Title :
Quantifying Resiliency of IaaS Cloud
Author :
Ghosh, Rahul ; Longo, Federica ; Naik, Vijay K. ; Trivedi, Kishor S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
fDate :
Oct. 31 2010-Nov. 3 2010
Abstract :
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.
Keywords :
Internet; software architecture; stochastic processes; IaaS cloud; cloud based services; resiliency quantification; stochastic reward net based model; Analytical models; Clouds; Computational modeling; Delay; Markov processes; Steady-state;
Conference_Titel :
Reliable Distributed Systems, 2010 29th IEEE Symposium on
Conference_Location :
New Delhi
Print_ISBN :
978-0-7695-4250-8
DOI :
10.1109/SRDS.2010.49