Online energy-aware task dispatching with QoS guarantee in edge computing

Hao YUAN; Deke GUO; Guoming TANG; Lailong LUO

doi:10.11959/j.issn.2096-3750.2021.00230

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Online energy-aware task dispatching with QoS guarantee in edge computing

Topic: Edge Intelligence and Fog Computing in IoT | 更新时间：2024-06-05

- Online energy-aware task dispatching with QoS guarantee in edge computing
- Chinese Journal on Internet of Things Vol. 5, Issue 2, Pages: 71-77(2021)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2021.00230
  CLC： TP3
- Published：30 June 2021，
  
  Published Online：2021-06，
- 稿件说明：
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HAO YUAN, DEKE GUO, GUOMING TANG, et al. Online energy-aware task dispatching with QoS guarantee in edge computing. [J]. Chinese journal on internet of things, 2021, 5(2): 71-77.
DOI：

HAO YUAN, DEKE GUO, GUOMING TANG, et al. Online energy-aware task dispatching with QoS guarantee in edge computing. [J]. Chinese journal on internet of things, 2021, 5(2): 71-77. DOI： 10.11959/j.issn.2096-3750.2021.00230.

摘要

通过在网络边缘布置大量的边缘服务器，边缘计算能够为用户提供低时延、高带宽的服务。然而，大量布置边缘服务器也带来了高能耗等问题。当用户将任务从终端设备分派到不同的边缘服务器时，边缘服务器的异构性，会产生不同的能耗和时延。因此，如何在众多边缘服务器中选择一个最优的服务器进行任务分派，使得能耗和时延都比较低是具有挑战性的。提出了一种基于在线学习的具有服务质量（QoS

quality of service）保证的能耗感知任务分派方法，它可以通过与环境进行交互来获取实时的信息，从而在分派任务时，在保证QoS可接受的基础上，总体能耗最低。实验结果表明，与其他方法相比，提出的方法可以高效地将任务分派到最优的边缘服务器上，显著降低边缘计算网络的整体能耗。

Abstract

Edge computing can provide users with low-latency and high-bandwidth services by deploying many edge servers at the network edge.However

a large number of deployments also bring problems of high energy consumption.When dispatching tasks from end devices to different edge servers

different energy consumption and delays will occur due to the edge servers’ heterogeneity.Therefore

it is a challenge to select an optimal server among many edge servers for task dispatching so that energy consumption and delay are relatively low.An energy-aware task dispatching method with quality of service (QoS) guarantee based on online learning was proposed.It can obtain real-time information by interacting with the environment to ensure energy consumption was minimal while the QoS was acceptable when dispatching tasks.Experiments show that the proposed method can dispatch tasks efficiently to the optimal server compared with other methods

thereby reducing the edge computing network’s overall energy consumption significantly.

关键词

边缘计算任务分派QoS能耗感知在线学习

Keywords

edge computingtask dispatchingQoSenergy-awareonline learning

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