Multiple-criteria Evaluation of Disaster Recovery Strategies Based on Stochastic Models

Abstract

The consequences for a company losing its data or having its IT system disrupted are severe and can impact negatively on business operations. It can also cause customer dissatisfaction and subsequent revenue loss. In a competitive global market, companies have been adopting disaster recovery (DR) strategies as an attempt to keep IT systems operational, prevent data loss, and ensure business continuity. However, there is not a single DR strategy that meets the requirements of every business (e.g., availability and cost). Besides, most of the time, these requirements are conflicting. Therefore, efficient and accurate analysis of DR strategies before its deployment is crucial to choose the best strategy that suits companies’ needs and budget. In this paper, we propose the adoption of a multiple-criteria decision-making (MCDM) method and stochastic models to evaluate and rank DR strategies for IT infrastructures. The stochastic models are used for quantitative assessing distinct DR strategies regarding five DR key-metrics: availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO), and cost. We also use an MCDM method to rank the strategies according to multiple criteria (e.g., availability maximization and costs minimization). A case study demonstrates the feasibility and usefulness of the proposed approach for finding the best DR strategies according to multiple criteria.