SLA-aware Approach for IoT Workflow Activities Placement based on Collaboration between Cloud and Edge

In the Internet of Things (IoT) era, various nodes generate vast quantities of records, and data processing solutions consist of a number of activities/tasks that can be executed at the Edge of the network or on the Cloud. Their management at the Edge of the network may limit the time required to complete responses and return the final result/analytic to end users or applications. Also IoT nodes can perform a restricted amount of functionality over the contextual information gathered, owing to their restricted computational and resource capacities. Whether tasks are assigned to an Edge or a Cloud is based on a number of factors such as: tasks’ constraints, the load of nodes, and energy capacity. We propose a greedy heuristic algorithm to allocate tasks between the available resources while minimizing the execution time. The allocation algorithm considers factors such as the deadline associated with each task, location, and budget constraint. We evaluate the proposed work using iFogSim considering two use case studies. The performance analysis shows that the proposed algorithm has minimized cost, execution time, control loop delay, networking, and Cloud energy consumption compared to the Cloud-only approach.