Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system
Recently, several edge deployment types, such as on-premise edge clusters, Unmanned Aerial Vehicles (UAV)-attached edge devices, telecommunication base stations installed with edge clusters, etc., are being deployed to enable faster response time for latency-sensitive tasks. One fundamental problem...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-12-01
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| Series: | Digital Communications and Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864824000956 |
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| _version_ | 1846101607990689792 |
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| author | Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang |
| author_facet | Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang |
| author_sort | Uchechukwu Awada |
| collection | DOAJ |
| description | Recently, several edge deployment types, such as on-premise edge clusters, Unmanned Aerial Vehicles (UAV)-attached edge devices, telecommunication base stations installed with edge clusters, etc., are being deployed to enable faster response time for latency-sensitive tasks. One fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource utilization. Existing approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks, inter-dependencies of tasks and edge resource availability. These approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support, as well as provider lock-in. Therefore, we present EdgeColla, which is based on the integration of edge resources running across multi-edge deployments. EdgeColla leverages learning techniques to intelligently dispatch multi-dependent tasks, and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize them. Extensive experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes. |
| format | Article |
| id | doaj-art-1d39b2697a1f46f19b1c25d85e856707 |
| institution | Kabale University |
| issn | 2352-8648 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj-art-1d39b2697a1f46f19b1c25d85e8567072024-12-29T04:47:38ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482024-12-0110618371850Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing systemUchechukwu Awada0Jiankang Zhang1Sheng Chen2Shuangzhi Li3Shouyi Yang4School of Software, Henan Institute of Science and Technology, Xinxiang 453003, China; School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, ChinaDepartment of Computing and Informatics, Bournemouth University, Poole BH12 5BB, UK; Corresponding authors.School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK; Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, ChinaSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; Corresponding authors.School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, ChinaRecently, several edge deployment types, such as on-premise edge clusters, Unmanned Aerial Vehicles (UAV)-attached edge devices, telecommunication base stations installed with edge clusters, etc., are being deployed to enable faster response time for latency-sensitive tasks. One fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource utilization. Existing approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks, inter-dependencies of tasks and edge resource availability. These approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support, as well as provider lock-in. Therefore, we present EdgeColla, which is based on the integration of edge resources running across multi-edge deployments. EdgeColla leverages learning techniques to intelligently dispatch multi-dependent tasks, and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize them. Extensive experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.http://www.sciencedirect.com/science/article/pii/S2352864824000956Edge computingCollaborative learningResource utilizationExecution timeEdge federationGang scheduling |
| spellingShingle | Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system Digital Communications and Networks Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling |
| title | Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system |
| title_full | Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system |
| title_fullStr | Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system |
| title_full_unstemmed | Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system |
| title_short | Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system |
| title_sort | collaborative learning based inter dependent task dispatching and co location in an integrated edge computing system |
| topic | Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling |
| url | http://www.sciencedirect.com/science/article/pii/S2352864824000956 |
| work_keys_str_mv | AT uchechukwuawada collaborativelearningbasedinterdependenttaskdispatchingandcolocationinanintegratededgecomputingsystem AT jiankangzhang collaborativelearningbasedinterdependenttaskdispatchingandcolocationinanintegratededgecomputingsystem AT shengchen collaborativelearningbasedinterdependenttaskdispatchingandcolocationinanintegratededgecomputingsystem AT shuangzhili collaborativelearningbasedinterdependenttaskdispatchingandcolocationinanintegratededgecomputingsystem AT shouyiyang collaborativelearningbasedinterdependenttaskdispatchingandcolocationinanintegratededgecomputingsystem |