TL;DR
Results show that runtime stability-aware decision making can improve the efficiency and predictability of self-adaptation outcomes in a controlled cloud setting.
A double deep Q-Network-based proactive autoscaling approach (DDQN-Proactive) along with Resource Removal Strategy (RRS) that enhances decision-making by decoupling action selection from value evaluation, enabling more stable and adaptive scaling.
CARSO (Counterfactual Autoscaling and Resource-efficient Service Orchestration), a proactive and interpretable framework that integrates eXplainable Artificial Intelligence (XAI) into the autoscaling process that outperforms state-of-the-art proactive autoscaling frameworks in both QoS compliance and overall resource u...
Analytical findings indicate that decentralized agent specialization, shared contextual state, adaptive workload redistribution, and failure-aware coordination can provide a stronger basis for resilient streaming than static pipelines.
The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, res...
The results show that soft SLO limits reduce corrective rescheduling actions by 49% compared to hard-limit approaches while maintaining acceptable performance guarantees, and resource-aware scheduling decreases node-level congestion and further mitigates SLO violations, demonstrating the effectiveness of incorporating...
Multi-tenant cloud environments demand adaptive security governance policies that cannot be fixed. Traditional Service Control Policies (SCPs) are usually pre-established, coarse-grained, and isolated from runtime characteristics such as user behavior, workload sensitivity, or region-specific constraints. This discrepa...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
