Adaptive Service Offloading for Revenue Maximization in Mobile Edge Computing With Delay-Constraint

Samanta, Amit; Chang, Zheng

doi:10.1109/jiot.2019.2892398

Cited by 88 publications

(41 citation statements)

References 32 publications

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“…The authors in [7] proposed a computation offloading algorithm by jointly considering the offloading decision, computation resources, and the transmit power for computation offloading. The authors in [16] proposed an iterative computation offloading algorithm to improve the resource utilization efficiency in wireless networks.…”

Section: A Computation Offloading In Wireless Networkmentioning

confidence: 99%

Edge Intelligence for Energy-Efficient Computation Offloading and Resource Allocation in 5G Beyond

Dai

Zhang

Maharjan

et al. 2020

IEEE Trans. Veh. Technol.

160

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5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous capabilities of the end devices, edge servers, and the cloud and thus has the potential to enable computation-intensive and delay-sensitive applications via computation offloading. However, in multi user wireless networks, diverse application requirements and the possibility of various radio access modes for communication among devices make it challenging to design an optimal computation offloading scheme. In addition, having access to complete network information that includes variables such as wireless channel state, and available bandwidth and computation resources, is a major issue. Deep Reinforcement Learning (DRL) is an emerging technique to address such an issue with limited and less accurate network information. In this paper, we utilize DRL to design an optimal computation offloading and resource allocation strategy for minimizing system energy consumption. We first present a multiuser end-edge-cloud orchestrated network where all devices and base stations have computation capabilities. Then, we formulate the joint computation offloading and resource allocation problem as a Markov Decision Process (MDP) and propose a new DRL algorithm to minimize system energy consumption. Numerical results based on a real-world dataset demonstrate that the proposed DRLbased algorithm significantly outperforms the benchmark policies in terms of system energy consumption. Extensive simulations show that learning rate, discount factor, and number of devices have considerable influence on the performance of the proposed algorithm.

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Section: A Computation Offloading In Wireless Networkmentioning

confidence: 99%

Edge Intelligence for Energy-Efficient Computation Offloading and Resource Allocation in 5G Beyond

Dai

Zhang

Maharjan

et al. 2020

IEEE Trans. Veh. Technol.

160

View full text Add to dashboard Cite

show abstract

“…With the development of caching technology, in [24] Yu et al designed a collaborative offloading scheme and cached the popular computation results that was likely to be reused by other mobile users using caching enhancements to minimize the task latency at the mobile terminal side. In [25] Samanta et al considered both the delaytolerant and delay-constraint services in order to achieve the optimized service latency and revenue.…”

Section: A Mec System Without Uav Assistancementioning

confidence: 99%

Task number maximization offloading strategy seamlessly adapted to UAV scenario

Tang

Yang

et al. 2020

Computer Communications

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…Sardellitti et al 13 considered a multi-user multi-cell computation offloading system that jointly allocated radio and computing resources to minimize mobile energy consumption under offload delay constraints. Besides, in literature, [14][15][16] the profits of users were modeled using various metrics. However, the benefits of clouds were not considered by above the research works.…”

Section: Related Workmentioning

confidence: 99%

A multi-user multi-operator computing pricing method for Internet of things based on bi-level optimization

Jiang

Wang

et al. 2020

International Journal of Distributed Sensor Networks

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The Internet of mobile things is a promising paradigm that generates, stores, and processes amount of real-time data to render rich services for mobile users. Along with the increase of mobile devices in the field of Internet of things, more and more intelligent applications, such as face recognition and virtual reality, have emerged. These applications typically consume large amounts of computing and energy resources. However, due to the physical size limitations of Internet of things terminals, their computing capacity and power are limited, where users’ needs for application processing delay and power consumption cannot be met. Therefore, the concept of edge cloud computing has been proposed, which enhances the computing capacity of Internet of things terminals by offloading user tasks to edge servers for computation. When there are multiple operators, it is important to understand how users choose an operator to perform computation and how operators can reasonably price the computing capacity to meet their own interests. Therefore, we study the computation pricing and user decision-making problems of Internet of things under multi-user and multi-operator scenarios. The problem is divided into three phases and modeled as a two-level optimization problem. While an operator’s goal is to minimize the loss of his interests, the user’s goal is to minimize the computation cost (energy consumption and price). First, since the lower-level user decision-making problem is an integer linear programming problem, we transform it into an equivalent continuous linear programming problem by relaxation. Second, we transform the bi-level optimization problem into an equivalent single-level optimization problem by substituting the lower problem’s Karush–Kuhn–Tucker conditions into an upper problem. Finally, we use a spatial branch and bound algorithm to solve the problem. Experimental results show that the proposed algorithm can effectively maintain the benefits of both operators and users in the field of Internet of things.

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Adaptive Service Offloading for Revenue Maximization in Mobile Edge Computing With Delay-Constraint

Abstract: This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details.

Cited by 88 publications

References 32 publications

Edge Intelligence for Energy-Efficient Computation Offloading and Resource Allocation in 5G Beyond

Edge Intelligence for Energy-Efficient Computation Offloading and Resource Allocation in 5G Beyond

Task number maximization offloading strategy seamlessly adapted to UAV scenario

A multi-user multi-operator computing pricing method for Internet of things based on bi-level optimization

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