Traditional web search engines find it challenging to achieve good search quality for recency-sensitive queries, as they are prone to delays in discovering, indexing and ranking new web pages. In this paper we introduce PreGen, an adaptive preview generation system, which is run as part of a web search engine to improve search result quality for recency-sensitive queries. PreGen uses a machine learning algorithm to classify and select live web feeds, and generates "previews" of new web pages based on the link descriptions available in these feeds. The search engine can then index and present relevant page previews as part of its search results before the pages are fetched from the web, thereby reducing end-to-end delays. Our experiments show that PreGen improves the search relevance of a state-of-the-art search engine for recency-sensitive queries by 3% and reduces the average latencies of affected documents by 50%.

High-speed organizational networks running over leased fiber-optic lines or VPNs suffer from the well-known limitations of TCP over long-fat pipes. High-performance protocols like XCP require changes in the network. Other protocols like FastTCP assume nothing about the network but may not perform as well as network-aware protocols. In this paper, we present a new transport protocol that exploits the fact that these networks can offer priority queuing, thus finding the sweet spot between assuming too much and too little about the network. Our protocol splits a given transport flow into two prioritized flows. The higher priority flow operates with the legacy congestion control while the lower priority flow aggressively exploits spare capacity in the network while not interfering with the other participating flows. This isolation of the aggressive flow into strictly lower priority queues gives us more latitude in how to operate the aggressive component. We show through Emulab experiments of our implementation as well as simulations that this protocol can produce near-perfect goodputs in lossy networks, can considerably improve the completion time of short flows, and can sustain a high bottleneck utilization even in changing network conditions.

The rising popularity of live IPTV has triggered renewed interest in P2P multicast. In particular, the simple and robust 'swarming' style of P2P multicast is currently favored over more traditional tree-based approaches, which are seen to be complex and fragile. Swarming approaches, however, exhibit a basic control-overhead-versus-latency tradeoff that gears it more towards high-volume, latency-tolerant applications. This paper presents a new unstructured P2P multicast protocol called Chunkyspread that is tree-based yet simple and robust. Chunkyspread uses multiple trees to provide fine-grained control over member load, reacts quickly to membership changes, scales well, and has low overhead. This paper gives a detailed description of Chunkyspread and an apples-to-apples comparison with the DHT-based Splitstream multi-tree P2P multicast algorithm. We show that Chunkyspread exhibits far better control over transmit load than Splitstream, while exhibiting comparable or better latency and responsiveness to churn. This comparison establishes Chunkyspread as the 'best of breed' among tree- based P2P multicast algorithms, thus setting the stage for future comparisons with swarming-based approaches.