Ontological Modeling and Clustering Techniques for Service Allocation on the Edge: A Comprehensive Framework

Nowadays, we are in a world of large amounts of heterogeneous devices with varying computational resources, ranging from small devices to large supercomputers, located on the cloud, edge or other abstraction layers in between. At the same time, software tasks need to be performed. They have specific computational or other types of requirements and must also be executed at a particular physical location. Moreover, both services and devices may change dynamically. In this context, methods are needed to effectively schedule efficient allocations of services to computational resources. In this article, we present a framework to address this problem. Our proposal first uses knowledge graphs for describing software requirements and the availability of resources for services and computing nodes, respectively. To this end, we proposed an ontology that extends our previous work. Then, we proposed a hierarchical filtering approach to decide the best allocation of services to computational nodes. We carried out simulations to evaluate four different clustering strategies. The results showed different performances in terms of the number of allocated services and node overload.