Large-Scale Simulator for Global Data Infrastructure Optimization

Abstract

IT infrastructures in global corporations are appropriately compared with nervous systems, in which body parts (interconnected datacenters) exchange signals (request responses) in order to coordinate actions (data visualization and manipulation). A priori inoffensive perturbations in the operation of the system or the elements composing the infrastructure can lead to catastrophic consequences. Downtime disables the capability of clients reaching the latest versions of the data and/or propagating their individual contributions to other clients, potentially costing millions of dollars to the organization affected. The imperative need of guaranteeing the proper functioning of the system not only forces to pay particular attention to network outages, hot-objects or application defects, but also slows down the deployment of new capabilities, features and equipment upgrades. Under these circumstances, decision cycles for these modifications can be extremely conservative, and be prolonged for years, involving multiple authorities across departments of the organization. Frequently, the solutions adopted are years behind state-of-the art technologies or phased out compared to leading research on the IT infrastructure field. In this paper, the utilization of a large-scale data infrastructure simulator is proposed, in order to evaluate the impact of what if scenarios on the performance, availability and reliability of the system. The goal is to provide data center operators a tool that allows understanding and predicting the consequences of the deployment of new network topologies, hardware configurations or software applications in a global data infrastructure, without affecting the service. The simulator was constructed using a multi-layered approach, providing a granularity down to the individual server component and client action, and was validated against a downscaled version of the data infrastructure of a Fortune 500 company.