Energy-aware Graph Job Allocation in Software Defined Air-Ground Integrated Vehicular Networks

The software defined air-ground integrated vehicular (SD-AGV) networks have emerged as a promising paradigm, which realize the flexible on-ground resource sharing to support innovative applications for UAVs with heavy computational overhead. In this paper, we investigate a vehicular cloud-assisted graph job allocation problem in SD-AGV networks, where the computation-intensive jobs carried by UAVs, and the vehicular cloud are modeled as graphs. To map each component of the graph jobs to a feasible vehicle, while achieving the trade-off among minimizing UAVs' job completion time, energy consumption, and the data exchange cost among vehicles, we formulate the problem as a mixed-integer non-linear programming problem, which is Np-hard. Moreover, the constraint associated with preserving job structures poses addressing the subgraph isomorphism problem, that further complicates the algorithm design. Motivated by which, we propose an efficient decoupled approach by separating the template (feasible mappings between components and vehicles) searching from the transmission power allocation. For the former, we present an efficient algorithm of searching for all the subgraph isomorphisms with low computation complexity. For the latter, we introduce a power allocation algorithm by applying convex optimization techniques. Extensive simulations demonstrate that the proposed approach outperforms the benchmark methods considering various problem sizes.Comment: 14 pages, 7 figure

A survey on cost-effective context-aware distribution of social data streams over energy-efficient data centres

Social media have emerged in the last decade as a viable and ubiquitous means of communication. The ease of user content generation within these platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges, including derivation of real-time meaningful insights from effectively gathered social information, as well as a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general. In this article we present a comprehensive survey that outlines the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centres supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. We systematize the existing literature and proceed to identify and analyse the main research points and industrial efforts in the area as far as modelling, simulation and performance evaluation are concerned