Author Archives: Giuliano Casale

TNSM Special Issue on Big Data Analytics for Management 2019

I am co-organizer of an upcoming special issue on Novel Techniques in Big Data Analytics for Management to appear in IEEE Transactions on Network and Service Management in 2019.  The objective of the special issue is to attract the latest development in applications of analytics, machine learning and AI to management.

Important dates:

  • Paper submission date: November 15, 2018
  • Review results returned: February 15, 2019
  • Revision submission: March 15, 2019
  • Final acceptance notification: June 15, 2019
  • Final paper submission: July 7, 2019
  • Publication date (tentative): September 2019

Paper accepted in IEEE CLOUD

The paper “How to Supercharge the Amazon T2: Observations and Suggestions” by Jiawei Wen, Lihua Ren, Feng Yan, Daniel J. Dubois, Giuliano Casale, and Evgenia Smirni has been accepted for IEEE CLOUD 2017. The paper proposes a methodology to increase the performance benefits of T2 instances on Amazon EC2.

JMT 1.0.0 Released!

Download: http://sourceforge.net/projects/jmt/files/jmt/JMT-1.0.0/JMT-installer-1.0.0.jar/download

JMT now supports simulation of generalized stochastic Petri nets and queueing Petri nets!

Summary of changes – JSIMgraph, JSIMwiz:

  • Petri net components: Place and Transition (see JMT user manual for more details)
  • Added some examples of Petri nets and hybrid models in the JMT working folder
  • Enabled group constraints in Finite Capacity Regions
  • Fixed miscellaneous bugs

Paper accepted at Sigmetrics’17

My paper Accelerating Performance Inference over Closed Systems by Asymptotic Methods has been accepted for presentation at ACM Sigmetrics’17 as a full paper!

The paper investigates the computation of likelihoods in closed multi-class queueing networks. Novel exact and asymptotic approximations for the normalizing constant of state probabilities are derived and shown to address computational limitations of prior art.