330 research outputs found
Social network monitoring for bursty cascade detection
Singapore National Research Foundation under International Research Centres in Singapore Funding Initiativ
Excitable human dynamics driven by extrinsic events in massive communities
Using empirical data from a social media site (Twitter) and on trading
volumes of financial securities, we analyze the correlated human activity in
massive social organizations. The activity, typically excited by real-world
events and measured by the occurrence rate of international brand names and
trading volumes, is characterized by intermittent fluctuations with bursts of
high activity separated by quiescent periods. These fluctuations are broadly
distributed with an inverse cubic tail and have long-range temporal
correlations with a power spectrum. We describe the activity by a
stochastic point process and derive the distribution of activity levels from
the corresponding stochastic differential equation. The distribution and the
corresponding power spectrum are fully consistent with the empirical
observations.Comment: 9 pages, 3 figure
The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena
The Internet is the most complex system ever created in human history.
Therefore, its dynamics and traffic unsurprisingly take on a rich variety of
complex dynamics, self-organization, and other phenomena that have been
researched for years. This paper is a review of the complex dynamics of
Internet traffic. Departing from normal treatises, we will take a view from
both the network engineering and physics perspectives showing the strengths and
weaknesses as well as insights of both. In addition, many less covered
phenomena such as traffic oscillations, large-scale effects of worm traffic,
and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex
System
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The Design and Implementation of Low-Latency Prediction Serving Systems
Machine learning is being deployed in a growing number of applications which demand real- time, accurate, and cost-efficient predictions under heavy query load. These applications employ a variety of machine learning frameworks and models, often composing several models within the same application. However, most machine learning frameworks and systems are optimized for model training and not deployment.In this thesis, I discuss three prediction serving systems designed to meet the needs of modern interactive machine learning applications. The key idea in this work is to utilize a decoupled, layered design that interposes systems on top of training frameworks to build low-latency, scalable serving systems. Velox introduced this decoupled architecture to enable fast online learning and model personalization in response to feedback. Clipper generalized this system architecture to be framework-agnostic and introduced a set of optimizations to reduce and bound prediction latency and improve prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. And InferLine provisions and manages the individual stages of prediction pipelines to minimize cost while meeting end-to-end tail latency constraints
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