Author Archives: Giuliano Casale

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.

Paper accepted in IEEE TRel

The paper “LINE : A Scalable Tool for Evaluating Software Applications in Unreliable Environments“, co-authored by J.F.Peréz and G.Casale, has been accepted for inclusion in an upcoming issue of  IEEE Transactions on Reliability!

If you want to use the techniques presented in the paper, please download the latest release of the LINE tool.

Paper accepted at IFIP/IEEE IM’17

The paper “Energy-Efficient Resource Allocation and Provisioning for InMemory Database Clusters” coauthored by K. Molka and G. Casale has been accepted for IFIP/IEEE IM 2017!

The paper looks at consolidaton of SAP HANA clusters and defines a mixed-integer nonlinear optimization problem for dealing with resource allocation and server assignment. The solution approach combines genetic algorithms and a modified best-fit-decreasing heuristic for finding a near-optimal solution.