@article {kocsis-ijcc-2013,
   author = { Imre Kocsis and András Pataricza and Zoltán Micskei and András Kövi and Zsolt Kocsis},
   title = {Analytics of resource transients in cloud-based applications},
   journal = {Int. J. of Cloud Computing},
   publisher = {Inderscience},
   pages = {191-212},
   volume = {2},
   issue = {2/3},
   doi = {10.1504/IJCC.2013.055267},
   abstract = {Guaranteeing QoS of services deployed in clouds is a key issue in cloud environments. Cloud services rely heavily on sharing resources between tenants; users have only partial knowledge and control of them. Limited observability and controllability make guaranteeing QoS a challenging task. Many important classic approaches to QoS assurance cannot be adopted, as for a tenant resource arbitration is a black box. Unexpected changes in resource allowance and characteristics introduce novel risks for demanding applications, most importantly soft real-time ones. We introduce the concept of application-integrated early warning sensors - 'mystery shoppers' - to increase the observability of the platform performability state. Mystery shoppers are special, continuously running benchmarks that run as low footprint applications. They deliver fine-grained reports by simulating application resource usage and measuring platform characteristics. This enables inferring the hidden resource sharing characteristics of public clouds. The derived metrics can aid deployment planning and control application-level fault tolerance mechanisms.},
   year = {2013}
}