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AirEdge: A dependency-aware multi-task orchestration in federated aerial computing

AirEdge: A dependency-aware multi-task orchestration in federated aerial computing
AirEdge: A dependency-aware multi-task orchestration in federated aerial computing

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 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 keeping edge resources running across multiple drones in a single pool, to holistically manage and control these resources from a single federated plane as well as to eliminate vendor lock-in situations. This research 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. The selected drone is then deployed autonomously to the needed location. For the utilization of the drones' attached edge resources or cluster, we propose a variant bin-packing optimization approach through gang-scheduling of multi-dependent tasks that co-locates tasks tightly on nodes to fully utilize available resources. Extensive experiments on real-world 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.

Aerial computing, Application container, Cloud computing, Dependency-aware, Dispatching, Drones, Edge computing, Execution time, Real-time systems, Resource efficiency, Resource management, Task analysis
0018-9545
805-819
Awada, Uchechukwu
ef034620-7288-4b31-88f6-134dde39c7c9
Zhang, Jiankang
bd2e40d3-f72c-4e42-8aac-51d4065ce772
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Li, Shuangzhi
ef205f54-53f2-4d49-b1c7-96c565ed9c85
Awada, Uchechukwu
ef034620-7288-4b31-88f6-134dde39c7c9
Zhang, Jiankang
bd2e40d3-f72c-4e42-8aac-51d4065ce772
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Li, Shuangzhi
ef205f54-53f2-4d49-b1c7-96c565ed9c85

Awada, Uchechukwu, Zhang, Jiankang, Chen, Sheng and Li, Shuangzhi (2021) AirEdge: A dependency-aware multi-task orchestration in federated aerial computing. IEEE Transactions on Vehicular Technology, 71 (1), 805-819. (doi:10.1109/TVT.2021.3127011).

Record type: Article

Abstract

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 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 keeping edge resources running across multiple drones in a single pool, to holistically manage and control these resources from a single federated plane as well as to eliminate vendor lock-in situations. This research 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. The selected drone is then deployed autonomously to the needed location. For the utilization of the drones' attached edge resources or cluster, we propose a variant bin-packing optimization approach through gang-scheduling of multi-dependent tasks that co-locates tasks tightly on nodes to fully utilize available resources. Extensive experiments on real-world 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.

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More information

Accepted/In Press date: 8 November 2021
Published date: 13 November 2021
Additional Information: Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Aerial computing, Application container, Cloud computing, Dependency-aware, Dispatching, Drones, Edge computing, Execution time, Real-time systems, Resource efficiency, Resource management, Task analysis

Identifiers

Local EPrints ID: 452194
URI: http://eprints.soton.ac.uk/id/eprint/452194
ISSN: 0018-9545
PURE UUID: f1f7d009-c695-4075-a652-e659f1f15d3f

Catalogue record

Date deposited: 29 Nov 2021 17:32
Last modified: 23 Jul 2022 00:33

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Contributors

Author: Uchechukwu Awada
Author: Jiankang Zhang
Author: Sheng Chen
Author: Shuangzhi Li

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