Fog computing domain plays a prominent role in supporting time-delicate applications, which are associated with smart Internet of Things (IoT) services, like smart healthcare and smart city. However, cloud computing is a capable standard for IoT in data processing owing to the high latency restriction of the cloud, and it is incapable of satisfying needs for time-sensitive applications. The resource provisioning and allocation process in fog-cloud structure considers dynamic alternations in user necessities, and also restricted access resources in fog devices are more challenging. The global adoption of IoT-driven applications has led to the rise of fog computing structure, which permits perfect connection for mobile edge and cloud resources. The effectual scheduling of application tasks in fog environments is a challenging task because of resource heterogeneity, stochastic behaviours, network hierarchy, controlled resource abilities, and mobility elements in IoT. The deadline is the most significant challenge in the fog computing structure due to the dynamic variations in user requirement parameters. In this paper, Mayfly Taylor Optimisation Algorithm (MTOA) is developed for dynamic scheduling in the fog-cloud computing model. The developed MTOA-based Deep Q-Network (DQN) showed better performance with energy consumption, service level agreement (SLA), and computation cost of 0.0162, 0.0114, and 0.0855, respectively.
The Internet of Things ecosystem pertains to web-enabled connected devices that operate built-in processors to record, send, and act on information from their surroundings via embedded communication hardware. IoT applications span from education, healthcare to self-driving cars. The high delay supplied through the connecting network to the data centers and huge data traffic can cause the system to become congested, so the cloud is not suggested for delay-sensitive applications and it is extremely difficult to provide educational applications, particularly in a mix of cloud and fog conditions. Fog computing was created to address this problem and improve time-sensitive applications by considering quality of service (QoS). Allocation of resources and scheduling of tasks are challenging issues for IoT applications in a fog environment. Resources are required for each educational application that includes several modules to run. In this paper, we used Weighted Greedy Knapsack (WGK) based algorithm for the resource allocation to the modules/components in the fog system. We have considered the smart parade application to provide certain services/resources and proposed method was experimented in iFogSim. Proposed method shows a better energy consumption and execution cost than that of concurrent, First-Come-First-Served (FCFS) and Delay-Priority algorithms.
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