AidOps: A Data-Driven Management of NFV configurations

  • Arjona Aroca J.
  • Jin Y.
  • Lugones D.

NFV is expected to transform the way telecom operators deploy and manage their networks and services. Yet, network functions are usually deployed on expensive and proprietary appliances that require expertise for deployment and maintenance. In this paper we argue that technological advances in NFV need an equivalent evolution on resource-management capabilities to simplify provi- sioning while increasing operator confidence in delivering appropriate QoS at minimal costs. We propose AidOps, a framework that relies on machine learning to aid NFV orchestration leveraging workload patterns extracted from operator data. Using domain-specific knowledge, AidOps is able to optimize resource utiliza- tion according to operator constrains in terms of stability, capacity and provisioning times. We have evaluated our framework using enterprise communication apps and carrier-class services with real traffic traces.

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 ...