Microservice Workflow Scheduling with a Resource Configuration Model Under Deadline and Reliability Constraints
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a cost-optimized scheduling approach for microservice workflows in container environments, ensuring reliability and meeting deadlines. The method significantly reduces cloud resource costs while maintaining service quality.
Area Of Science
- Cloud Computing
- Distributed Systems
- Artificial Intelligence
Background
- Microservice architectures and containerization are rapidly evolving, increasing the complexity of large-scale cloud service scheduling.
- Ensuring reliability and meeting deadlines for cloud services under these evolving technologies presents a significant challenge.
Purpose Of The Study
- To present a cost-optimized scheduling approach for microservice workflows in container environments.
- To address deadline and reliability constraints in cloud service scheduling.
- To reduce cloud resource leasing costs while maintaining service quality.
Main Methods
- Developed a graph deep learning model (DeepMCC) for automatic container configuration to meet Quality of Service (QoS) requirements.
- Proposed a reliability microservice workflow scheduling algorithm (RMWS) using heuristic leasing and deployment strategies.
- Evaluated the approach on four scientific workflow datasets.
Main Results
- The proposed approach achieved an average cost reduction of 44.59% compared to existing reliability scheduling algorithms.
- Demonstrated significant cost improvements, with a 26.63% reduction in the worst case and 73.72% in the best case.
- Successfully met deadline and reliability constraints while optimizing resource utilization.
Conclusions
- The presented cost-optimized scheduling approach effectively balances reliability, deadline adherence, and cost reduction for microservice workflows.
- DeepMCC and RMWS offer a robust solution for efficient cloud service scheduling in containerized environments.
- The findings highlight the potential for substantial cost savings in cloud resource management through intelligent scheduling.
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