Name
Abstract | ||
|---|---|---|
Given the elasticity, dynamicity and on-demand nature of the cloud, cloud-based applications require dynamic models for Quality of Service (QoS), especially when the sensitivity of QoS tends to fluctuate at runtime. These models can be autonomically used by the cloud-based application to correctly self-adapt its QoS provision. We present a novel dynamic and self-adaptive sensitivity-aware QoS modeling approach, which is fine-grained and grounded on sound machine learning techniques. In particular, we combine symmetric uncertainty with two training techniques: Auto-Regressive Moving Average with eXogenous inputs model (ARMAX) and Artificial Neural Network (ANN) to reach two formulations of the model. We describe a middleware for implementing the approach. We experimentally evaluate the effectiveness of our models using the RUBiS benchmark and the FIFA 1998 workload trends. The results show that our modeling approach is effective and the resulting models produce better accuracy when compared with conventional models. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SEAMS.2013.6595491 | SEAMS |
Keywords | Field | DocType |
sensitivity,novel dynamic,exogenous inputs model,self-adaptive qos modeling,cloud-based application,modeling approach,learning (artificial intelligence),quality of service,rubis benchmark,prediction,autoregressive moving average processes,qos modeling,dynamic model,machine learning technique,qos provision,sensitivity-aware qos modeling,ann,armax model,artificial neural network,autoregressive moving average with exogenous inputs model,self-adaptive sensitivity-aware qos modeling,cloud computing,machine learning,fifa 1998 workload trend,interference,neural nets,conventional model,learning artificial intelligence,accuracy,data models,uncertainty | Middleware,Data modeling,Workload,Computer science,Quality of service,Real-time computing,Software,Artificial neural network,Moving average,Distributed computing,Cloud computing | Conference |
ISSN | ISBN | Citations |
2157-2305 | 978-1-4799-0344-3 | 18 |
PageRank | References | Authors |
0.83 | 17 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Chen | 1 | 599 | 29.93 |
| Rami Bahsoon | 2 | 534 | 60.22 |