A proactive fog service provisioning framework for Internet of Things applications: An autonomic approach

다국어 초록 (Multilingual Abstract)

In recent years, Internet of Things (IoT) services have expanded to promote the quality of life in different areas. Cloud connectivity services are so popular now that they have prompted the experts to enhance cloud computing for its utilization in IoT, making everything online in the next few decades. For reducing latency, immediate processing, and network congestion, fog computing has emerged in which cloud computing is expanded to the edge of the network. On the other hand, concerning the limitations in fog hardware resources compared with the cloud, and the dynamic and unpredictable fog environment, the provision of dynamic fog services is a challenge. Automatic matching of the resources based on the workload oscillations of IoT applications leads to allocating minimum fog resources to IoT devices, therefore, the satisfaction of service level agreement (SLA) and quality of service (QoS) parameters.
The present article introduces a method based on the control monitoring‐analysis‐planning‐execution having shared knowledge‐base loop and presents an approach for dynamic resource provisioning based on autonomic computing and reinforcement learning techniques. The proposed scheme uses learning automata as a decision‐maker in the planning phase and time series prediction model in the analysis phase. The simulation test results indicated a reduced delay in service provisioning, total cost, and SLA violation compared with other approaches, highlighting the potential of fog computing in ensuring the QoS.
Introducing a new framework based on the control MAPE‐k loop to facilitate the relation between fog and cloud nodes and supply the fog services resources.
Provisioning of dynamic supply of resources for IoT applications on the basis of the integrated concept of autonomic computing and machine learning technique.
Formulating the issue of dynamic provisioning of resources in fog computing through calculation of delay in services, total cost, and SLA violations.
Simulating the tests for evaluating the efficiency of presented approach and comparing it with other strategies to show the potential of fog computing through proposed approach to improve the quality of services and experience in comparison with cloud computing.