Learning-Based Deadlock-Free Multi-Objective Task Offloading in Satellite Edge Computing With Data-Dependent Constraints and Limited Buffers

Satellite edge computing (SEC) is important for future network deployments because of its global coverage and low-latency computing services. Nevertheless, due to data dependencies among tasks and limited buffers in satellites, a coupling exists between transmission and computation, and undesired de...

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Veröffentlicht in:IEEE transactions on network science and engineering Jg. 12; H. 1; S. 356 - 368
Hauptverfasser: Zhang, Ruipeng, Feng, Yanxiang, Yang, Yikang, Li, Xiaoling, Li, Hengnian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Buffers
Computation offloading
Costs
Couplings
Edge computing
Energy consumption
Energy resolution
Evolutionary algorithms
Internet of Things
Machine learning
Mixed integer
multi-task offloading
Multiple objective analysis
Nonlinear programming
Petri nets
Polynomials
Real time
satellite edge computing
Satellite-terrestrial integrated networks
Satellites
Scheduling algorithms
Search process
System recovery
Traffic control
Vectors
Windows (intervals)
ISSN:2327-4697, 2334-329X
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Zusammenfassung:Satellite edge computing (SEC) is important for future network deployments because of its global coverage and low-latency computing services. Nevertheless, due to data dependencies among tasks and limited buffers in satellites, a coupling exists between transmission and computation, and undesired deadlocks may arise. This paper addresses task offloading in SEC and aims to minimize service latency, energy consumption, and time window violations simultaneously. First, a mixed-integer nonlinear programming model is presented. To resolve potential deadlocks, a deadlock amending algorithm (DAA) based on Petri net with polynomial time complexity is proposed. Deadlocks in solutions are amended by finding a transition sequence that corresponding transmission and computation can be performed sequentially. By embedding DAA, we develop a learning-based deadlock-free multi-objective scheduling algorithm (LDMOSA) that combines the exploration of evolutionary algorithms with the perception of reinforcement learning. To enhance the convergence and diversity of solutions, an initialization strategy employing problem-specific constructive heuristics is designed. Then, a learning-based mechanism is used to leverage real-time information to perform adaptive operator selection during the search process. Finally, extensive experiments demonstrate the effectiveness of DAA in resolving deadlocks, and the LDMOSA outperforms state-of-the-art algorithms for task offloading in SEC.
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ISSN:2327-4697
2334-329X
DOI:10.1109/TNSE.2024.3496902