Examining the Economic and Business Impact of Cloud-Based Disaster Recovery

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Authors: Somaning Turwale
Cited by: 0

Abstract

Cloud-based disaster recovery has emerged as a transformative alternative to conventional infrastructure protection strategies. Traditional disaster recovery models demanded substantial capital investments in redundant physical facilities. Secondary data centers remained idle during normal operations while consuming significant resources. The shift toward cloud-native recovery architectures addresses fundamental inefficiencies inherent in legacy protection frameworks. Consumption-based pricing models eliminate upfront hardware procurement requirements. Operational expenses replace capital expenditures through pay-per-use service arrangements. Site reliability engineering practices allow for automation in failure detection and response. Structures that can be allotted geographically throughout different availability zones can resist a regional disruption. Non-stop replication of statistics enables in reduction of the threat of information loss for the duration of a failover situation. Resource provisioning that is elastic can be compared to a dynamic capacity which is changing along with the business requirements. Scaling operations proceed without procurement delays or fixed capacity constraints. Resource scheduling algorithms optimize workload distribution across available infrastructure. Investment evaluation requires consideration of direct cost savings alongside risk mitigation value and strategic benefits. Decision support frameworks assist organizations in navigating adoption complexity through structured assessment mechanisms. The economic advantages of cloud-based disaster recovery extend beyond simple cost reduction to encompass improved organizational agility and enhanced competitive positioning.

Fields of Study & Subjects

Cloud Computing and Resource Management Distributed systems and fault tolerance Cloud Data Security Solutions

Publication details

Journal / source
Journal of Information Systems Engineering & Management
Volume
11
Issue
2s
Pages
109–116
Publication date
13 February 2026
Language
EN