Multiobjective Optimization for Computation Offloading in Fog Computing

Liu, Liqing; Chang, Zheng; Guo, Xijuan; Mao, Shiwen; Ristaniemi, Tapani

doi:10.1109/jiot.2017.2780236

Cited by 450 publications

(237 citation statements)

References 24 publications

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“…With coordination among the communication, computation and caching, QoS requirements like high spectral efficiency, high energy efficiency and low latency for different service types can be met. Many studies on F-RANs have been conducted, like computation offloading in [7], and edge caching strategies in [8], [9]. In [7], the impact of fog computing on energy consumption and delay performance are investigated.…”

Section: A Related Workmentioning

confidence: 99%

“…Many studies on F-RANs have been conducted, like computation offloading in [7], and edge caching strategies in [8], [9]. In [7], the impact of fog computing on energy consumption and delay performance are investigated. With queuing models established, a multi-objective optimization problem considering energy consumption, execution delay and payment cost is formulated.…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

Xiang

Peng

Sun

et al. 2020

IEEE Trans. Veh. Technol.

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The mode selection and resource allocation in fog radio access networks (F-RANs) have been advocated as key techniques to improve spectral and energy efficiency. In this paper, we investigate the joint optimization of mode selection and resource allocation in uplink F-RANs, where both of the traditional user equipments (UEs) and fog UEs are served by constructed network slice instances. The concerned optimization is formulated as a mixed-integer programming problem, and both the orthogonal and multiplexed subchannel allocation strategies are proposed to guarantee the slice isolation. Motivated by the development of machine learning, two reinforcement learning based algorithms are developed to solve the original high complexity problem under traditional and fog UEs' specific performance requirements. The basic idea of the proposals is to generate a good mode selection policy according to the immediate reward fed back by an environment. Simulation results validate the benefits of our proposed algorithms and show that a tradeoff between system power consumption and queue delay can be achieved.Index Terms-fog radio access network, network slicing, reinforcement learning.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

Xiang

Peng

Sun

et al. 2020

IEEE Trans. Veh. Technol.

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show abstract

“…Some recently proposed schemes for computation offloading consider both energy and delay efficiency aspects [7,9,24]. In particular, the work in [7] proposes a radio and computing resource allocation framework where the computational loads of fog and cloud servers are determined and the trade-off between power consumption and service delay is investigated.…”

Section: A Related Workmentioning

confidence: 99%

“…In particular, the work in [7] proposes a radio and computing resource allocation framework where the computational loads of fog and cloud servers are determined and the trade-off between power consumption and service delay is investigated. Additionally, the authors of [24] jointly optimize the transmit power and offloading probability for minimization of the average weighted energy, delay, and payment cost. In [9], the authors study fair computation offloading design minimizing the maximum WEDC of all users in a hierarchical fog-cloud system.…”

Section: A Related Workmentioning

confidence: 99%

Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems

Nguyen

et al. 2020

IEEE Trans. Wireless Commun.

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Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This joint optimization problem is studied in the current paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where DC is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where DC is performed at both the mobile users and the fog server. We propose three efficient algorithms to overcome the strong coupling between the offloading decisions and the resource allocation. We show that the proposed optimal algorithm for DC at only the mobile users can reduce the WEDC by up to 65% compared to computation offloading strategies that do not leverage DC or use sub-optimal optimization approaches. Besides, the proposed algorithms for additional DC at the fog server can further reduce the WEDC.

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“…A key goal is to identify abnormalities at the earliest possible stage. The data processing for such IoT applications can be made more cost‐effective using Fog‐based computational model (Battula, Garg, Naha, Thulasiraman, & Thulasiram, ) (Liu, Chang, Guo, Mao, & Ristaniemi, ).…”

Section: Healthcare 40: Emerging Application Areasmentioning

confidence: 99%

Healthcare 4.0: A review of frontiers in digital health

Jayaraman

Forkan

Morshed

et al. 2019

WIREs Data Min & Knowl

124

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Healthcare 4.0 is a term that has emerged recently and derived from Industry 4.0. Today, the health care sector is more digital than in past decades; for example, spreading from x‐rays and magnetic resonance imaging to computed tomography and ultrasound scans to electric medical records. With the wide spectrum of digital technologies underpinning Healthcare 4.0 to deliver more effective and efficient health care services, in this article, we use the wisdom pyramid methodology to conduct a systematic review of current digital frontiers in Healthcare 4.0. This article is categorized under: Technologies > Computer Architectures for Data Mining Application Areas > Health Care Application Areas > Data Mining Software Tools Fundamental Concepts of Data and Knowledge > Knowledge Representation

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Multiobjective Optimization for Computation Offloading in Fog Computing

Cited by 450 publications

References 24 publications

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems

Healthcare 4.0: A review of frontiers in digital health

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