October 08, 2017

Augmenting Practical Cross-layer MAC Schedulers via Offline Reinforcement Learning

  • Peter Danielsen
  • Pianese F.

An automated offline design process for optimized cross-layer schedulers can produce augmented scheduling algorithms tailored to a target deployment scenario. We discuss the application of ODDS, a reinforcement learning technique we introduced, for augmenting LTE MAC scheduler algorithms of practical significance. ODDS observes the correlation between the value of a utility function and the parameters applied by an instrumented baseline scheduler, selecting the best variable ranges and sets of parameters via an offline Monte Carlo exploration of the problem space. The result of the ODDS process is a compact definition of a scheduling policy that has been optimized for a target scenario and utility function. In this paper we instrument a production scheduler definition to evaluate the potential of augmented schedulers in practical applications, and experiment with awareness to traffic classes by using a multi-class utility function, yielding scheduling policies that behave differently depending on the properties of individual traffic flows.

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 ...