What question did this study set out to answer?

This work aims to improve service placement and resource allocation for IoT applications in multi-cloud and fog computing environments.

Internet of Things-Centric Optimized Service Provisioning in Multi-Cloud Environment

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Randomized trial examines service provisioning efficacy in IoT applications, indicating a need for dynamic frameworks.

This work aims to improve service placement and resource allocation for IoT applications in multi-cloud and fog computing environments.
Introduced a lightweight, QoS-aware service placement algorithm for real-time evaluation of performance factors.
Implemented enhanced weighted fair queuing (EWFQ) for flexible task scheduling based on user priorities.
Developed a weighted Q-learning algorithm (WQLA) to enhance decision-making through environmental feedback.
Simulation results show reduced latency and consistent wait times compared to heuristic and genetic approaches.
The framework maintains energy efficiency and service availability under fluctuating load conditions.
Demonstrated superiority in adapting to dynamic resource availability and workload demands.

Atre et al. (2026) studied this question.

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