Francesco Longo, Rahul Ghosh, et al.
DSN 2011
Cloud based services may experience changes - internal, external, large, small - at any time. Predicting and quantifying the effects on the quality-of-service during and after a change are important in the resiliency assessment of a cloud based service. In this paper, we quantify the resiliency of infrastructure-as-a-service (IaaS) cloud when subject to changes in demand and available capacity. Using a stochastic reward net based model for provisioning and servicing requests in a IaaS cloud, we quantify the resiliency of IaaS cloud w.r.t. two key performance measures - job rejection rate and provisioning response delay. © 2010 IEEE.
Francesco Longo, Rahul Ghosh, et al.
DSN 2011
Vijay K. Naik, Sanjeev K. Setia, et al.
Journal of Parallel and Distributed Computing
Francesco Longo, Dario Bruneo, et al.
IJHPCN
Vijay K. Naik, Pawel Garbacki, et al.
ICPADS 2006