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SummaryInternet of Things (IoT) is an ecosystem that can improve the life quality of humans through smart services, thereby facilitating everyday tasks. Connecting to cloud and utilizing its services are now public and common, and the experts seek to find some ways to complete cloud computing to use it in IoT, which in next decades will make everything online. Fog computing, where the cloud computing expands to the edge of the network, is one way to achieve the objectives of delay reduction, immediate processing, and network congestion. Since IoT devices produce variations of workloads over time, IoT application services will experience traffic trace fluctuations. So knowing about the distribution of future workloads required to handle IoT workload while meeting the QoS constraint. As a result, in the context of fog computing, the main objective of resource management is dynamic resource provisioning such that it avoids the excess or dearth of provisioning. In the present work, we first propose a distributed computing framework for autonomic resource management in the context of fog computing. Then, we provide a customized version of a provisioning system for IoT services based on control MAPE‐k loop. The system makes use of a reinforcement learning technique as decision maker in planning phase and support vector regression technique in analysis phase. At the end, we conduct a family of simulation‐based experiments to assess the performance of our introduced system. The average delay, cost, and delay violation are decreased by 1.95%, 11%, and 5.1%, respectively, compared with existing solutions.

The number of Internet-connected devices is constantly increasing due to the growth of IoT. However, this results in a large volume of data transmission, which can cause issues with cloud-based storage and data processing due to inadequate bandwidth. This could lead to inadequacy of IoT; therefore, managing and storing data in such a way as not to cause the slightest delay in processing has become a major challenge in IoT. Both fog and cloud computing offer storage space, applications, and data for users, but fog computing is more geographically distributed and closer to the end-user, which increases system efficiency and reduces data transmission distance. Various QoS requirements of IoT services, distributed and heterogeneous nature of fog node computational capabilities make the application placement in Fog a challenging task. This paper proposes a solution that utilizes the Harris hawks optimization technique to monitor QoS requirements and available fog node capabilities to determine an efficient service placement plan. The proposed mechanism considers throughput, cost, and energy consumption as objective functions while meeting the QoS requirements of each IoT service. The simulation results obtained demonstrate that the proposed solution increases the resource usage and service acceptance ratio by 4.5% and 3.8%, respectively and reduces the service delay and the energy consumption by 2.95% and 1.62%, respectively compared with other state-of-the-art works.

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