Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

doi:10.1109/TMC.2021.3115262

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Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

2023-04-01

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DOI：10.1109/TMC.2021.3115262

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Abstract

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The cloud-assisted mobile edge computing system is a critical architecture to process computation-intensive and delay-sensitive mobile applications in close proximity to mobile users with high resource efficiency. Due to the heterogenous dynamics of task arrivals at edge nodes and the distributed nature of the system, the workloads of edge nodes are prone to be unbalanced, which can cause high task response time and resource cost. This paper solves the dynamic task scheduling problem in cloud-assisted mobile edge computing (including both peer task scheduling among edge nodes and cross-layer task scheduling from edge nodes to the cloud), aiming at minimizing average task response time within resource budget limit. To overcome the challenges of task arrival dynamics, edge node heterogeneity, and computation-communication delay tradeoff, we propose a Water-filling Based Dynamic Task Scheduling (WiDaS) algorithm. WiDaS dynamically tunes the usage of cloud resources based on the Lyapunov optimization method and efficiently schedules mobile tasks among edge nodes (and the cloud) by exploiting the idea of water filling. Extensive simulations are conducted to evaluate WiDaS under a trace-driven traffic pattern and two mathematic traffic patterns. The results demonstrate that WiDaS shows two-fold benefits of efficiency and effectiveness. In terms of efficiency, WiDaS can achieve the approximate results with the KKT-based algorithm while reducing the computation complexity from exponential order to polynomial order. In terms of effectiveness, WiDaS can reduce the average task response time by up to 64.4% and 47.2% over the Fair-ratio and the Edge-first algorithm.

Keywords:

Task analysis

Peer-to-peer computing

Dynamic scheduling

Processor scheduling

Delays

Heuristic algorithms

Mobile handsets

Mobile edge computing

cloud

task scheduling

workload scheduling