Survivability Modeling and Analysis of Cloud Service in Distributed Data Centers

Analyzing the survivability of a cloud service is critical as the application or service migration from local to cloud is an irresistible trend. However, former research on cloud service or virtual system (VS) availability and/or reliability was only carried out from the perspective of steady state. This paper aims to analyze the survivability of the cloud service after a service breakdown occurrence by presenting a model and the closed-form solutions with the use of continuous-time Markov chain. The service breakdown may be caused by virtual machine (VM) and/or VM monitor (VMM) bugs or software rejuvenation and/or host failures and NAS (Network Area Storage) failures. In order to improve the cloud service survivability, the VS applies two techniques: VM failover and VM live-migration. Through the model proposed and the survivability metrics defined in this paper, we are able to quantitatively assess the system survivability while providing insights into the investment efforts in system recovery strategies. In order to study the impact of key parameters on system survivability, this paper also provides a parameter sensitivity analysis through numerical experiments.