Existing edge computing solutions in the Internet of Things (IoT) domain operate with the control plane residing in the cloud and edge as a slave that executes the workload deployed by the cloud. The growing diversity in the IoT applications requires the edge to be able to run multiple distinct workloads corresponding to the dedicated inputs it receives, each catering to a specific task. Achieving this with the current approach poses a limitation as the cloud lacks the local knowledge at the edge and sharing this knowledge regularly between the edge and the cloud will defeat the very purpose of edge computing, i.e., low latency, less network congestion and data privacy. To solve this problem, we propose an orchestration framework for edge computing that enables the edge to actively initiate and orchestrate the workloads on request by using the local knowledge available in the form of IoT resources at the edge.