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Message Ferrying is a mobility assisted scheme in which a special node, called a message ferry, is tasked with delivering data among a set of disconnected wireless nodes. One key challenge for such scheme is to design the ferry route in a way that improves certain network characteristics such as average data delivery delay or data loss ratio. Previous work has optimized ferry travel time, rather than directly optimizing message delivery performance metrics. In this paper, we revisit the basic ferry route design problem for stationary nodes with the goal of providing a framework for optimizing delay performance. We start with a Markovian Decision Problem (MDP) formulation which produces the optimal ferry route that minimizes the average data delivery delay. While this formulation, in principle, enables optimal ferry route design, it is numerically intractable for moderate to large size problems. Solutions to small problems, however, yield insight into the properties of optimal ferry routes. These insights, in turn, lead us to propose a ferry route design algorithm that takes advantage of the similarity between our problem and the link scheduling problem in traditional networks. Our algorithm is inspired by the Deficit Round Robin (DRR) algorithm which has provable properties for delay optimization when applied to link scheduling. Using simulations we show that our DRR-based algorithm produces ferry routes that are close to optimal when compared to the MDP-derived solutions for small problems. Our results also show that our algorithm produces ferry routes for moderate to large problems that significantly outperform existing solutions.

Despite the growing popularity of video streaming over the Internet, problems such as re-buffering and high startup latency continue to plague users. In this paper, we present an end-to-end characterization of Yahoo's video streaming service, analyzing over 500 million video chunks downloaded over a two-week period. We gain unique visibility into the causes of performance degradation by instrumenting both the CDN server and the client player at the chunk level, while also collecting frequent snapshots of TCP variables from the server network stack. We uncover a range of performance issues, including an asynchronous disk-read timer and cache misses at the server, high latency and latency variability in the network, and buffering delays and dropped frames at the client. Looking across chunks in the same session, or destined to the same IP prefix, we see how some performance problems are relatively persistent, depending on the video's popularity, the distance between the client and server, and the client's operating system, browser, and Flash runtime.

Mobile ad hoc networks range from traditional MANETs where end-to-end paths exist from sources to destinations, to DTNs where no contemporaneous end-to-end paths exist and communication is achieved by the store, carry, and forward model of routing. Hence, nodes of these networks need to identify the level of connectivity of the network they belong to and classify it as a MANET or a DTN, in order to properly select appropriate protocols to achieve end-toend communication. What is more, since mobile ad hoc networks change over time and space, nodes need to periodically re-access their network classification to adapt to the always changing environment. Recently, there has been an effort to classify the various types of mobile ad hoc networks assuming there is a centralized authority that has complete knowledge of the network and its dynamics. In this paper we design distributed mechanisms for nodes to perform the above classification on the fly, based only on local information that they collect as they move and encounter other nodes. The mechanisms take advantage of a combination of measurements and analytical techniques. We investigate the accuracy of our mechanisms by comparing the network classification of the centralized authority to that of a node using our schemes.