Latency (engineering)

About: Latency (engineering) is a research topic. Over the lifetime, 7278 publications have been published within this topic receiving 115409 citations. The topic is also known as: lag.

Low latency live video streaming using HTTP chunked encoding

Abstract: Hypertext transfer protocol (HTTP) based streaming solutions for live video and video on demand (VOD) applications have become available recently. However, the existing HTTP streaming solutions cannot provide a low latency experience due to the fact that inherently in all of them, latency is tied to the duration of the media fragments that are individually requested and obtained over HTTP. We propose a low latency HTTP streaming approach using HTTP chunked encoding, which enables the server to transmit partial fragments before the entire video fragment is published. We develop an analytical model to quantify and compare the live latencies in three HTTP streaming approaches. Then, we present the details of our experimental setup and implementation. Both the analysis and experimental results show that the chunked encoding approach is capable of reducing the live latency to one to two chunk durations and that the resulting live latency is independent of the fragment duration.

Delayed-Dynamic-Selective (DDS) Prediction for Reducing Extreme Tail Latency in Web Search

Abstract: A commercial web search engine shards its index among many servers, and therefore the response time of a search query is dominated by the slowest server that processes the query. Prior approaches target improving responsiveness by reducing the tail latency of an individual search server. They predict query execution time, and if a query is predicted to be long-running, it runs in parallel, otherwise it runs sequentially. These approaches are, however, not accurate enough for reducing a high tail latency when responses are aggregated from many servers because this requires each server to reduce a substantially higher tail latency (e.g., the 99.99th-percentile), which we call extreme tail latency.We propose a prediction framework to reduce the extreme tail latency of search servers. The framework has a unique set of characteristics to predict long-running queries with high recall and improved precision. Specifically, prediction is delayed by a short duration to allow many short-running queries to complete without parallelization, and to allow the predictor to collect a set of dynamic features using runtime information. These features estimate query execution time with high accuracy. We also use them to estimate the prediction errors to override an uncertain prediction by selectively accelerating the query for a higher recall.We evaluate the proposed prediction framework to improve search engine performance in two scenarios using a simulation study: (1) query parallelization on a multicore processor, and (2) query scheduling on a heterogeneous processor. The results show that, for both scenarios, the proposed framework is effective in reducing the extreme tail latency compared to a start-of-the-art predictor because of its higher recall, and it improves server throughput by more than 70% because of its improved precision.

An improved approximation algorithm for data aggregation in multi-hop wireless sensor networks

Abstract: Data aggregation is an efficient primitive in wireless sensor network (WSN) applications. This paper focuses on data aggregation scheduling problem to minimize the latency. We propose an efficient distributed method that produces a collision-free schedule for data aggregation in WSNs. We prove that the latency of the aggregation schedule generated by our algorithm is at most 16R+Δ--14 time-slots. Here R is the network radius and Δ is the maximum node degree in the communication graph of the original network. Our method significantly improves the previously known best data aggregation algorithm [3], that has a latency bound of 24D+6Δ+16 time-slots, where D is the network diameter (Note that D can be as large as 2R). We conduct extensive simulations to study the practical performances of our proposed data aggregation method. Our simulation results corroborate our theoretical results and show that our algorithms perform better in practice.We prove that the overall lower-bound of latency of data aggregation under any interference model is max{log n, R} where n is the network size. We provide an example to show that the lower-bound is (approximately) tight under protocol interference model when rI=r, where rI is the interference range and r is the transmission range. We also derive the lower-bound of latency under protocol interference model when r