A Self Organised Workload Classification and Scheduling Approach in IoT-Edge-Cloud Ecosystem

Internet of Things (IoT) has brought major changes in the way the workload is processed closer to the location of the data source. The need for near-to-real time provisioning of IoT workload has necessitated the emergence of Edge Computing. However, it is not entirely possible to shit the entire workload on to the edge layer due to the computational limitations of the edge devices. Hence, this challenge ended up with the amalgamation of IoT-Edge-Cloud ecosystem. But, one of the major challenges in this ecosystem is workload management in a self-organized manner (or according to the nature of the workload). This article tries to overcome this challenge by utilizing the benefits of Self Organized Map (SOM). This article comprises of three strands, 1) a SOM-based workload classification approach to handle the IoT workloads in a flexible manner, 2) an energy-efficient workload scheduling scheme using container-based virtualization, and 3) a workload migration mechanism based on secure caching technique. The proposed strands are evaluated using a simulated environment and the outcomes seem promising in contrast to generalized container-based workload scheduling.