Joint Task Offloading and Resource Allocation for Energy-Constrained Mobile Edge Computing

Jiang, Hongbo; Dai, Xingxia; Xiao, Zhu; Iyengar, Arun

doi:10.1109/tmc.2022.3150432

Cited by 168 publications

(36 citation statements)

References 50 publications

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“…Kuang et al 13 put forward two iterative algorithms JPOA‐J and JPOA‐C to minimize the weighted sum of energy consumption and execution delay in a single‐user MEC system. Jiang et al 14 studied the MEC system with long‐term energy constraint for edge server, and proposed the Lyapunov‐guided algorithm to minimize the average weighted sum of task delay and energy consumption of MDs. Deng et al 15 presented a branch‐and‐bound algorithm LRBB as well as a meta‐heuristic algorithm IWPSO to minimize delay, energy consumption, and service price via jointly optimizing offloading decision and resource allocation.…”

Section: Related Workmentioning

confidence: 99%

Energy‐efficient reliability‐aware offloading for delay‐sensitive tasks in collaborative edge computing

Li,

Yu,

Fan

et al. 2024

Concurrency and Computation

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SummaryAs a burgeoning paradigm, collaborative mobile edge computing (C‐MEC) can cater to growing computation demand of mobile devices (MDs). However, there are great challenges for joint task offloading and resource allocation. In addition, failures on both MDs and edge servers greatly affect reliable task execution. This paper investigates the joint optimization problem of offloading decision, power allocation, and computation resource allocation in the multi‐user C‐MEC system, where nondivisible tasks may be executed locally, offloaded to nearby collaborative devices, or processed by the edge server. We aim at minimizing the total energy consumption of MDs while satisfying the reliability and delay constraints, and the replication technique is adopted to enhance task execution reliability. To tackle this problem, we first transform it into a bi‐level optimization problem, and then propose an iterative algorithm named JOC. Specifically, the offloading decision is constructed in the upper level on the basis of ant colony system (ACS), and the allocation of working power, transmission power, and computation resources is optimized in the lower level by using the monotonic optimization approach. Simulation experimental results reveal that the proposed algorithm saves more energy and achieves a higher task success rate in comparison with baseline schemes.

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Section: Related Workmentioning

confidence: 99%

Energy‐efficient reliability‐aware offloading for delay‐sensitive tasks in collaborative edge computing

Li,

Yu,

Fan

et al. 2024

Concurrency and Computation

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show abstract

“…The edge layer facilitates terminal device downward access and retail chains and computes information uploaded by terminals. Join the cloud and send the processed information there [ 18 ]. Because this layer is closer to the user, data transfer to the edge layer is better suited for real-time data assessment and digital signal processing, which makes it more effective and safer than cloud computing.…”

Section: Evaluation Of Internet Fraud Governance Capability Using Edg...mentioning

confidence: 99%

Criminal law regulation of cyber fraud crimes—from the perspective of citizens’ personal information protection in the era of edge computing

Dong

2023

J Cloud Comp

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Currently, cloud computing provides users all over the globe with Information and Communication Technology facilities that are utility-oriented. This technology is trying to drive the development of data center design by designing and building them as networks of cloud machines, enabling users to access and run the application from any part of the globe. Cloud computing provides considerable benefits to organizations by providing rapid and adaptable ICT software and hardware systems, allowing them to concentrate on creating innovative business values for the facilities they provide. The right to privacy of big data has acquired new definitions with the continued advancement of cloud computing, and the techniques available to protect citizens’ personal information under administrative law have managed to grow in a multitude. Because of the foregoing, internet fraud is a new type of crime that has emerged over time and is based on network technology. This paper analyzed and studied China’s internet fraud governance capabilities, and made a comprehensive evaluation of them using cloud computing technology and the Analytic Hierarchy Process (AHP). This paper discussed personal information security and the improvement of criminal responsibility from the perspective of citizens’ information security and designed and analyzed cases. In addition, this paper also analyzed and studied the ability of network fraud governance in the era of cloud computing. It also carried out a comprehensive evaluation and used the fuzzy comprehensive evaluation method to carry out the evaluation. A questionnaire survey was used to survey 100 residents in district X of city Z and district Y of the suburban area. Among the 100 people, almost all of them received scam calls or text messages, accounting for 99%, of which 8 were scammed. Among the people, more than 59.00% of the people expressed dissatisfaction with the government’s Internet fraud satisfaction survey. Therefore, in the process of combating Internet fraud, the government still needs to step up its efforts.

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“…where κ is the energy coefficient, which depends on the chip architecture [35][36][37]. In this article, according to the work in [38], we set κ = 10 −25 .…”

Section: Processing At Iot Devicementioning

confidence: 99%

Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network

Chen

Liu

2022

Sensors

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Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue may arise when the raw data is migrated to other MEC servers or the central cloud server. Since federated learning has the characteristics of protecting the privacy and improving training performance, it is introduced to solve the issue. In this article, we formulate the joint optimization problem of task offloading and resource allocation to minimize the energy consumption of all Internet of Things (IoT) devices subject to delay threshold and limited resources. A two-timescale federated deep reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG) framework (FL-DDPG) is proposed. Simulation results show that the proposed algorithm can greatly reduce the energy consumption of all IoT devices.

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Joint Task Offloading and Resource Allocation for Energy-Constrained Mobile Edge Computing

Cited by 168 publications

References 50 publications

Energy‐efficient reliability‐aware offloading for delay‐sensitive tasks in collaborative edge computing

Energy‐efficient reliability‐aware offloading for delay‐sensitive tasks in collaborative edge computing

Criminal law regulation of cyber fraud crimes—from the perspective of citizens’ personal information protection in the era of edge computing

Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network

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