Dmitrii Chemodanov

TL;DR: A novel “offload decision-making” algorithm that analyzes the tradeoffs in computing policies to offload visual data processing to address the processing-throughput versus energy-efficiency tradeoffs and a “Sustainable Policy-based Intelligence-Driven Edge Routing’ algorithm that uses machine learning within Mobile Ad hoc Networks.

TL;DR: This paper proposes an incident-supporting visual cloud computing solution by defining a collection, computation, and consumption (3C) architecture supporting fog computing at the network edge close to the collection/consumption sites, which is coupled with cloud offloading to a core computation, utilizing software-defined networking (SDN).

TL;DR: A stateless greedy forwarding is proposed that uses an area knowledge obtained from the satellite imagery (available at the edge cloud) by applying deep learning to proactively avoid the local minimum problem by diverting traffic with an algorithm that emulates electrostatic repulsive forces.

TL;DR: Insight is provided on how to enable suitable resource calibration and perform network troubleshooting for high user experience for both the therapist and the senior, and realize a Big Data architecture for PTaaS and other similar personalized healthcare services to be remotely delivered at a large-scale in a reliable, secure and cost-effective manner.

TL;DR: This paper proposes a novel metapath composite variable approach that reaches 99% optimality on average and takes seconds for practically sized integer MCCF problems of US Tier-1 and regional infrastructure providers’ topologies and shows that the solution composes twice as many SFCs than the state-of-the-art network virtualization methods.