Radical Reduction in Computing Costs with AI-powered Predictive Memory Technology
Server memory (DRAM) is one of the biggest cost components in the data center, but it’s common to find that over half of the provisioned memory isn’t actively utilized by applications and can be considered cold. Studies from major cloud providers / hyperscalers including Microsoft Azure, Google Cloud, and Meta have found that utilization for internal and external workloads regularly drops to 50% or below.* This means that much of DRAM is wasted—resulting in massive amounts of completely wasted spend.
In this session, we will delve into how the industry has (unsuccessfully) tried to solve this challenge with approaches like CXL, Optane and others, and the limitations that come with each. We will also explore a novel approach, AI-powered predictive memory, that enables system Flash to appear as DRAM-speed memory—brining Flash into the memory tier and enabling unprecedented cost efficiencies and scope for capacity expansion. We’ll examine how this approach can be applied in any computing environment—on-prem or in the cloud, with any processor, across virtualized / bare metal / containerized environments, with no changes to the OS or applications.
Sources: *https://dl.acm.org/doi/pdf/10.1145/3578338.3593553 & https://www.datacenterdynamics.com/en/news/only-13-of-provisioned-cpus-and-20-of-memory-utilized-in-cloud-computing-report/
