Conference paper Open Access

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

Salvatore Dipietro; Giuliano Casale

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 the availability of probabilistic measures from both the system and the underpinning model. 

Validation against a case study using the Apache Cassandra NoSQL database and random experiments show that SD can accurately predict demands and improve system behavior modeling and prediction.

Files (34.2 kB)
Name Size
SD-ficloud2019.zip
md5:a2a3151bf50eb160e16bf3f0b9d0bd36
34.2 kB Download
11
0
views
downloads
All versions This version
Views 1111
Downloads 00
Data volume 0 Bytes0 Bytes
Unique views 1010
Unique downloads 00

Share

Cite as