REACH: Reinforcement Learning for Adaptive Microservice Rescheduling in the Cloud-Edge Continuum
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914080387760128 |
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| author | Bai, Xu Islam, Muhammed Tawfiqul Buyya, Rajkumar Toosi, Adel N. |
| author_facet | Bai, Xu Islam, Muhammed Tawfiqul Buyya, Rajkumar Toosi, Adel N. |
| contents | Cloud computing, despite its advantages in scalability, may not always fully satisfy the low-latency demands of emerging latency-sensitive pervasive applications. The cloud-edge continuum addresses this by integrating the responsiveness of edge resources with cloud scalability. Microservice Architecture (MSA) characterized by modular, loosely coupled services, aligns effectively with this continuum. However, the heterogeneous and dynamic computing resource poses significant challenges to the optimal placement of microservices. We propose REACH, a novel rescheduling algorithm that dynamically adapts microservice placement in real time using reinforcement learning to react to fluctuating resource availability, and performance variations across distributed infrastructures. Extensive experiments on a real-world testbed demonstrate that REACH reduces average end-to-end latency by 7.9%, 10%, and 8% across three benchmark MSA applications, while effectively mitigating latency fluctuations and spikes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06675 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | REACH: Reinforcement Learning for Adaptive Microservice Rescheduling in the Cloud-Edge Continuum Bai, Xu Islam, Muhammed Tawfiqul Buyya, Rajkumar Toosi, Adel N. Distributed, Parallel, and Cluster Computing Cloud computing, despite its advantages in scalability, may not always fully satisfy the low-latency demands of emerging latency-sensitive pervasive applications. The cloud-edge continuum addresses this by integrating the responsiveness of edge resources with cloud scalability. Microservice Architecture (MSA) characterized by modular, loosely coupled services, aligns effectively with this continuum. However, the heterogeneous and dynamic computing resource poses significant challenges to the optimal placement of microservices. We propose REACH, a novel rescheduling algorithm that dynamically adapts microservice placement in real time using reinforcement learning to react to fluctuating resource availability, and performance variations across distributed infrastructures. Extensive experiments on a real-world testbed demonstrate that REACH reduces average end-to-end latency by 7.9%, 10%, and 8% across three benchmark MSA applications, while effectively mitigating latency fluctuations and spikes. |
| title | REACH: Reinforcement Learning for Adaptive Microservice Rescheduling in the Cloud-Edge Continuum |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2510.06675 |