The Community for Technology Leaders
2015 IEEE International Conference on Services Computing (SCC) (2015)
New York City, NY, USA
June 27, 2015 to July 2, 2015
ISBN: 978-1-4673-7280-0
pp: 451-458
Cloud Computing represents a new trend in the development and use of software. Many organizations are currently adopting the use of services that are hosted in the cloud by employing the Software as a Service (SaaS) model. Services are typically accompanied by a Service Level Agreement (SLA), which defines the quality terms that a provider offers to its customers. Many monitoring tools have been proposed to report compliance with the SLA. However, they have some limitations when changes to monitoring requirements must be made and because of the complexity involved in capturing low-level raw data from services at runtime. In this paper, we propose the design of a platform-independent monitoring middleware for cloud services, which supports the monitoring of SLA compliance and provides a report containing SLA violations that may help stakeholders to make decisions regarding how to improve the quality of cloud services. Moreover, our middleware definition is based on the use of models@run.time, which allows the dynamic change of quality requirements and/or the dynamic selection of different metric operationalizations (i.e., Calculation formulas) with which to measure the quality of services. In order to demonstrate the feasibility of our approach, we show the instantiation of the proposed middleware that can be used to monitor services when deployed on the Microsoft Azure© platform.
Monitoring, Middleware, Measurement, Radiation detectors, Engines, Runtime, Software as a service,Models@run.time, Cloud Computing, Software as a Service, Monitoring, Middleware, Quality of Service
Priscila Cedillo, Javier Jimenez-Gomez, Silvia Abrahao, Emilio Insfran, "Towards a Monitoring Middleware for Cloud Services", 2015 IEEE International Conference on Services Computing (SCC), vol. 00, no. , pp. 451-458, 2015, doi:10.1109/SCC.2015.68
96 ms
(Ver 3.3 (11022016))