Hybrid workflow provisioning and scheduling on cooperative edge cloud computing

![CDATA[The dramatic growth of IoT-based applications in many domains such as real-time monitoring, interactive reporting, and smart manufacturing brings challenges for adoption of cloud-based solutions for integration of latency-sensitive and resource-intensive applications. We refer to this integration as a hybrid-workflow. This paper provides a resource estimation and task scheduling framework to run hybrid workflows on edge and cloud computing systems. We propose an adaptive resource estimation technique with an online gradient descent approximation to handle the complexity of hybrid workflows. In addition, a scheduling technique to execute workflow tasks on a cooperative edge cloud system to resolve the issues of latencysensitive application as well as to improve resource utilization at the edge layer is proposed. Experimental results show the capability of the cooperative model in reducing the time and cost of running complex and large scale hybrid workflows.]]