Translation of application-level terms to resource-level attributes across the Cloud stack layers

• Published in: 2011 IEEE Symposium on Computers and Communications (ISCC) pp. 153 - 160
• Main Authors: Kousiouris, G., Kyriazis, D., Gogouvitis, S., Menychtas, A., Konstanteli, K., Varvarigou, T.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2011
• Subjects: Artificial neural networks; Cloud computing; Estimation; Hardware; Mathematical model; performance estimation; Quality of service; Real time systems; real-time Clouds; SLA translation; Software
• ISBN: 9781457706806; 1457706806
• ISSN: 1530-1346
• DOI: 10.1109/ISCC.2011.5984009

The emergence of new environments such as Cloud computing highlighted new challenges in traditional fields like performance estimation. Most of the current cloud environments follow the Software, Platform, Infrastructure service model in order to map discrete roles / providers according to the offering in each "layer". However, the limited amount of information passed from one layer to the other has raised the level of difficulty in translating user-understandable application terms from the Software layer to resource specific attributes, which can be used to manage resources in the Platform and Infrastructure layers. In this paper, a generic black box approach, based on Artificial Neural Networks is used in order to perform the aforementioned translation. The efficiency of the approach is presented and validated through different application scenarios (namely FFMPEG encoding and real-time interactive e-Learning) that highlight its applicability even in cases where accurate performance estimation is critical, as in cloud environments aiming to facilitate real-time and interactivity.