Hierarchical Caching for Online Video

  • Akhtar S.
  • Beck A.
  • Rimac I.

We survey existing caching algorithms for their suitability to online video in a hierarchical cache. We have proposed a new algorithm based on additional requirements for online video. We propose new evaluation metrics for online video caching such as hit-rate, replacement rate and ability to recover quickly from cache popularity changes. We developed a realistic dynamically changing popularity model expected for online video. Based on this model and the additional criteria, we evaluate the new algorithm's performance against typically deployed algorithms today such as LRU and GDSF as well as algorithms such as a Perfect-LFU and LCD which have shown to produce high performance. We show that the new algorithm performs better than the existing well known algorithms, in terms of hit-rate and replacement with the dynamically changing popularity environment. We also contribute an analytical method to determine hit rate in a LRU based caching hierarchy as well as an estimation of the hit rate with the new algorithm.

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 ...