For many applications with inter-datacenter cloud deployments it is important to rely on an accurate model of delay times across different geolocations. Unfortunately, such a model is currently not available to researchers and practitioners. To fill that gap, this letter presents a thorough analysis of real-life latency values collected between different Amazon datacenter locations, and proposes a novel Gaussian mixture approximation model of the round-trip time distribution based on the relaxed boxed approximation algorithm. The proposed model can be effectively used for the emulation/simulation of cross-cloud application and service deployments.

Estimating delay times between cloud datacenters: A pragmatic modeling approach

Cerroni, Walter;Foschini, Luca;
2018

Abstract

For many applications with inter-datacenter cloud deployments it is important to rely on an accurate model of delay times across different geolocations. Unfortunately, such a model is currently not available to researchers and practitioners. To fill that gap, this letter presents a thorough analysis of real-life latency values collected between different Amazon datacenter locations, and proposes a novel Gaussian mixture approximation model of the round-trip time distribution based on the relaxed boxed approximation algorithm. The proposed model can be effectively used for the emulation/simulation of cross-cloud application and service deployments.
2018
Cerroni, Walter*; Foschini, Luca; Grabarnik, Genady Ya.; Shwartz, Larisa; Tortonesi, Mauro
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/631915
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 9
social impact