Joint Task Offloading, D2D Pairing, and Resource Allocation in Device-Enhanced MEC: A Potential Game Approach

Fang, Tao; Yuan, Feng; Ao, Liang; Chen, Jiaxin

doi:10.1109/jiot.2021.3097754

Cited by 87 publications

(45 citation statements)

References 41 publications

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“…With ETSI's reference architecture and the mature concept of MEC, 5G MEC is realized by a 5G core network (5GC), edge computing platform and UEs to meet the requirements of billing, legal interception, mobility management and quality of service (QoS) in edge scenarios [7]. Therefore, as a 5G native function, MEC will help perform application localization, content distribution and computing marginalization, which is highly consistent with the concept of expanding vertical industry and service-oriented networks in the future, and MEC has therefore become the critical technology and foundation for the development of 5G/B5G, the industrial Internet of Things (IoT) and computing networks [8], [9]. In addition, in contrast to traditional central cloud computing technology, MEC integrates telecommunications and Internet Technology (IT) services, provides cloud computing capability for UEs and other network devices at the edge of the wireless access network, and can reduce the latency of computing, storage, processing and access of UEs [10].…”

Section: Introductionmentioning

confidence: 94%

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SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

Song¹,

Sun²,

Yu³

et al. 2022

Preprint

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In recent years, edge computing, as an important pillar for future networks, has been developed rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run computing-intensive applications, accelerate computing speed and save energy. An efficient and feasible task offloading scheme can not only greatly improve the quality of experience (QoE) but also provide strong support and assistance for 5G/B5G networks, the industrial Internet of Things (IIoT), computing networks and so on. To achieve these goals, this paper proposes an adaptive edge task offloading scheme assisted by service deployment (SD-AETO) focusing on the optimization of the energy utilization ratio (EUR) and the processing latency. In the pre-implementation stage of the SD-AETO scheme, a service deployment scheme is invoked to assist with task offloading considering each service's popularity. The optimal service deployment scheme is obtained by using the approximate deployment graph (AD-graph). Furthermore, a task scheduling and queue offloading design procedure is proposed to complete the SD-AETO scheme based on the task priority. The task priority is generated by the corresponding service popularity and task offloading direction. Finally, we analyze our SD-AETO scheme and compare it with related approaches, and the results show that our scheme has a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network.

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Section: Introductionmentioning

confidence: 94%

“…At the time at which a new service s new with service probability P (s new ) enters the network, we update the set of services S to S new , as shown in Equation (8).…”

Section: Service's Popularity Designmentioning

confidence: 99%

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

Song¹,

Sun²,

Yu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Let K = {1, 2, ..., K} denote the set of K orthogonal sub-channels, and the bandwidth of each sub-channel is B (in Hz). Since we consider a kind of data-partition applications (e.g., the face detection and recognition application) which usually have similar input data sizes, it is reasonable to assume that each RD is assigned to one sub-channel [12], [17], [36]. In addition, to ensure the orthogonality of uplink transmissions among RDs in the same cell, we assume that RDs in the same cell are allocated with different sub-channels, and only the suffered interferences from other cells and D2D links are considered [18].…”

Section: Communication Modelmentioning

confidence: 99%

“…By taking the advantages of the proximity, spatial reuse, traffic offloading gain, and better coverage of D2D communications, D2D-assisted MEC offloading architectures can help mobile users enjoy ubiquitous edge computing anywhere and anytime. However, most current optimization or game based methods [10], [11], [12] require the prior information of environment statistics and cannot be efficiently applied to dynamic D2D-assisted MEC offloading systems.…”

Section: Introductionmentioning

confidence: 99%

Joint Computation Offloading and Resource Allocation for D2D-Assisted Mobile Edge Computing

Jiang

Feng

Sun

et al. 2022

IEEE Trans. Serv. Comput.

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Computation offloading via device-to-device communications can improve the performance of mobile edge computing by exploiting the computing resources of user devices. However, most proposed optimization-based computation offloading schemes lack self-adaptive abilities in dynamic environments due to time-varying wireless environment, continuous-discrete mixed actions, and coordination among devices. The conventional reinforcement learning based approaches are not effective for solving an optimal sequential decision problem with continuous-discrete mixed actions. In this paper, we propose a hierarchical deep reinforcement learning (HDRL) framework to solve the joint computation offloading and resource allocation problem. The proposed HDRL framework has a hierarchical actor-critic architecture with a meta critic, multiple basic critics and actors. Specifically, a combination of deep Q-network (DQN) and deep deterministic policy gradient (DDPG) is exploited to cope with the continuous-discrete mixed action spaces. Furthermore, to handle the coordination among devices, the meta critic acts as a DQN to output the joint discrete action of all devices and each basic critic acts as the critic part of DDPG to evaluate the output of the corresponding actor. Simulation results show that the proposed HDRL algorithm can significantly reduce the task computation latency compared with baseline offloading schemes.

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“…If the UE gets more payoff, its task offloading decision will be changed, otherwise, its task offloading decision will be unchanged. The payoff is bounded due to the limited strategy space, when no UE can gain more payoff by changing its task offloading decision, the algorithm will converge and obtain the local or global optimal solution of P [34]. In this case, the game reaches the NE.…”

Section: Joint Offloading Decision and Resource Allocationmentioning

confidence: 99%

Joint Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Network

Tong

Wang

et al. 2022

Symmetry

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With the development of satellite-terrestrial network (STN), mobile edge computing (MEC) servers are deployed at low orbit earth (LEO) satellites to provide computing services for user devices (UEs) in areas without terrestrial network coverage. There is symmetry between satellite networks and terrestrial networks, but there is asymmetry between their resources. Computing resources of satellites’ MEC servers may not be enough. The satellite-terrestrial cooperation is promising, where a satellite migrates tasks to a base station (BS) in an adjacent area, thus utilizing computing resources of the BS’s MEC server. Although there are some studies on computation offloading in STN, few studies consider a satellite as both a relay and a computing unit to assist UEs in computing tasks. This paper proposes a joint offloading decision and resource allocation scheme in MEC-enabled STN, which minimizes the completion delay of all UEs’ indivisible tasks. Firstly, the optimization problem is formulated and decomposed. Then, the proposed scheme based on potential game and the Lagrange multiplier method makes UEs’ task offloading decisions and allocates the satellite’s and the BS’s computing resources, thus obtaining the optimal solution through continuous iterations. Finally, the simulation results validate that the proposed scheme can obtain better gain than other baseline schemes.

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Joint Task Offloading, D2D Pairing, and Resource Allocation in Device-Enhanced MEC: A Potential Game Approach

Cited by 87 publications

References 41 publications

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

Joint Computation Offloading and Resource Allocation for D2D-Assisted Mobile Edge Computing

Joint Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Network

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