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Network functions virtualization (NFV) together with software-defined networking (SDN) has the potential to help operators satisfy tight service level agreements, accurately monitor and manipulate network traffic, and minimize operating expenses. However, in scenarios that require packet processing to be redistributed across a collection of network function(More)
In this paper, we present a Bayesian framework for multilabel classification using compressed sensing. The key idea in compressed sensing for multilabel classification is to first project the label vector to a lower dimensional space using a random transformation and then learn regression functions over these projections. Our approach considers both of(More)
Frequent episode discovery is a popular framework for temporal pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Currently algorithms exist for episode discovery only when the associated partial order is total order (serial episode) or trivial (parallel episode). In this paper,(More)
— We present Footprint, a system for delivering online services in the " integrated " setting, where the same provider operates multiple elements of the infrastructure (e.g., proxies, data centers, and the wide area network). Such integration can boost system efficiency and performance by finely modulating how traffic enters and traverses the(More)
Networks employ complex, and hence error-prone, routing control plane configurations. In many cases, the impact of errors manifests only under failures and leads to devastating effects. Thus, it is important to proactively verify control plane behavior under arbitrary link failures. State-of-the-art verifiers are either too slow or impractical to use for(More)
Recent work has made the case for geo-distributed analytics, where data collected and stored at multiple datacenters and edge sites worldwide is analyzed in situ to drive operational and management decisions. A key issue in such systems is ensuring low response times for analytics queries issued against geo-distributed data. A central determinant of(More)
— We present Footprint, a system for delivering online services in the " integrated " setting, where the same provider operates multiple elements of the infrastructure (e.g., proxies, data centers, and the wide area network). Such integration can boost system efficiency and performance by finely modulating how traffic enters and traverses the(More)
Recent work has made the case for geo-distributed analytics, where data collected and stored at multiple datacenters and edge sites worldwide is analyzed in situ to drive operational and management decisions. A key issue in such systems is ensuring low response times for analytics queries issued against geo-distributed data. A central determinant of(More)
In this paper, we consider the problem of estimating self-tuning his-tograms using query workloads. To this end, we propose a general learning theoretic formulation. Specifically, we use query feedback from a workload as training data to estimate a histogram with a small memory footprint that minimizes the expected error on future queries. Our formulation(More)
We address the problem of finding patterns from multi-neuronal spike trains that give us insights into the multi-neuronal codes used in the brain and help us design better brain computer interfaces. We focus on the synchronous firings of groups of neurons as these have been shown to play a major role in coding and communication ([6]). With large electrode(More)
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