April 07, 2017

Risk Thinking for Cloud-Based Application Services

  • Bauer E.

Enterprises take risks, like developing new product/service offerings and expanding into new markets and sales channels, in pursuit of reward. Many enterprises are moving their applications and IT services to the cloud in pursuit of reduced operational expenses and an accelerated pace of service innovation without compromising user service quality. Better risk management results in fewer operational surprises and failures, greater stakeholder confidence and reduced regulatory concerns; in essence, proactive risk management maximizes the likelihood that an enterprises objectives will be achieved, thereby enabling organizational success. This work methodically considers the risks and opportunities that an enterprise taking their applications or services onto the cloud must consider to obtain the cost reductions and service velocity improvements they desire without suffering the consequences of unacceptable user service quality. The better the risk management that an enterprise has in place, the more risk it can take in pursuit of returns.

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 ...