Virtualizing Network Services & Cloud Reliability

In a communications network, several physical appliances are used to manage and operate the network. A few examples of these appliances are equipment used as load balancers, firewalls and intrusion detection systems. With the advance of virtualization and cloud computing, these physical appliances are being replaced by virtual servers, in a process called network function virtualization (NFV). Using NFV has several advantages over employing physical appliances: increased modularity, reduced costs and easier scaling. In this talk, I will focus on a related aspect: reliability. Years of research and engineering efforts have been spent to ensure that the physical appliances will perform their duties in a reliable way. Although this work has resulted in high-quality equipment, it is fairly unknown how the same quality and reliability can be achieved using the cloud when virtualizing network functions. To this end, I will cover recent related work in cloud reliability, including some of our studies, identifying root causes and potential improvement areas. I will also discuss some recent efforts to ensure high reliability when deploying applications in the cloud.

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 ...