Computationally Efficient CP Tensor Decomposition Update Framework for Emerging Component Discovery in Streaming Data
September 25, 2018
We present streaming CP update, an algorithmic framework for updating CP tensor decompositions that possesses the capability of identifying emerging components and can produce decompositions of large, sparse tensors streaming along multiple modes at a low computational cost. We discuss a large-scale implementation of the proposed scheme integrated within the ENSIGN tensor analysis package, and we evaluate and demonstrate the performance of the framework, in terms of computational efficiency and capability to discover emerging components, on a real cyber dataset.
September 25, 2018
Multiresolution priority queues are data structures recently discovered by Reservoir Labs that reduce the entropy of some critical graph algorithms—such as Dijkstra’s or Prim’s algorithms—and deliver new lower computational complexity bounds. These new data structures are capable of exploiting the multiresolution properties of discrete algorithms, a characteristic that has been otherwise overlooked in the field of graph algorithms. Similar to the concept of resolution found in signal processing—by which a signal can be undersampled while information loss is zero or very small—graphs’ entropy tends to be concentrated in regions that can be efficiently exploited by multiresolution data structures. In this approach, a small controllable bounded discrete error is introduced in a way that entropy is substantially reduced, resulting in new lower computational complexity algorithms.
While the fastest currently known graph algorithms provide exact solutions at the expense of incurring high computational costs, a multiresolution graph algorithm is capable of softening graph problems and breaking their current information theoretic barriers, introducing a small amount of controlled error in a way that the problem’s entropy is reduced. As a result, a new class of higher performance graph algorithms is enabled, enabling the solution of previously deemed intractable problems by identifying solutions that are close to optimal and within a known bounded error.
September 25, 2018
As the scale of unlabeled data rises, it becomes increasingly valuable to perform scalable, unsupervised data analysis. Tensor decompositions, which have been empirically successful at finding meaningful cross-dimensional patterns in multidimensional data, are a natural candidate to test for scalability and meaningful pattern discovery in these massive real-world datasets. Furthermore, the production of big data of different types necessitates the ability to mine patterns across disparate sources. The coupled tensor decomposition framework captures this idea by decomposing several tensors from different data sources together. We present a scalable implementation of coupled tensor decomposition on Apache Spark. We introduce nonnegativity and sparsity constraints, and perform all-at-once quasi-Newton optimization of all factor matrix parameters. We present results showing the billion-scale scalability of this novel implementation and also demonstrate the high level of interpretability in the components produced, suggesting that coupled, all-at-once tensor decompositions on Apache Spark represent a promising framework for large-scale, unsupervised pattern discovery.
June 19, 2018
In this paper we introduce a new framework to detect elephant flows at very high speed rates and under uncertainty. The framework provides exact mathematical formulas to compute the detection likelihood and introduces a new flow reconstruction lemma under partial information. These theoretical results lead to the design of BubbleCache, a new elephant flow detection algorithm designed to operate near the optimal tradeoff between computational scalability and accuracy by dynamically tracking the traffic’s natural cutoff sampling rate. We demonstrate on a real world 100 Gbps network that the BubbleCache algorithm helps reduce the computational cost by a factor of 1000 and the memory requirements by a factor of 100 while detecting the top flows on the network with very high probability.
June 18, 2018
Heavy-hitter flows or Cheetah Flows (CF), which are high-rate flows can result in increased packet losses and delay in general Internet traffic. A Cheetah Flow Traffic Engineering System (CFTES) is presented, which can dynamically compute a heavy-hitter or CF threshold using information from the general background traffic. The system works in conjunction with a Cheetah Flow Identification Network Function (CFINF) to detect CFs at high-link rates using an SDN controller for actions involving redirection of CFs to a lower priority scavenger queue.
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