Now we’re all Cloud Natives, what’s next? Andrew Randall, Principal PM Manager, Microsoft Christopher Liljenstolpe, Heretical Product Manager, Cisco 11 September 2023 @ahrkrak @liljenstolpe hachyderm.io/@ahrkrak hachyderm.io/@liljenstolpe

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This wasn’t always the way 😲

Technology Diffusion (Rogers & Moore)

Types of Innovation (Christensen) Sustaining Incremental improvement on existing product Extends an existing market Disruptive Challenges existing business model Lower-end, lower-cost product and/or creates entirely new market

The Road to Cloud Native IaaS AWS EC2 Open Source PaaS CloudFoundry PaaS Heroku 2010 2006 2009 2010 2011 Cloud | Ubiquitous broadband + mobile Open Source IaaS OpenStack & CloudStack Virtualization VMware Open Source Virtualization Xen, KVM 2005 2001 2004 2006 Virtualization, Growth of Internet 2005 Zones Solaris 10 2000 Jails FreeBSD 4.0 Non-virtualized Hardware, Early Internet IBM PC + MS-DOS Linux Sun-3 SunOS 2.0 2000 1981 1985 1991 1995 World Wide Web Netscape, Windows 95 1995 Java NSFNet !AUP 1992

The Cloud Native Era Cloud Native Maturation | Ubiquitous digitalization 2016 eBPF+XDP Cilium 2016 2017 Service Mesh Linkerd, Istio Serverless Wasm Cloudflare Workers 2018 2020 Wasm in K8s Krustlet 2017 Web Assembly WASM/WASI Serverless K8s Knative 2018 2018 Arm64 IaaS Graviton Standardized Observability OpenTelemetry 2019 2015 Package Management Helm 2020 Containers Docker, CoreOS+etcd 2013 Orchestration Mesos[phere], Kubernetes 2015 2014 Serverless Lambda Cloud Native Foundations | 4G 2015 Foundation CNCF 2015 2013

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“Kubernetes is a reflection of solving problems for people” - Kris Nóva, in Cloud Native Rejekts Podcast

Source: Gartner, Hype Cycle for Emerging Technologie, August 2023

Three Themes Artificial Intelligence Pervasive Cloud Developer Experience

Artificial Intelligence

AI impact on productivity 0.3% 55% Annual productivity growth from 1850 to 1910 46% Faster coding Code written Watt double-acting steam engine, built 1832 $1.5tn Estimated GDP impact

AI impact on happiness 59% 60% Less frustrated when coding More fulfilled with my job 74% Focus on more satisfying work “(With Copilot) I have to think less, and when I have to think it's the fun stuff. It sets off a little spark that makes coding more fun and more efficient.” — Senior Software Engineer 73% More in the flow Source: A Year In, GitHub Measures AI-Based Copilot's Productivity Boost -- Visual Studio Magazine

AI impact on Cloud Native? https://kubernetes.io/case-studies/openai/ https://cloudblogs.microsoft.com/opensource/2018/01/22/openai-masters-scale-kubernetes-azure https://openai.com/research/scaling-kubernetes-to-7500-nodes Cloud Native as a platform for AI workloads • Drives demand for GPU capacity, scale, efficiency • E.g. OpenAI scaled up to 7,500 K8s nodes per cluster in Azure Enable AI-powered applications • Innovation • Competitiveness Simplified operations ● Improve operator satisfaction Enabler of scale, efficiency and optimized architectures ● Smarter, real-time optimization decisions

Demo Time

kubectl-ai https://github.com/sozercan/kubectl-ai Kube Copilot https://github.com/feiskyer/kube-copilot

Some Cautions Some aspects of AI (such as LLM training) is incredibly resource consumptive • In some cases rack power densities are increasing by an order of magnitude in the space of 2-3 years. • This, and the matching cooling load, will collide immediately with ESG goals. CUDA is, for all intents and purposes, a single vendor API • The vast majority of software requires CUDA • We are possibly walking into another single vendor API monoculture. • This Is Not The Way

Pervasive Cloud

Evolution of Cloud Locality Public cloud (continental regions, e.g. us-east-1) Regional cloud Sovereign clouds Multi-cloud Industry clouds Edge and IOT On-premises Public cloud Space

Pervasive cloud implies heterogeneous compute architectures (which will continue to evolve) CPU/GPU arch. True run anywhere Loosely coupled Constrained environments Lightweight functions close to data

We’re not in Seattle any more, Toto This isn’t the infinitely elastic hyperscale world in which Cloud Native was born and grew up Many assumptions will change

Evolution of unit of code deployment Binary package manually deployed in a machine of unknown state Desktop / pet server VM image Data Center / IaaS (+) Container Cloud Native (+) Wasm Module Pervasive Cloud (+)

“If WASM+WASI existed in 2008, we wouldn’t have needed to create Docker. That’s how important it is. Webassembly on the server is the future of computing.” - Solomon Hykes co-founder, Docker

Developer Experience

Modernizing Software Development is Key to Digital Transformation Source: Adapted from Gartner Predicts 2022 and Gartner Future of Cloud 2027 2020 Drivers 2025 60% of new event-driven apps Serverless computing is niche Re-architecting applications to be cloud native Serverless goes mainstream due to elasticity & low ops overhead 75% of orgs with platform teams Disjointed dev tools create a bumpy road to production Increased focus on developer experience Self-service developer portals create paved road to prod 30% of large enterprises Locally installed IDEs Anywhere access to development workspaces Browser-based IDEs (e.g. GitHub Codespaces, Gitpod) 60% of organizations Development environments assumed safe, are unprotected Supply chain security risks Securing development environments is a key priority 70% of new cloud-native apps Monitoring via proprietary SDKs and agents Developer-centric observability practices White-box observability using OpenTelemetry instrumentation Most apps built by software developers writing code Accelerated innovation led by business technologists Apps built with low/no-code tools, by non-traditional devs 70% of new apps

What does a developer portal look like? https://backstage.io/ ● Software catalog ● Templates ● Service discovery & management ● Plug-ins

Concluding Thoughts The future of cloud-based app development and deployment will be Characterized by a more streamlined, automated, enjoyable, productive developer and operator experience Driven by AI Deployed in heterogeneous environments, with data and compute optimally (and potentially dynamically) located for efficiency, performance, cost, and regulatory needs

Danke. This presentation is dedicated to Kris Nóva.