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Supporting Critical Services with Intelligent Placement in the Computing Continuum | ||
| Journal of Data Science and Modeling | ||
| دوره 3، شماره 1 - شماره پیاپی 5، اسفند 2024، صفحه 223-253 اصل مقاله (3.33 M) | ||
| نوع مقاله: original | ||
| شناسه دیجیتال (DOI): 10.22054/jdsm.2026.86787.1073 | ||
| نویسندگان | ||
| Reza Sookhtsaraei* 1؛ Mehdi Sakhaei-nia2؛ Fereshteh Azadi Parand3 | ||
| 1Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran | ||
| 2Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan | ||
| 3Faculty of Statistics, Mathematics and Computer Sciences, Allameh Tabatabai University, Tehran, Iran. | ||
| چکیده | ||
| Critical services increasingly rely on distributed computing infrastructures that can deliver fast and reliable responses under dynamic conditions. Traditional cloud-centric deployments often struggle to meet these demands due to latency and reliability constraints. This paper addresses these challenges by exploring intelligent service placement across the cloud–edge computing continuum. We introduce an adaptive placement framework that continuously adjusts deployment decisions in response to changing system states and service requirements. To improve robustness, the framework integrates learning transfer mechanisms and a criticality-aware resilience strategy that prioritizes service continuity during failures. The proposed approach enhances responsiveness and reliability, leading to a higher proportion of services meeting strict timing constraints. Comprehensive experimental evaluations confirm that the framework provides more effective support for critical and delay-sensitive services compared to existing placement strategies in continuum-based environments. | ||
| کلیدواژهها | ||
| Mission critical services؛ computing continuum؛ service placement؛ deep reinforcement learning؛ fault tolerance | ||
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