Reducing the Carbon Impact of Kubernetes with Optimization

New, aggressive carbon emissions regulations in the U.S. and E.U. have put sustainability directly in the spotlight for companies. Measures in the U.S. call for the reduction of net greenhouse gas emissions (GHGs) by 50%-52% by 2030 and net zero by 2050. The European Climate Law targets a reduction of GHGs of 55% by 2030 and makes climate neutrality legally binding by 2050.

At the same time, organizations are under increasing scrutiny from customers, demanding more sustainable practices and faster progress. While much of the focus has rightly been on the transportation sector — the largest emitter of GHGs — cloud computing is also coming under the microscope.

Environmental sustainability and cloud computing may seem unrelated at first glance, but a Kubernetes environment operating at peak efficiency can help bridge the two. Kubernetes handles cloud resources in a more energy-efficient manner, especially when compared with resources managed by containerized virtual machines (VMs). When Kubernetes optimization is fully embraced, its potential to reduce the environmental carbon impact of digital processes is increasingly important.

Yet, many firms remain unaware of the environmental implications of the cloud, Kubernetes and autonomous optimization.

The Cloud’s Carbon Impact

Cloud computing has a significant carbon footprint, accounting for 3.7% of all greenhouse gas emissions — greater than the airline industry as a whole. Despite investments in alternative energy, most data centers use power from dirty sources and consume mass amounts of electricity and water to power and cool their facilities. Data centers have become a significant contributor to climate change.

Increasing consumption from renewable energy sources is part of the solution, but it will not entirely solve the problem for data centers. The right cloud service provider can make a difference. For example, cloud services like AWS use a power mix that is 28% less carbon-intensive than the average, resulting in an 88% carbon reduction compared to traditional data centers.

Kubernetes as a Carbon Reduction Tool

Kubernetes can also curb carbon footprints by enabling organizations to scale resources on demand. This helps ease cloud waste and overprovisioning. Companies that refactor using Kubernetes typically see a measurable reduction in their carbon footprint.

However, initial gains can evaporate as usage expands. New code releases and growing user bases require additional resources. Applications may span multiple availability zones to increase availability, adding to the carbon impact. Despite Kubernetes’ rapid scalability, many organizations continue to overprovision and struggle to manage the complex infrastructure effectively, leading to wasted resources and energy. As an example, 15 unused servers are responsible for about the same amount of CO2 as driving 1,000 miles every month.

While Kubernetes can add to sustainability with its efficient use of resources, it must be optimized continuously to realize these benefits. Unless your dev teams are using every node 24/7, you’re paying for idle time and contributing to the carbon footprint if you aren’t managing Kubernetes efficiently.

Kubernetes’ autoscaling helps by intelligently reducing unneeded resources, but overprovisioning and unmanaged resources still create waste and increase the carbon impact. While workloads are bin-packed into nodes for efficiency, this doesn’t guarantee any increased sustainability if you are overprovisioned. Inefficient resource utilization is the norm and it’s no surprise. Our analysis shows that more than 70% of Kubernetes’ clusters suffer from overprovisioning of 30% or more. Managing nodes and clusters can be complex and time-consuming without the right tools.

Optimizing Kubernetes with Intel Tiber App-Level Optimization

Optimizing Kubernetes with Intel Tiber App-Level Optimization can reduce carbon emissions significantly.

Intel Tiber App-Level Optimization provides autonomous and continuous workload optimization, ensuring fewer resources are wasted while improving throughput and response speed. With zero code changes, Intel Tiber App-Level Optimization can reduce resource consumption by up to 45%, downsizing server usage while improving performance.

Intel Tiber App-Level Optimization also tracks the impact that workload optimization has on your carbon footprint. You get comprehensive visibility into resource consumption, energy savings, and the carbon impact of optimization.

Resource management and workload orchestration automatically match workloads to resources for provisioning, placement, and sizing. Configuration tuning optimization produces automated parameter modification for determining the best workload configuration. By removing idle servers and autonomously managing your workloads, you can significantly boost utilization rates to more than 80% without sacrificing performance or availability. The result is less waste and power consumption.