Survey of traffic control schemes and error control schemes for ATM networks

Among the techniques proposed for B-ISDN transfer mode, ATM concept is considered to be the most promising transfer technique because of its flexibility and efficiency. This paper surveys and reviews a number of topics related to ATM networks. Those topics cover congestion control, provision of multiple classes of traffic, and error control. Due to the nature of ATM networks, those issues are far more challenging than in conventional networks. Sorne of the more promising solutions to those issues are surveyed, and the corresponding results on performance are summarized. Future research problems in ATM protocol aspect are also presented

Echolocation: Using Word-Burst Analysis to Rescore Keyword Search Candidates in Low-Resource Languages

State of the art technologies for speech recognition are very accurate for heavily studied languages like English. They perform poorly, though, for languages wherein the recorded archives of speech data available to researchers are relatively scant. In the context of these low-resource languages, the task of keyword search within recorded speech is formidable. We demonstrate a method that generates more accurate keyword search results on low-resource languages by studying a pattern not exploited by the speech recognizer. The word-burst, or burstiness, pattern is the tendency for word utterances to appear together in bursts as conversational topics fluctuate. We give evidence that the burstiness phenomenon exhibits itself across varied languages. Using burstiness features to train a machine-learning algorithm, we are able to assess the likelihood that a hypothesized keyword location is correct and adjust its confidence score accordingly, yielding improvements in the efficacy of keyword search in low-resource languages

Characterizing the impact of the workload on the value of dynamic resizing in data centers

Energy consumption imposes a significant cost for data centers; yet much of that energy is used to maintain excess service capacity during periods of predictably low load. Resultantly, there has recently been interest in developing designs that allow the service capacity to be dynamically resized to match the current workload. However, there is still much debate about the value of such approaches in real settings. In this paper, we show that the value of dynamic resizing is highly dependent on statistics of the workload process. In particular, both slow time-scale non-stationarities of the workload (e.g., the peak-to-mean ratio) and the fast time-scale stochasticity (e.g., the burstiness of arrivals) play key roles. To illustrate the impact of these factors, we combine optimization-based modeling of the slow time-scale with stochastic modeling of the fast time-scale. Within this framework, we provide both analytic and numerical results characterizing when dynamic resizing does (and does not) provide benefits