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AirEdge: A Dependency-Aware Multi-Task Orchestration in Federated Aerial Computing.

Awada, U., Zhang, J., Chen, S. and Li, S., 2022. AirEdge: A Dependency-Aware Multi-Task Orchestration in Federated Aerial Computing. IEEE Transactions on Vehicular Technology, 71 (1), 805-819.

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airedge_final.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1109/TVT.2021.3127011


Emerging edge computing (EC) systems are currently exploiting attaching portable edge devices on drones for data processing close to the sources, to achieve high performance, fast response times and real-time insights. To this end, existing EC research has proposed several multiple drone-based edge deployments for various purposes, such as data caching, task offloading, real-time video analytics, and computer vision. However, none of them consider the ability of seamlessly integrating edge resources running across multiple drones in a single pool, to holistically manage and control these resources as well as to eliminate vendor lock-in situations. This paper presents an intelligent resource scheduling solution for a federated aerial EC system, called AirEdge, which jointly considers task dependencies, heterogeneous resource demand and drones’ flight time. We propose a multi-task execution time estimation and a dispatching policy, to select the closest drone deployment having congruent flight time and resource availability to execute ready tasks at any given time. For the utilization of the drones’ attached edge resources, we propose a variant bin-packing optimization approach through gangscheduling of multi-dependent tasks that co-locates tasks tightly on nodes to fully utilize available resources. Experiments on realworld data-trace from Alibaba cluster trace with information on task dependencies (about 12,207,703 dependencies) and resource demands show the effectiveness, fast executions, and resource efficiency of our approach

Item Type:Article
Additional Information:Funding Agency: Innovative Talent of Colleges and the University of Henan Province (Grant Number: 18HASTIT021) 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61571401 and 61901416)
Uncontrolled Keywords:Edge computing Aerial computing, Dependency-aware; Application container; Execution time; Resource efficiency
Group:Faculty of Science & Technology
ID Code:36218
Deposited By: Symplectic RT2
Deposited On:08 Nov 2021 13:56
Last Modified:14 Mar 2022 14:30


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