Offloading Demand Prediction-Driven Latency-Aware Resource Reservation in Edge Networks

Zhang, Jianhui; Wang, Jiacheng; Yuan, Zhongyin; Zhang, Wanqing; Liu, Liming

doi:10.1109/jiot.2023.3263188

Cited by 3 publications

(1 citation statement)

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“…However, given that task forecasting is closely related to resource allocation, the reservation of compute resources should be fully considered when making task forecasting. The authors in [33] proposed a delay-aware resource reservation strategy based on spatio-temporal and spatial-task decentralization demand forecasting and proposed a regional-edge server resource reservation algorithm based on the prediction model to minimize the latency of terminal tasks. The authors in [34] updated the upload strategy based on feedback from the environment and optimized resource allocation with the help of Salp's artificial bee colony algorithm to improve the performance of IoT devices.…”

Section: Edge-computing Task Offloading Based On Resource Predictionmentioning

confidence: 99%

MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing

Wang,

Huang,

Wei

et al. 2024

Future Internet

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With the increasing popularity of the Internet of Things (IoT), the proliferation of computation-intensive and timing-dependent applications has brought serious load pressure on terrestrial networks. In order to solve the problem of computing resource conflict and long response delay caused by concurrent application service applications from multiple users, this paper proposes an improved edge computing timing-dependent, task-offloading scheme based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that aims to shorten the offloading delay and improve the resource utilization rate by means of resource prediction and collaboration among multiple agents to shorten the offloading delay and improve the resource utilization. First, to coordinate the global computing resource, the gated recurrent unit is utilized, which predicts the next computing resource requirements of the timing-dependent tasks according to historical information. Second, the predicted information, the historical offloading decisions and the current state are used as inputs, and the training process of the reinforcement learning algorithm is improved to propose a task-offloading algorithm based on MADDPG. The simulation results show that the algorithm reduces the response latency by 6.7% and improves the resource utilization by 30.6% compared with the suboptimal benchmark algorithm, and it reduces nearly 500 training rounds during the learning process, which effectively improves the timeliness of the offloading strategy.

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Section: Edge-computing Task Offloading Based On Resource Predictionmentioning

confidence: 99%