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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.11266 |
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| _version_ | 1866912719860400128 |
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| author | Horvath, Kurt Kimovski, Dragi Prodan, Radu |
| author_facet | Horvath, Kurt Kimovski, Dragi Prodan, Radu |
| contents | Scheduling services within the computing continuum is complex due to the dynamic interplay of the Edge, Fog, and Cloud resources, each offering distinct computational and networking advantages. This paper introduces SCAREY, a user location-aided service lifecycle management framework based on state machines. SCAREY addresses critical service discovery, provisioning, placement, and monitoring challenges by providing unified dynamic state machine-based lifecycle management, allowing instances to transition between discoverable and non-discoverable states based on demand. It incorporates a scalable service deployment algorithm to adjust the number of instances and employs network measurements to optimize service placement, ensuring minimal latency and enhancing sustainability. Real-world evaluations demonstrate a 73% improvement in service discovery and acquisition times, 45% cheaper operating costs and over 57% less power consumption and lower CO2 emissions compared to existing related methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11266 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SCAREY: Location-Aware Service Lifecycle Management Horvath, Kurt Kimovski, Dragi Prodan, Radu Distributed, Parallel, and Cluster Computing Scheduling services within the computing continuum is complex due to the dynamic interplay of the Edge, Fog, and Cloud resources, each offering distinct computational and networking advantages. This paper introduces SCAREY, a user location-aided service lifecycle management framework based on state machines. SCAREY addresses critical service discovery, provisioning, placement, and monitoring challenges by providing unified dynamic state machine-based lifecycle management, allowing instances to transition between discoverable and non-discoverable states based on demand. It incorporates a scalable service deployment algorithm to adjust the number of instances and employs network measurements to optimize service placement, ensuring minimal latency and enhancing sustainability. Real-world evaluations demonstrate a 73% improvement in service discovery and acquisition times, 45% cheaper operating costs and over 57% less power consumption and lower CO2 emissions compared to existing related methods. |
| title | SCAREY: Location-Aware Service Lifecycle Management |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2505.11266 |