Resource Allocation Using Weighted Greedy Knapsack Based Algorithm in an Educational Fog Computing Environment

The Internet of Things ecosystem pertains to web-enabled connected devices that operate built-in processors record, send, and act on information from their surroundings via embedded communication hardware. IoT applications span education, healthcare self-driving cars. high delay supplied through the connecting network data centers huge traffic can cause system become congested, so cloud is not suggested for delay-sensitive it extremely difficult provide educational applications, particularly in a mix fog conditions. Fog computing was created address this problem improve time-sensitive by considering quality service (QoS). Allocation resources scheduling tasks are challenging issues environment. Resources required each application includes several modules run. In paper, we used Weighted Greedy Knapsack (WGK) based algorithm resource allocation modules/components system. We have considered smart parade certain services/resources proposed method experimented iFogSim. Proposed shows better energy consumption execution cost than concurrent, First-Come-First-Served (FCFS) Delay-Priority algorithms.