Sensitivity analysis of an availability model for disaster tolerant cloud computing system. Issue 6 (18th July 2018)

• Record Type: Journal Article
• Authors: Silva, Bruno; Matos, Rubens; Tavares, Eduardo; Maciel, Paulo; Zimmermann, Armin
• Is Part Of: International journal of network management. Volume 28:Issue 6(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-07-18
• Subjects: Computer networks -- Management -- Periodicals; 004.6
• DOI: 10.1002/nem.2040
• Languages: English
• ISSNs: 1055-7148

Abstract

Summary: Because of the dependence on Internet‐based services, many efforts have been conceived to mitigate the impact of disasters on service provision. In this context, cloud computing has become an interesting alternative for implementing disaster tolerant services due to its resource on‐demand and pay‐as‐you‐go models. This paper proposes a sensitivity analysis approach to assess the parameters that most impact the availability of cloud data centers, taking into account disaster occurrence, hardware and software failures, and disaster recovery mechanisms for cloud systems. The analysis adopts continuous‐time Markov chains, and the results indicate that disaster issues should not be neglected. Hardware failure rate and time for migration of virtual machines (VMs) are the critical factors pointed out for the system modeled in our analysis. Moreover, the location where data centers are placed has a significant impact on system availability, due to time for migrating VMs from a backup server. Abstract : This paper proposes a sensitivity analysis approach to assess the parameters that most impact the availability of cloud data centers, taking into account disaster occurrence, hardware and software failures, and disaster recovery mechanisms for cloud systems. The analysis adopts continuous‐time Markov chains, and the results indicate that disaster issues should not be neglected. Hardware failure rate and time for migration of virtual machines are the critical factors pointed out for the system modeled in our analysis.