Running Enterprise Workloads in Google Cloud

Jeff Welsch, product manager at Google Cloud, discusses the opportunity of running enterprise workloads in the cloud, emphasizing that enterprise use cases are substantial for many customers. He outlines Google’s compute organization, which includes offerings such as virtual machines, TPUs, GPUs, block storage, and enterprise solutions like VMware, SAP, and Microsoft. Welsch explains that Google Cloud is focused on optimizing infrastructure to meet customer requirements, especially in light of challenges like increasing compute demands from AI and the plateauing of Moore’s Law. Google Cloud’s approach involves leveraging AI capabilities and modern infrastructure to improve performance, reliability, security, and cost efficiency, while also prioritizing sustainability.

Welsch introduces Google’s Titanium technology, which aims to optimize infrastructure by breaking out of traditional server limitations and disaggregating performance capabilities. Titanium allows for tiered offloading, improving CPU responsiveness and storage performance, as exemplified by the HyperDisk service. He highlights that Titanium enables better optimization and efficiency, providing benefits like reduced latency and improved price performance without requiring customers to consume more resources. Additionally, Titanium supports dynamic resource management, allowing for live migration and non-disruptive maintenance, which enhances the overall reliability and performance of enterprise workloads.

The presentation also covers specific enterprise workloads like Microsoft, VMware, and SAP. Google Cloud offers robust support for Microsoft workloads, with features like cost optimization, live migration, and integration with AI-based modernization tools. For VMware, Google Cloud provides a seamless, integrated experience with the Google Cloud VMware Engine, facilitating easy migration and access to Google Cloud services. SAP workloads benefit from Google Cloud’s memory-optimized instances and tight integration with AI and machine learning capabilities. Welsch concludes by emphasizing Google Cloud’s commitment to optimizing infrastructure to meet the diverse needs of enterprise applications, ensuring performance, reliability, and cost-effectiveness.