A framework for component certification in autonomic systems

  • Ciavaglia L.
  • Sander Spek
  • Vânia Gonçalves

While theory on software certification exists (e.g., see [1] for an overview), little of it has been applied to autonomic systems in the context of Network Management Systems. In this paper, a framework will be provided for component certification for autonomous networks. It has been applied in the UniverSelf Project, featuring a unified management framework (umf) as well as many autonomous mechanisms that can be developed by different parties. The framework uses two variables (offline vs. online certification, self vs. third-party certification) to arrive at four scenarios. Taking into account different degrees of operator trust in autonomics, the framework can assist to the safe and informed implementation and execution of autonomic mechanisms.

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 ...