Future Cognitive Mobile Networks : Connecting Management and Orchestration

  • Gruber M.

The goals when running a mobile network have remained the same over the years, namely reducing CAPEX/OPEX by decreasing the necessary level of manual intervention and increasing the network performance. However, the complexity has dramatically increased: 5G networks will have to be able to deal with dynamic slice management and a multi-cloud environment with flexibly orchestrated resources while guaranteeing challenging latency requirements and seamless mobility. As a response to this, a more intelligent, or cognitive, self-management approach will be necessary where meaningful information, many of which will be based on machine learning, is shared across the mobile network management and orchestration domains. Cognition will be represented by modular, programmable pieces of software that connect both domains in a truly end-to-end and conflict-free manner. This panel will assemble stakeholders from two, so far relatively unrelated, communities: the one working on self-organizing networks (SON) and the other one working on orchestration bringing in additional virtualization aspects.

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 ...