Allocation of applications to Fog resources via semantic clustering techniques: with scenarios from intelligent transportation systems

Abstract

The fast development in IoT and Cloud technologies has propelled the emergence of a variety of computing paradigms, among which Fog and Edge computing are salient computing technologies. Such new paradigms are opening up new opportunities to implement novel application scenarios, not possible before, by supporting features of mobility, edge intelligence and end-user support. This, however, comes with new computing challenges. One such challenge is the allocation of applications to Fog and Edge nodes. Indeed, for some application scenarios larger computing capacity might be needed. Therefore, due to co-existence of computing devices of different computing granularity, techniques for grouping up and clustering resources into virtual nodes of larger computing capacity are required. In this paper we present some clustering techniques for creating virtual computing nodes from Fog/Edge nodes by combining semantic description of resources with semantic clustering techniques. Then, we use such clusters for optimal allocation (via heuristics and Liner Programming) of applications to virtual computing nodes. Simulation results are reported to support the feasibility of the model and efficacy of the proposed approach. First Fit Heuristic Algorithm (FFHA) outperformed ILP method for medium and large size instances. Likewise, FFHA performed more consistently than ILP on various experimental setting. Finally, the results showed that the proposed clustering techniques deliver relatively fast response times, while enabling the service of a larger number of applications, with more demanding requirements