And this potential is huge: With Data Centres emitting 4%* of greenhouse gas emissions globally, and £8.8 million** wasted in cloud services annually, the scope to make cloud infrastructures more efficient is sky-high.
In this blog we reveal the Swiss Army Knife capabilities of AI to achieve cloud sustainability. AI can bring big benefits to your Digital Strategy yet, like any new technology implementation, it must be done with your eyes wide open as we explain in our white paper.
AI offers a compelling proposition to reduce your organisation's reliance on data centres. This has the potential to achieve great strides in lowering the environmental impact of data storage and processing, making them more sustainable functions.
AWS says it is committed to becoming carbon neutral by 2040, powering its infrastructure with 100% renewable energy by 2025. Several AWS regions, including North Virginia, Ireland, and London are already using 100% renewable energy.
AWS encourages shared responsibility across supply chains in aspects such as energy sources, utilisation and service provision. Critical to these is using AI to accelerate sustainability by optimising e-waste, monitoring CO2e, water management, hardware design and architectural guidance.
Harnessing AI to drive efficiency depends on technology teams deploying the most cost-efficient cloud computing systems as a platform to build on.
So what cloud technologies are there to help drive sustainability?
Graviton processors are custom-designed for AWS workloads, such as Lambda and EC2, and are claimed to consume up to 60% less energy than comparable processors.
Energy-efficient storage options like S3 Glacier and S3 One Zone-IA, together with serverless solutions.
AWS provides customers with tools and resources to help them manage their own sustainability goals, such as the AWS Cloud Carbon Footprint Tool, which tracks their carbon emissions.
A key sustainable cloud solution is AWS Lambda serverless. Prioritising serverless architecture over monolithic application so that workloads only run for an exact amount of time reduces energy wastage and processing time.
The mindset for application architecture should be serverless first, then containers and EC2 as the last model.
Choosing the AWS region based on the project requirements and regulations, but whenever possible, choose an AWS region with a lower carbon footprint.
Ensuring the application code uses optimised algorithms and their libraries are kept updated with the latest versions.
Making sure your workload is right-sized and uses Graviton instances when possible. Also, ensure that the right type of instance is used for the tasks they are running.
Using EC2/Fargate spot instances for fault-tolerant and flexible applications. Implement horizontal auto-scaling to reduce waste in capacity.
Understanding your SLA agreements to avoid over-provisioning of resources in situations where a failover can be managed with a cold start.
Implementing controls and safeguards, such as Web Application Firewalls (WAFs) and Amazon GuardDuty to help prevent bots and malicious users.
Applying lifecycle policies to S3 objects to transition them to different storage classes when they are no longer accessed frequently
Across enterprises, CTOs have a responsibility to ensure their technology teams optimise their cloud applications to reduce their carbon footprint. They can achieve this by right sizing, code efficiency and minimising data impact. Other routes include optional architecture patterns, responsible SLAs, technology selection, workload placement and end-user impact.
Ensuring the long-term sustainability of applications is a crucial decision. In fact, sustainability is one of the pillars of AWS's Well-Architected Framework. In 2021, under the framework, AWS introduced a Sustainability Pillar to help organisations learn, measure and improve their workloads using environmental best practices for cloud computing.
The more sophisticated AI technologies become, the greater the potential for cloud infrastructure to more scalable, accessible and automation-ready.
AWS Compute Optimizer helps you avoid overprovisioning and underprovisioning of four AWS resources: Amazon Elastic Compute Cloud (EC2) instance types, Elastic Block Store (EBS) volumes, Elastic Container Service (ECS) services and AWS Lambda functions.
Amazon DevOps Guru for RDS helps you identify bottlenecks in your relational databases, which can lead to more efficient, cost-effective, and environment-friendly solutions.
Amazon DevOps Guru uses machine learning models to detect abnormal application behaviour, providing remediation options.
AWS Trusted Advisor recommends best practices and machine learning, advising on cost and performance optimization to make your workload more energy efficient. For example, it can identify idle RDS DB instances, underutilised EBS volumes, unassociated Elastic IP addresses, excessive timeouts in Lambda functions, compute usage of EC2 instances and configurations on CloudFront.
Amazon CodeGuru can help you identify vulnerabilities within your codebase and performance improvements that ultimately lead to a more secure, cost-optimised and energy-efficient application.
In summary, the synergy of cloud technologies and AI has the potential to create more sustainable and eco-friendly cloud infrastructure.
By harnessing the power of AI, CTOs can optimise resource usage, reduce energy consumption and lower their carbon footprint. This integration not only benefits businesses, but also contributes to the broader global goal of environmental sustainability.
AI opens up horizons not just for optimising your cloud environments, but across many capabilities of your enterprise to achieve operational efficiencies, speed to development and service delivery.
The challenge is how do you navigate the complexities of AI to drive innovation in your enterprise?
Our white paper provides strategic guidance and valuable insight on how to achieve this.
Sources:
*EY
**Insight Intelligent Technology Index