Programmable switches based on the Protocol Independent Switch Architecture (PISA) have greatly enhanced the flexibility of today's networks by allowing new packet protocols to be deployed without any hardware changes. They have also been instrumental in enabling a new computing paradigm in which parts of an application's logic run within the network core (in-network computing). The characteristics and requirements of in-network applications, however, are quite different from those of packet protocols for which programmable switches were originally designed. Packet protocols are typically stateless, while in-network applications require frequent operations on shared state maintained in the switch. This mismatch increases the developing complexity of in-network computing and hampers widespread adoption. In this paper, we describe the key obstacles to developing innetwork applications on PISA and propose rethinking the current switch architecture. Rather than changing the existing architecture, we propose augmenting it with a Stateful Data Plane (SDP). The SDP supports the requirements of stateful applications, while the conventional data plane (CDP) performs packet-protocol functions.
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Recording sensor data is seldom a perfect process. Failures in power, communication or storage can leave occasional blocks of data missing, affecting not only real-time monitoring but also compromising the quality of near- and off-line data analysis. Several recovery (imputation) algorithms have been proposed to replace missing blocks. Unfortunately, little is known about their relative performance, as existing comparisons are limited to either a small subset of relevant algorithms or to very few datasets or often both. Drawing general conclusions in this case remains a challenge. In this paper, we empirically compare twelve recovery algorithms using a novel benchmark. All but two of the algorithms were re-implemented in a uniform test environment. The benchmark gathers ten different datasets, which collectively represent a broad range of applications. Our benchmark allows us to fairly evaluate the strengths and weaknesses of each approach, and to recommend the best technique on a use-case basis. It also allows us to identify the limitations of the current body of algorithms and suggest future research directions.
Financial time series streams are watched closely by millions of traders. What exactly do they look for and how can we help them do it faster? Physicists study the time series emerging from their sensors. The same question holds for them. Musicians produce time series. Consumers may want to compare them. This tutorial presents techniques and case studies for four problems: 1. Finding sliding window correlations in financial, physical, and other applications. 2. Discovering bursts in large sensor data of gamma rays. 3. Matching hums to recorded music, even when people don't hum well. 4. Maintaining and manipulating time-ordered data in a database setting. This tutorial draws mostly from the book High Performance Discovery in Time Series: techniques and case studies, Springer-Verlag 2004. You can find the power point slides for this tutorial at http://cs.nyu.edu/cs/faculty/shasha/papers/sigmod04.ppt.The tutorial is aimed at researchers in streams, data mining, and scientific computing. Its applications should interest anyone who works with scientists or financial "quants." The emphasis will be on recent results and open problems. This is a ripe area for further advance.
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