April 01, 2017

Coding for Distributed Fog Computing

  • Avestimehr S.
  • Li S.
  • Maddahali M.

Redundancy is abundant in Fog networks (i.e., many computing and storage points) and grows linearly with network size. We demonstrate the transformational role of coding in Fog computing for leveraging such redundancy to substantially reduce the bandwidth consumption and latency of computing. In particular, we discuss two recently proposed coding concepts, namely Minimum Bandwidth Codes and Minimum Latency Codes, and illustrate their impacts in Fog computing. We also review a unified coding framework that includes the above two coding techniques as special cases, and enables a tradeoff between computation latency and communication load to optimize system performance. At the end, we will discuss several open problems and future research directions.

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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 ...