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

Jiang, Wei; Feng, Daquan; Sun, Yao; Feng, Gang; Wang, Zhenzhong; Xia, Xiang-Gen

doi:10.1109/tsc.2022.3190276

Cited by 53 publications

(7 citation statements)

References 40 publications

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“…To validate the effectiveness of the proposed P-DQN method, the following four baseline methods are listed as follows: Random offloading (RO): Randomly offloading tasks locally, to LEO satellites and to the cloud server [ 52 ]. Average resource allocation (ARA): Computing resources on both LEO satellites and the cloud server are evenly shared among offloaded tasks [ 40 ].…”

Section: Simulations and Results Analysismentioning

confidence: 99%

“… Average resource allocation (ARA): Computing resources on both LEO satellites and the cloud server are evenly shared among offloaded tasks [ 40 ]. DQN offloading (DQNO): The DQN is only used for the task offloading [ 52 ]. Deep deterministic policy gradient (DDPG) resource allocation (DDPGRA): The DDPG is used to allocate both computing and power resources for already offloaded tasks.…”

Section: Simulations and Results Analysismentioning

confidence: 99%

“…Random offloading (RO): Randomly offloading tasks locally, to LEO satellites and to the cloud server [ 52 ].…”

Section: Simulations and Results Analysismentioning

confidence: 99%

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Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks

Yang,

Fang,

Gao

et al. 2023

Sensors

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Traditional low earth orbit (LEO) satellite networks are typically independent of terrestrial networks, which develop relatively slowly due to the on-board capacity limitation. By integrating emerging mobile edge computing (MEC) with LEO satellite networks to form the business-oriented “end-edge-cloud” multi-level computing architecture, some computing-sensitive tasks can be offloaded by ground terminals to satellites, thereby satisfying more tasks in the network. How to make computation offloading and resource allocation decisions in LEO satellite edge networks, nevertheless, indeed poses challenges in tracking network dynamics and handling sophisticated actions. For the discrete-continuous hybrid action space and time-varying networks, this work aims to use the parameterized deep Q-network (P-DQN) for the joint computation offloading and resource allocation. First, the characteristics of time-varying channels are modeled, and then both communication and computation models under three different offloading decisions are constructed. Second, the constraints on task offloading decisions, on remaining available computing resources, and on the power control of LEO satellites as well as the cloud server are formulated, followed by the maximization problem of satisfied task number over the long run. Third, using the parameterized action Markov decision process (PAMDP) and P-DQN, the joint computing offloading, resource allocation, and power control are made in real time, to accommodate dynamics in LEO satellite edge networks and dispose of the discrete-continuous hybrid action space. Simulation results show that the proposed P-DQN method could approach the optimal control, and outperforms other reinforcement learning (RL) methods for merely either discrete or continuous action space, in terms of the long-term rate of satisfied tasks.

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Section: Simulations and Results Analysismentioning

confidence: 99%

Section: Simulations and Results Analysismentioning

confidence: 99%

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Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks

Yang,

Fang,

Gao

et al. 2023

Sensors

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“…Computing offloading via V2V communication and V2I communication can make full use of the ubiquitous computing resources of the system and improve the performance of mobile edge computing [26]. However, due to the mobility of vehicles and dynamic wireless channel conditions, the formulation of computing offloading strategies has highdimensional and time-varying characteristics.…”

Section: Related Workmentioning

confidence: 99%

Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network

Liu,

Zheng,

Zhang

et al. 2024

Sensors

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In Vehicular Edge Computing Network (VECN) scenarios, the mobility of vehicles causes the uncertainty of channel state information, which makes it difficult to guarantee the Quality of Service (QoS) in the process of computation offloading and the resource allocation of a Vehicular Edge Computing Server (VECS). A multi-user computation offloading and resource allocation optimization model and a computation offloading and resource allocation algorithm based on the Deep Deterministic Policy Gradient (DDPG) are proposed to address this problem. Firstly, the problem is modeled as a Mixed Integer Nonlinear Programming (MINLP) problem according to the optimization objective of minimizing the total system delay. Then, in response to the large state space and the coexistence of discrete and continuous variables in the action space, a reinforcement learning algorithm based on DDPG is proposed. Finally, the proposed method is used to solve the problem and compared with the other three benchmark schemes. Compared with the baseline algorithms, the proposed scheme can effectively select the task offloading mode and reasonably allocate VECS computing resources, ensure the QoS of task execution, and have a certain stability and scalability. Simulation results show that the total completion time of the proposed scheme can be reduced by 24–29% compared with the existing state-of-the-art techniques.

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“…As the number of smart devices and the demand for real-time applications increase, MEC architectures alone cannot fully meet the stringent latency requirements, especially in scenarios with high device densities and unpredictable dynamic environments. This realization has led to the emergence of D2D computing, which extends the concept of edge computing by enabling direct communication between nearby devices without routing traffic through a base station or central server [5,7,11,[24][25][26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Task Offloading with Uncertain Processing Times in Device-to-Device Edge Networks

Shu,

Luo,

Liu

2024

Electronics

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D2D edge computing is a promising solution to address the conflict between limited network capacity and increasing application demands, where mobile devices can offload their tasks to other peer devices/servers for better performance. Task offloading is critical to the performance of D2D edge computing. Most existing works on task offloading assume the task processing time is known or can be accurately estimated. However, the processing time is often uncertain until it is finished. Moreover, the same task can have largely different execution times under different scenarios, which leads to inaccurate offloading decisions and degraded performance. To address this problem, we propose a game-based probabilistic task offloading scheme with an uncertain processing time in D2D edge networks. First, we characterize the uncertainty of the task processing time using a probabilistic model. Second, we incorporate the proposed probabilistic model into an offloading decision game. We also analyze the structural properties of the game and prove that it can reach a Nash equilibrium. We evaluate the proposed work using real-world applications and datasets. The experimental results show that the proposed probabilistic model can accurately characterize the uncertainty of completion time, and the offloading algorithm can effectively improve the overall task completion rate in D2D networks.

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

Cited by 53 publications

References 40 publications

Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks

Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks

Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network

Probabilistic Task Offloading with Uncertain Processing Times in Device-to-Device Edge Networks

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