Multitask Multiobjective Deep Reinforcement Learning-Based Computation Offloading Method for Industrial Internet of Things

Cai, Jun; Fu, Hongtian; Liu, Yan

doi:10.1109/jiot.2022.3209987

Cited by 24 publications

(2 citation statements)

References 33 publications

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“…Throughout the experiments, we assume that UEs are randomly distributed within an area of 350m × 350m. Additionally, the reference distance, channel bandwidths between the UE and the ENs, and the transmit power from UEs to ENs are 50 m, 6 MHz, and 25 dBm, respectively [31].…”

Section: A Simulation Settingsmentioning

confidence: 99%

Computation Offloading for Tasks With Bound Constraints in Multiaccess Edge Computing

Wang

et al. 2023

IEEE Internet Things J.

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Multi-Access Edge Computing (MEC) provides task offloading services to facilitate the integration of idle resources with the network and bring cloud services closer to the end user. By selecting suitable servers and properly managing resources, task offloading can reduce task completion latency while maintaining the Quality of Service (QoS). Prior research, however, has primarily focused on tasks with strict time constraints, ignoring the possibility that tasks with soft constraints may exceed the bound limits and failing to analyze this complex task constraint issue. Furthermore, considering additional constraint features makes convergent optimization algorithms challenging when dealing with such complex and high-dimensional situations. In this paper, we propose a new computational offloading decision framework by minimizing the long-term payment of computational tasks with mixed bound constraints. In addition, redundant experiences are gotten rid of before the training of the algorithm. The most advantageous transitions in the experience pool are used for training in order to improve the learning efficiency and convergence speed of the algorithm as well as increase the accuracy of offloading decisions. The findings of our experiments indicate that the method we have presented is capable of achieving fast convergence rates while also reducing sample redundancy.

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Section: A Simulation Settingsmentioning

confidence: 99%

Computation Offloading for Tasks With Bound Constraints in Multiaccess Edge Computing

Wang

et al. 2023

IEEE Internet Things J.

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“…Besides the above works, some works also define special optimization objectives for task-resource scheduling. For example, [28] formulates a multi-objective problem to minimize latency and energy consumption simultaneously, and employs MADRL to make an optimal offloading decision for cloudedge-end computing. [29] proposes an end-to-end DRL algorithm to maximize the number of tasks before their respective deadlines and minimize energy consumption simultaneously.…”

Section: Cost Minimizationmentioning

confidence: 99%

Digital Twin-Driven Collaborative Scheduling for Heterogeneous Task and Edge-End Resource via Multi-Agent Deep Reinforcement Learning

Xu,

Tang,

et al. 2023

IEEE J. Select. Areas Commun.

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With the interdisciplinary advances of mobile communication and edge computing, massive heterogeneous tasks are accessing wireless networks and competing for the edgeend computing and communication resources. Digital twin (DT), which establishes the digital models of physical objects for simulation, analysis and optimization, provides a promising method for network scheduling and management. This paper proposes a DT-driven edge-end collaborative scheduling algorithm for heterogeneous tasks and heterogeneous computing/communication resources. Specifically, multiple end devices (EDs) cooperate with each other to accomplish a complex job, where each ED can offload individual task to multiple edge servers (ESs) for parallel computing. By fully considering deadline requirements of heterogeneous tasks, maximum computing capabilities of ESs and EDs, computing resource estimation deviations of DT, maximum transmit powers of EDs and tolerable peak interference powers to coexisting EDs, we formulate a job completion time minimization problem to jointly optimize the edge-end task division, transmit power control, computing resource type matching and allocation. To solve this non-convex problem, we first reformulate it by multi-agent Markov decision process, where a compound reward leveraging latency reward and deadline reward according to the task criticality is designed. Then, we propose a multi-agent deep reinforcement learning-based scheduling algorithm, where Actor-Critic framework with estimation and target networks is designed for policy and value iterations. Meanwhile, a step-bystep -greedy algorithm is proposed to balance exploration and exploitation, avoiding local optimal trap. Through offline centralized training by DT and online distributed execution by EDs, we realize edge-end collaborative computing for heterogeneous tasks. Experimental results demonstrate that, comparing with typical benchmark algorithms, the proposed algorithm converges with the highest reward and achieves the smallest job completion time, where the deadlines of heterogeneous tasks can be well satisfied respectively.

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Reliable Function Computation Offloading in Cloud-Edge Collaborative Network

Li,

Xie,

et al. 2024

Lecture Notes in Computer Science

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Multitask Multiobjective Deep Reinforcement Learning-Based Computation Offloading Method for Industrial Internet of Things

Cited by 24 publications

References 33 publications

Computation Offloading for Tasks With Bound Constraints in Multiaccess Edge Computing

Computation Offloading for Tasks With Bound Constraints in Multiaccess Edge Computing

Digital Twin-Driven Collaborative Scheduling for Heterogeneous Task and Edge-End Resource via Multi-Agent Deep Reinforcement Learning

Reliable Function Computation Offloading in Cloud-Edge Collaborative Network

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