discussion of the underlying mathematical and statistical properties of self-similarity and their relationship with actual network behavior. We also present traffic models based on self-similar stochastic processes that provide simple, accurate, and realistic descriptions of traffic scenarios expected during

consumption tends to vary significantly with the actual computing activity. Effective power provisioning strategies are needed to determine how much computing equipment can be safely and efficiently hosted within a given power budget. In this paper we present the aggregate power usage characteristics of large

relaxation of certain traditional database research goals in the pursuit of scalability and widespread adoption. We present simulation results showing PIER gracefully running relational queries across thousands of machines, and show results from the same software base in actual deployment on a large

rough cost estimates of each technology and supplier, a more focused look at the actual or possible implementations in the market with rough estimates of the systems ' economics in each implementation. Following this is a discussion of notable dynamics and potentially effective strategies, based

are actually deployed. This paper proposes a more realistic model for such random networks and describes various scenarios which can be more accurately represented. The approach described here can be developed to provide more refined models in the future, and the source code of an implementation is freely

a virtual deployment before an actual deployment, provide a decision builder tool (i.e., designed to compare different architectures or buy new resource), take into account the platform scalability. Using this modeling, it is possible to determine the bottleneck of the platform and thus to know

Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general

is concentrated in a smaller area and takes place in locations where motorists are accustomed to a greater degree of mobility. In 2001, more than 1000 fatalities occurred in work zones, with many occurring in the transition area leading up to the actual construction site. Just as ITS is being applied to relieve

-of-the-envelope calculations, or costly field deployments; they need better ways to evaluate the effects of such hypothetical “what-if ” questions before the actual deployments. This paper presents What-If Scenario Evaluator (WISE), a tool that predicts the effects of possible configuration and deployment changes in content

Network research often involves the evaluation of new application designs, system architectures, and protocol implementations. Due to the immense scale of the Internet, deploying an Internet-wide system for the purpose of experimental study is nearly impossible. Instead, researchers evaluate