Red Hat India & South Asia Sustainable Computing from Code to Containers The new KRA for developers and SREs Disclaimer: “Opinions expressed within the content are solely the author's and do not reflect the opinions and beliefs of CNCF or RED HAT.”
Sources: Lancaster University “The climate impact of ICT: A review of estimates, trends and regulations” (December 2020) Microsoft “A new approach for Scope 3 emissions transparency” (2021) Published estimates of ICT current share of global greenhouse gas emissions systematically underestimate the carbon footprint. It now represents about 4% of the greenhouse gas emissions on the planet in 2020 and could reach 8% by 2025 if nothing is changed. Energy efficiencies that have been achieved have historically spurred demand at a pace that exceeds the efficiency savings. Increase in uptake of Blockchain, 5G, IoT and AI technologies will contribute to further increase.
can’t measure: organizations need real time power- and emissions-monitoring capabilities to track impact of their decisions Sources: BCG Gamma, “Use AI to Measure Emissions Exhaustively, Accurately, and Frequently” (October 2021) Emma Chervek, “AWS, Google, Microsoft Measure Cloud Customers' CO2” (SDXCentral, 23 December 2021) of organizations are concerned about reducing their emissions are able to measure their emissions comprehensively 85% 9%
sustainability journey: 1. Hybrid-Cloud Infrastructure Measurements 2. Sustainable Software Engineering Practices 3. Application Optimization by Design Sources: McKinsey “The green IT revolution: A blueprint for CIOs to combat climate change” (15 September 2022) Accenture “The Green Behind the Cloud” (22 September 2020)
instant scale up and high density memory utilization in container orchestration platforms like Kubernetes. https://github.com/quarkusio/quarkus & https://quarkus.io/
usage higher throughput for same resources elasticity machine selectio n (provisioning) scaling workloads down (ideally to 0) serverless a good example (but not the only one) running the same workload on a smaller machine saves energy saves embodied carbon
native low workload (so throughput isn’t the bottleneck) resource-constrained or old hardware (especially memory) high re-deploy rate (applications never get warmed up before being spun down) serverless (of course) use case for JVM high workload (you need lots of throughput) long-lived processes (the rapid start of native doesn’t save you much over the lifetime) stable workload or very little elasticity in underlying orchestration
! https://github.com/kruize/kruize • Monitor application containers for resource usage • Analyse/Predict the right size for the containers • Offer recommendations on the right CPU and Memory request and limit values.
Red Hat India & South Asia Sustainable Computing from Code to Containers The new KRA for developers and SREs Disclaimer: “Opinions expressed within the content are solely the author's and do not reflect the opinions and beliefs of CNCF or RED HAT.”