On Machine Learning for Cloud Resource Management

  • Cello M.
  • Lugones D.

The dynamicity and heterogeneity of cloud resources require significant expertise and manual labour to properly configure these resources for delivering acceptable quality of service levels to the end users. Cloud resource configuration and orchestration is, in facts, challenging because it requires dynamic coordination across different infrastructure layers. For this reason, we need to automate the whole resource management process and Machine Learning (ML) tools have the potential to facilitate such automation. Moreover, future cloud infrastructure will be more and more dynamic and heterogeneous and for this reason we need ML algorithms able to dynamically adapt and change their parameters according to the new data. In this position paper we classify and analyses the more suitable ML algorithms that fit in such highly-heterogeneous and high-dynamic scenario.

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 ...