Predictive Capacity Modeling For Multi-Generation Cloud Fleets: A Data-Driven Approach To Infrastructure Optimization

Abstract

Multi-generation cloud fleet predictive capacity modeling is a radical change in the behavior of hyperscale infrastructure providers in the context of a heterogeneous hardware environment, with respect to their ability to manage computational resources. Conventional reactive and static capacity planning tools have inherent shortcomings in their use with current cloud systems, where virtual machines are of different families, using mixed hardware generations, and where customer migrations are multifaceted. These traditional methods may struggle to respond to the changes in workload with the speed of their provisioning response times, leading to sustained performance impairments during periods of demand change or unnecessary over-allocation during periods of low utilization. The predictive capacity modeling addresses these limitations by integrating multi-dimensional signals, machine learning, and future-oriented demand prediction, which can be used to provide resources proactively in accordance with the projected workload trends. Combining technical telemetry, data on workload characterization, and operational measures in the form of neural network models provides predictions that are superior to the more conventional statistical methods. Cross-generational demand modeling, which takes into account the power management issues, virtualization dynamics, and migration patterns, allows optimal capacity allocation on different hardware platforms. The closed-loop predictive systems are directly fed into capital allocation and pricing, service lifecycle, and product management, which changes the capacity from an operational consideration to a strategic data-driven asset that increases the efficiency of the fleet utilization without reducing the reliability of the service provision.