Machine Learning
Reza Sookhtsaraei; Mehdi Sakhaei-nia; Fereshteh Azadi Parand
Abstract
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 ...
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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.
