Learn to Coordinate for Computation Offloading and Resource Allocation in Edge Computing: A Rational-Based Distributed Approach

Liu, Zhicheng; Yong, Zhao; Song, Jinduo; Qiu, Chao; Chen, Xu

doi:10.1109/tnse.2021.3136942

Cited by 12 publications

(3 citation statements)

References 56 publications

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“…The first assumes that ESs can provide resources at no cost [18][19][20][21][22][23]. The other assumes that ESs provide resources under some form of incentive mechanism [24][25][26][27][28][29][30][31][32][33]. Zhang et al [18] performed a joint optimization of channel allocation and dependent task offloading, as well as designed an algorithm based on genetic algorithms and deep deterministic policy gradients.…”

Section: Related Workmentioning

confidence: 99%

“…Based on whether the edge servers have sufficient resources, the study of incentive mechanisms can be divided into two aspects: First, when ESs lack adequate resources, the energy consumption of ESs acts as a counter incentive for users [24,25]. Second, when the ESs have sufficient resources, the energy consumption of ESs becomes an incentive for users [26][27][28][29]. Avgeris et al [24] studied the offloading problem involving multiple users and multiple edge nodes, where the energy consumption of the ESs was considered a constraining factor in finding the optimal solution.…”

Section: Related Workmentioning

confidence: 99%

“…The energy consumption of ESs serves as a positive incentive. Liu et al [26] studied resource allocation and pricing problems in an EC system, then formulated them as mixedinteger linear programming and proved the problem to be NP-hard. To solve this problem, auction-based mechanisms and linear programming-based approximation mechanisms were proposed and developed.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation

Li,

Ju,

Ren

2023

Future Internet

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The existing research on dependent task offloading and resource allocation assumes that edge servers can provide computational and communication resources free of charge. This paper proposes a two-stage resource allocation method to address this issue. In the first stage, users incentivize edge servers to provide resources. We formulate the incentive problem in this stage as a multivariate Stackelberg game, which takes into account both computational and communication resources. In addition, we also analyze the uniqueness of the Stackelberg equilibrium under information sharing conditions. Considering the privacy issues of the participants, the research is extended to scenarios without information sharing, where the multivariable game problem is modeled as a partially observable Markov decision process (POMDP). In order to obtain the optimal incentive decision in this scenario, a reinforcement learning algorithm based on the learning game is designed. In the second stage, we propose a greedy-based deep reinforcement learning algorithm that is aimed at minimizing task execution time by optimizing resource and task allocation strategies. Finally, the simulation results demonstrate that the algorithm designed for non-information sharing scenarios can effectively approximate the theoretical Stackelberg equilibrium, and its performance is found to be better than that of the other three benchmark methods. After the allocation of resources and sub-tasks by the greedy-based deep reinforcement learning algorithm, the execution delay of the dependent task is significantly lower than that in local processing.

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