Model-based analysis of data center tiers: a reliability and availability perspective
TL;DR
A hierarchical and heterogeneous modeling approach that adopts reliability block diagrams with stochastic petri nets with stochastic petri nets to represent redundant data center infrastructures shows that the subsystem most impacting system availability is the cooling subsystem.
Abstract
Data centers (DCs) have become a fundamental infrastructure in people’s daily lives, providing remote data services for business, entertainment, and many other human needs, such as healthcare, smart cities, and smart homes. These large data centers must meet several dependability requirements to ensure quality of service with high reliability and availability. Data centers have three main infrastructures: IT (Information Technology), electrical, and cooling. Therefore, for data centers to achieve high availability, these infrastructures must be designed according to redundancy specifications ranging from Tier I to Tier IV. In this paper, we adopt a hierarchical and heterogeneous modeling approach that adopts reliability block diagrams (RBDs) with stochastic petri nets (SPNs) to represent these redundant data center infrastructures. We performed an evaluation focusing on key metrics, including availability, reliability, downtime, and uptime, as well as perform a sensitivity analysis of the constructed models to verify which subsystem most impacts the system’s behavior. Finally, we observe that the values obtained for the evaluated metrics using the proposed models fall within the reference parameters, indicating that real-world scenarios were successfully represented in the tier analysis. Furthermore, the sensitivity analysis showed that the subsystem most impacting system availability is the cooling subsystem.