No abstract

Network coding has been proposed as a promising approach for peer-to-peer content distribution in recent literature. Not only reducing the average download time, but also improving resilience to peer churn has been showed as the benefits brought by network coding. State-of-the-art network coding content distribution systems perform network coding within segments or chunks, in order to reduce computational overhead. In our previous work (Xu et al. 2008), we proposed to schedule segment requests at a localrarest-first basic. Local-rarest-first segment scheduling, as we demonstrated, is superior to the random scheduling. In this paper, we make a further step towards improving chunked network coding content distribution systems. We propose a requests reducer that reduces the overhead in control traffic and an encoding vectors reducer that eliminates the transmission of encoding vectors. Our contributions are to save unnecessary requests from downstream peers, and to reduce encoding vectors payload when the upstream peer owns the complete requested segment. This paper presents a realistic implementation, named I-Swifter. And we also make a comparative study on various related implementations. Experimental results show that there is about 10-20% of encoding vectors can be saved in I-Swifter. Moreover, I-Swifter improves average and maximum download time, the server load as well.

Abstract-A vehicle-to-grid (V2G) aggregator is an agent between the power grid and plug-in hybrid electrical vehicles (PHEVs). This paper studies the coordinated charging control of a V2G aggregator, which aims at minimizing the charging cost and reducing the power losses incurred by the fluctuating load. On one hand, a lower cost of charging gives the owners of PHEVs an incentive to cooperate. On the other hand, with an increasing popularity of PHEVs, the impact on the power grid such as power losses should be of concern to the aggregator. As an inherent property of a V2G aggregator, we enable bidirectional electric power flows between PHEVs and the power grid. Given the planned schedules of all the vehicles that are managed by an aggregator, we formulate the coordinated charging control as a dynamic programming problem. Due to the curse of dimensionality, we apply an approximate dynamic programming approach, which reduces the dimensionality of both state space and control space, to obtain the control sequences. We conduct simulations given the 24-hour schedules of 100 vehicles. Simulation results show that coordinated charging control can reduce both the total cost of charging and power losses significantly, compared with the scheme where each vehicle starts charging as soon as it is connected to the grid.