A dynamic fog service provisioning approach for IoT applications. (15th July 2020) - Faraji Mehmandar, Mohammad; Jabbehdari, Sam; Haj Seyyed Javadi, Hamid
Summary: Internet 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. Abstract : In this paper, framework is presented based on the control MAPE‐k loop that covers the issues of resource prediction and catch the dynamicity of IoT systems to examine their performance. In our framework, we have three layers that include IoT devices, the fog layer, and the cloud layer. To dynamically provide resources, we used reinforcement learning in combination with a reference model to enable autonomic control of loops, namely, the control MAPE (Monitor, Analysis, Plan, Execute) loop, running in the cloud environment to achieve dynamic computing. Control MAPE loop is an intelligent agent that realized its situation though utilizing sensors and make use of these conceptions to determine actions that should be implemented in the environment.