Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency
Satellite edge computing has attracted the attention of many scholars, but the limited resources of satellite networks bring great difficulties to the processing of edge-computing-dependent tasks. Therefore, under the system model of the satellite-terrestrial joint network architecture, this paper p...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10027 |
| _version_ | 1797581445184618496 |
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| author | Zhiguo Liu Yingru Jiang Junlin Rong |
| author_facet | Zhiguo Liu Yingru Jiang Junlin Rong |
| author_sort | Zhiguo Liu |
| collection | DOAJ |
| description | Satellite edge computing has attracted the attention of many scholars, but the limited resources of satellite networks bring great difficulties to the processing of edge-computing-dependent tasks. Therefore, under the system model of the satellite-terrestrial joint network architecture, this paper proposes an efficient scheduling strategy based on task degrees and a resource allocation strategy based on the improved sparrow search algorithm, aiming at the low success rate of application processing caused by the dependency between tasks, limited resources, and unreasonable resource allocation in the satellite edge network, which leads to the decline in user experience. The scheduling strategy determines the processing order of tasks by selecting subtasks with an in-degree of 0 each time. The improved sparrow search algorithm incorporates opposition-based learning, random search mechanisms, and Cauchy mutation to enhance search capability and improve global convergence. By utilizing the improved sparrow search algorithm, an optimal resource allocation strategy is derived, resulting in reduced processing latency for subtasks. The simulation results show that the performance of the proposed algorithm is better than other baseline schemes and can improve the processing success rate of applications. |
| first_indexed | 2024-03-10T23:05:34Z |
| format | Article |
| id | doaj.art-83249b0953804b01b08bc3b0041f71cb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:05:34Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-83249b0953804b01b08bc3b0041f71cb2023-11-19T09:22:00ZengMDPI AGApplied Sciences2076-34172023-09-0113181002710.3390/app131810027Resource Allocation Strategy for Satellite Edge Computing Based on Task DependencyZhiguo Liu0Yingru Jiang1Junlin Rong2Communication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaSatellite edge computing has attracted the attention of many scholars, but the limited resources of satellite networks bring great difficulties to the processing of edge-computing-dependent tasks. Therefore, under the system model of the satellite-terrestrial joint network architecture, this paper proposes an efficient scheduling strategy based on task degrees and a resource allocation strategy based on the improved sparrow search algorithm, aiming at the low success rate of application processing caused by the dependency between tasks, limited resources, and unreasonable resource allocation in the satellite edge network, which leads to the decline in user experience. The scheduling strategy determines the processing order of tasks by selecting subtasks with an in-degree of 0 each time. The improved sparrow search algorithm incorporates opposition-based learning, random search mechanisms, and Cauchy mutation to enhance search capability and improve global convergence. By utilizing the improved sparrow search algorithm, an optimal resource allocation strategy is derived, resulting in reduced processing latency for subtasks. The simulation results show that the performance of the proposed algorithm is better than other baseline schemes and can improve the processing success rate of applications.https://www.mdpi.com/2076-3417/13/18/10027satellite networksedge computingresource allocationsparrow search algorithms |
| spellingShingle | Zhiguo Liu Yingru Jiang Junlin Rong Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency Applied Sciences satellite networks edge computing resource allocation sparrow search algorithms |
| title | Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency |
| title_full | Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency |
| title_fullStr | Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency |
| title_full_unstemmed | Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency |
| title_short | Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency |
| title_sort | resource allocation strategy for satellite edge computing based on task dependency |
| topic | satellite networks edge computing resource allocation sparrow search algorithms |
| url | https://www.mdpi.com/2076-3417/13/18/10027 |
| work_keys_str_mv | AT zhiguoliu resourceallocationstrategyforsatelliteedgecomputingbasedontaskdependency AT yingrujiang resourceallocationstrategyforsatelliteedgecomputingbasedontaskdependency AT junlinrong resourceallocationstrategyforsatelliteedgecomputingbasedontaskdependency |