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Air-to-air collaborative learning: a multi-task orchestration in federated aerial computing

Air-to-air collaborative learning: a multi-task orchestration in federated aerial computing
Air-to-air collaborative learning: a multi-task orchestration in federated aerial computing
Recent research on edge computing (EC) has proposed federated or collaborative learning technique, where machine learning models are shared among participating edge deployments, thereby benefiting from all available datasets without exchanging them. In addition, 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. Existing research lack the potential to federate edge resources and manage corresponding service entities running across multiple drones, thus resulting to sub-optimal performance. Therefore, we introduce AerialEdge, a federated learning-based orchestration framework for a federated aerial EC system. We propose a federated multi-output linear regression model to estimate multi-task resource requirements and execution time, to select the closest drone deployment having congruent resource availability and flight time to execute ready tasks at any given time. For better utilization of resources, we propose a variant bin-packing optimization approach through gang-scheduling of multi-dependent containerized tasks that co-schedules and 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 show the effectiveness, fast executions, and resource efficiency of our approach.
Edge computing, dependency-Aware, edge federation, execution time, federated learning, resource efficiency
2159-6182
671-680
Awada, Uchechukwu
ef034620-7288-4b31-88f6-134dde39c7c9
Zhang, Jiankang
c6c025b3-6576-4f9d-be95-57908e61fa88
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Li, Shuangzhi
ef205f54-53f2-4d49-b1c7-96c565ed9c85
Ardagna, Claudio Agostino
Chang, Carl K.
Daminai, Ernesto
Ranjan, Rajiv
Wang, Zhongjie
Ward, Robert
Zhang, Jia
Zhang, Wensheng
Awada, Uchechukwu
ef034620-7288-4b31-88f6-134dde39c7c9
Zhang, Jiankang
c6c025b3-6576-4f9d-be95-57908e61fa88
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Li, Shuangzhi
ef205f54-53f2-4d49-b1c7-96c565ed9c85
Ardagna, Claudio Agostino
Chang, Carl K.
Daminai, Ernesto
Ranjan, Rajiv
Wang, Zhongjie
Ward, Robert
Zhang, Jia
Zhang, Wensheng

Awada, Uchechukwu, Zhang, Jiankang, Chen, Sheng and Li, Shuangzhi (2021) Air-to-air collaborative learning: a multi-task orchestration in federated aerial computing. Ardagna, Claudio Agostino, Chang, Carl K., Daminai, Ernesto, Ranjan, Rajiv, Wang, Zhongjie, Ward, Robert, Zhang, Jia and Zhang, Wensheng (eds.) In Proceedings - 2021 IEEE 14th International Conference on Cloud Computing, CLOUD 2021. vol. 2021-September, pp. 671-680 . (doi:10.1109/CLOUD53861.2021.00086).

Record type: Conference or Workshop Item (Paper)

Abstract

Recent research on edge computing (EC) has proposed federated or collaborative learning technique, where machine learning models are shared among participating edge deployments, thereby benefiting from all available datasets without exchanging them. In addition, 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. Existing research lack the potential to federate edge resources and manage corresponding service entities running across multiple drones, thus resulting to sub-optimal performance. Therefore, we introduce AerialEdge, a federated learning-based orchestration framework for a federated aerial EC system. We propose a federated multi-output linear regression model to estimate multi-task resource requirements and execution time, to select the closest drone deployment having congruent resource availability and flight time to execute ready tasks at any given time. For better utilization of resources, we propose a variant bin-packing optimization approach through gang-scheduling of multi-dependent containerized tasks that co-schedules and 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 show the effectiveness, fast executions, and resource efficiency of our approach.

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e-pub ahead of print date: 5 September 2021
Published date: 13 November 2021
Venue - Dates: 2021 IEEE International Conference on Cloud Computing: CLOUD, , Chicago, United States, 2021-09-05 - 2021-09-10
Keywords: Edge computing, dependency-Aware, edge federation, execution time, federated learning, resource efficiency

Identifiers

Local EPrints ID: 454649
URI: http://eprints.soton.ac.uk/id/eprint/454649
ISSN: 2159-6182
PURE UUID: 01323fee-af61-4587-ad9c-f0d562b346ba

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Date deposited: 17 Feb 2022 17:49
Last modified: 23 Jul 2022 01:46

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Contributors

Author: Uchechukwu Awada
Author: Jiankang Zhang
Author: Sheng Chen
Author: Shuangzhi Li
Editor: Claudio Agostino Ardagna
Editor: Carl K. Chang
Editor: Ernesto Daminai
Editor: Rajiv Ranjan
Editor: Zhongjie Wang
Editor: Robert Ward
Editor: Jia Zhang
Editor: Wensheng Zhang

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