Commuter-Adjusted Services Cloud

  • Brown J.
  • Gardell P.
  • Wilkin G.

What is a Commuter-Adjusted population? For the purposes of this description we will describe it as “Large amounts of people and things moving in a regular pattern, usually linked to work activities”1. This Commuter-Adjusted population, shifts by as much as 78% in large metropolitan areas including New York City, District of Columbia, London, Paris, and Amsterdam. Massive movements of people place significant demands on service provider networks . Typical adjustments occur when the named “Daytime Population”1, that we will call the daily commuter, is flocking into the larger metropolitan areas for work. Figure 1 below shows the population change across some of the larger cities within the United States with the type of workers commuting.

Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

  • Francini A.
  • Miller R.
  • Sharma S.

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...