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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2509.06722 |
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| _version_ | 1866914066346278912 |
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| author | Chamoun, Samuel Chakraborty, Sirin Graves, Eric Chan, Kevin Sun, Yin |
| author_facet | Chamoun, Samuel Chakraborty, Sirin Graves, Eric Chan, Kevin Sun, Yin |
| contents | In this paper, we study a goal-oriented communication problem for edge server monitoring, where compute jobs arrive intermittently at dispatchers and must be immediately assigned to distributed edge servers. Due to competing workloads and the dynamic nature of the edge environment, server availability fluctuates over time. To maintain accurate estimates of server availability states, each dispatcher updates its belief using two mechanisms: (i) active queries over shared communication channels and (ii) feedback from past job executions. We formulate a query scheduling problem that maximizes the job success rate under limited communication resources for queries. This problem is modeled as a Restless Multi-Armed Bandit (RMAB) with multiple actions and addressed using a Net-Gain Maximization (NGM) scheduling algorithm, which selects servers to query based on their expected improvement in execution performance. Simulation results show that the proposed NGM Policy significantly outperforms baseline strategies, achieving up to a 30% gain over the Round-Robin Policy and up to a 107% gain over the Never-Query Policy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Edge Server Monitoring for Job Assignment Chamoun, Samuel Chakraborty, Sirin Graves, Eric Chan, Kevin Sun, Yin Systems and Control In this paper, we study a goal-oriented communication problem for edge server monitoring, where compute jobs arrive intermittently at dispatchers and must be immediately assigned to distributed edge servers. Due to competing workloads and the dynamic nature of the edge environment, server availability fluctuates over time. To maintain accurate estimates of server availability states, each dispatcher updates its belief using two mechanisms: (i) active queries over shared communication channels and (ii) feedback from past job executions. We formulate a query scheduling problem that maximizes the job success rate under limited communication resources for queries. This problem is modeled as a Restless Multi-Armed Bandit (RMAB) with multiple actions and addressed using a Net-Gain Maximization (NGM) scheduling algorithm, which selects servers to query based on their expected improvement in execution performance. Simulation results show that the proposed NGM Policy significantly outperforms baseline strategies, achieving up to a 30% gain over the Round-Robin Policy and up to a 107% gain over the Never-Query Policy. |
| title | Edge Server Monitoring for Job Assignment |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.06722 |