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microservice workflow Scheduling with a Resource Configuration Model Under Deadline and Reliability Constraints

SENSORS 2025年第4期25卷 1253-1253页

作者： Li, Wenzheng Li, Xiaoping Chen, Long Wang, Mingjing Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325000 Peoples R China

With the continuous evolution of microservice architecture and containerization technology, the challenge of efficiently and reliably scheduling large-scale cloud services has become increasingly prominent. In this paper, we present a cost-optimized scheduling approach with resource configuration for microservice workflows in container environments, taking into account deadline and reliability constraints. We introduce a graph deep learning model (DeepMCC) that automatically configures containers to meet various service quality (QoS) requirements. Additionally, we propose a reliability microservice workflow scheduling algorithm (RMWS), which incorporates heuristic leasing and deployment strategies to ensure reliability while reducing cloud resource leasing cost. Experiments on four scientific workflow datasets show that the proposed approach achieves an average cost reduction of 44.59% compared to existing reliability scheduling algorithms, with improvements of 26.63% in the worst case and 73.72% in the best case.

关键词： workflow scheduling microservice workflow container environment cost-optimized scheduling

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Pattern learning for scheduling microservice workflow to cloud containers

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第9期15卷 3701-3714页

作者： Li, Wenzheng Li, Xiaoping Chen, Long Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Jiangsu Peoples R China

Patterns are crucial for efficiently scheduling microservice workflow applications to containers in cloud computing scenarios. However, it is challenging to learn patterns of microservice workflows because of their complex precedence constrained structures provided by users with more lightweighted, diversified, and personalized services. In this paper, we propose a graph neural network is designed to identify patterns within a set of microservice workflows by mining the common substructures of workflows. Based on the learned patterns, a pattern-based scheduling algorithm framework is developed for microservice workflows with soft deadline constraints to minimize the average tardiness. A sorting strategy is introduced based on urgency and pattern coverage rate. For simplification of the task sorting process, the pattern-based task sorting algorithm (PB-TS) is devised. Furthermore, a resource selection phase is incorporated to the pattern-based resource selection algorithm (PB-RS) to minimize the candidate resource space. Experimental results demonstrate the proposed method is much efficient as compared to three classical algorithms.

关键词： Service computing Scheduling optimization microservice workflow Container cloud Pattern recognition

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