publication . Conference object . Other literature type . 2019

SD: a Divergence-based Estimation Method for Service Demands in Cloud Systems

Dipietro, S; Casale, G;
Open Access English
  • Published: 11 Jun 2019
  • Country: United Kingdom
Abstract
Estimating performance models parameters of cloud systems presents several challenges due to the distributed nature of the applications, the chains of interactions of requests with architectural nodes, and the parallelism and coordination mechanisms implemented within these systems. In this work, we present a new inference algorithm for model parameters, called state divergence (SD) algorithm, to accurately estimate resource demands in a complex cloud application. Differently from existing approaches, SD attempts to minimize the divergence between observed and modeled marginal state probabilities for individual nodes within an application, therefore requiring th...
Subjects
free text keywords: service demands, inference, cloud, NoSQL database, queueing
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Conference object . 2019
Provider: ZENODO
Zenodo
Other literature type . 2019
Provider: Datacite
Zenodo
Other literature type . 2019
Provider: Datacite
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publication . Conference object . Other literature type . 2019

SD: a Divergence-based Estimation Method for Service Demands in Cloud Systems

Dipietro, S; Casale, G;