Optimization of task allocations in Cloud to Fog environment with application to Intelligent Transportation Systems

Fog and Edge computing are opening up new opportunities to implement novel features of mobility, edge intelligence and end-user support. The successful implementation and deployment of Fog layers, as part of Cloud-to-thing-computing, largely depends on optimized allocation of tasks and applications to Fog and Edge nodes. Similarly as in other large scale distributed systems, the optimization problems that arise are computationally hard to solve. Such problems become even more challenging due to the need of application scenarios for larger computing capacity, beyond those of single nodes, requiring thus efficient resource grouping. 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 Integer Linear Programming) of applications to virtual computing nodes. Simulation results are reported to support the feasibility of the model and efficacy of the proposed approach. Applications of allocation methods to Intelligent Transportation Systems are also discussedPeer ReviewedPostprint (published version