balanced Computing Offloading for Selfish IoT Devices in Fog Computing

2022

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“…Applications like speech recognition and computer vision are process and storage demanding ones, so it might require to be distributed between a group of fog nodes to accomplish the rigorous latency requirements and high network availability. Thus, fog cluster management must manage adjacent fog nodes, usually of similar capabilities, to form clusters of nodes [49]. A cluster is composed logically of several containers that cooperate to split a task and process it in parallel [50].…”

Section: Fc Software Featuresmentioning

confidence: 99%

Fog Computing Based on Machine Learning: A Review

Samann

Abdulazeez

Askar

2021

Int. J. Interact. Mob. Technol.

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<p>Internet of Things (IoT) systems usually produce massive amounts of data, while the number of devices connected to the internet might reach billions by now. Sending all this data over the internet will overhead the cloud and consume bandwidth. Fog computing's (FC) promising technology can solve the issue of computing and networking bottlenecks in large-scale IoT applications. This technology complements the cloud computing by providing processing power and storage to the edge of the network. However, it still suffers from performance and security issues. Thus, machine learning (ML) attracts attention for enabling FC to settle its issues. Lately, there has been a growing trend in utilizing ML to improve FC applications, like resource management, security, lessen latency and power usage. Also, intelligent FC was studied to address issues in industry 4.0, bioinformatics, blockchain and vehicular communication system. Due to the ML vital role in the FC paradigm, this work will shed light on recent studies utilized ML in a FC environment. Background knowledge about ML and FC also presented. This paper categorized the surveyed studies into three groups according to the aim of ML implementation. These studies were thoroughly reviewed and compared using sum-up tables. The results showed that not all studies used the same performance metric except those worked on security issues. In conclusion, the simulations of proposed ML models are not sufficient due to the heterogeneous nature of the FC paradigm.</p>

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“…As a complement to cloud and edge computing, fog computing provides a decentralized framework whereby the available computation resources of nearby mobile devices are exploited for task offloading through incentive policies [6][7][8]. Due to the proximity of the available resources, this type of task partitioning mechanism reduces the latency and the backbone traffic in the network, and in turn, increases energy efficiency [9].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient D2D-Aided Fog Computing under Probabilistic Time Constraints

Karatalay¹,

Psaromiligkos²,

Champagne³

2022

Preprint

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Device-to-device (D2D) communication is an enabling technology for fog computing by allowing the sharing of computation resources between mobile devices. However, temperature variations in the device CPUs affect the computation resources available for task offloading, which unpredictably alters the processing time and energy consumption. In this paper, we address the problem of resource allocation with respect to task partitioning, computation resources and transmit power in a D2Daided fog computing scenario, aiming to minimize the expected total energy consumption under probabilistic constraints on the processing time. Since the formulated problem is non-convex, we propose two sub-optimal solution methods. The first method is based on difference of convex (DC) programming, which we combine with chance-constraint programming to handle the probabilistic time limitations. Considering that DC programming is dependent on a good initial point, we propose a second method that relies on only convex programming, which eliminates the dependence on user-defined initialization. Simulation results demonstrate that the latter method outperforms the former in terms of energy efficiency and run-time.

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Incentive-Aware Micro Computing Cluster Formation for Cooperative Fog Computing

Cited by 48 publications

References 38 publications

Balanced Computing Offloading for Selfish IoT Devices in Fog Computing

Balanced Computing Offloading for Selfish IoT Devices in Fog Computing

Fog Computing Based on Machine Learning: A Review

Energy-Efficient D2D-Aided Fog Computing under Probabilistic Time Constraints

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