Businesses are increasingly embracing data-intensive workloads, including high-performance computing, artificial intelligence (AI) and machine learning (ML). These technologies drive innovation on their hybrid, multicloud journeys while focusing on resilience, performance, security and compliance. Companies are also striving to balance this innovation with growing environmental, social and governance (ESG) regulations. For most organizations, IT operations and modernization form a part of their ESG objective, and according to a recent Foundry survey, about 60% of organizations seek service providers specializing in green technology areas.
As carbon emissions reporting becomes common worldwide, IBM is committed to assisting its clients in making informed decisions that can help address their energy demands and associated carbon impact while reducing costs. To aid in building more sustainable IT estates, IBM has partnered up with Amazon Web Services (AWS) to facilitate sustainable cloud modernization journeys.
As companies fast-track their IT modernization to accelerate digital transformation and gain business advantage, a significant opportunity emerges. This opportunity involves rearchitecting IT environments and application portfolios toward greener, more sustainable designs. Such an approach not only drives cost efficiencies but also contributes to broader corporate sustainability goals.
All business applications that IBM builds and runs, whether for external or internal customers, come with a carbon cost, which is primarily due to electricity consumption. Regardless of the technology that IBM used to develop these applications or services, operating them requires hardware that consumes power.
The carbon dioxide (CO2) emissions produced by grid electricity vary based on the generation methods. Fossil fuels such as coal and gas emit significant amounts of carbon, whereas renewable sources such as wind or solar emit negligible amounts. Thus, each kilowatt (kW) of electricity consumed directly contributes to a specific amount of CO2 equivalent (CO2e) released into the atmosphere.
Therefore, reducing electricity consumption directly leads to lower carbon emissions.
Compute, storage and networking are the essential tech resources that consume energy in the process of building applications and services. Their activity requires active cooling and management of the data center spaces that they operate in. As custodians of sustainable IT practices, we must consider how we can reduce the consumption of resources through our daily activities.
Data centers draw power from the grid that supplies their operational region. This power runs various IT equipment such as servers, network switches and storage, which in turn support applications and services for customers. This power also operates ancillary systems such as heating, ventilation and air conditioning or cooling, which are essential for maintaining an environment that keeps the hardware within operational limits.
Modernizing applications is becoming pivotal for driving innovation and transforming businesses. IBM Consulting® applies the AWS Well-Architected framework to create a Custom Lens for Sustainability to perform workload assessments for applications both on premises and on AWS Cloud. To read about other key scenarios and entry points of IBM Consulting® Custom Lens for Sustainability, check out the blog post: Sustainable App Modernization Using AWS Cloud.
In this blog post, we delve into an in-depth analysis to assess, implement recommendations on, and analyze the carbon emission effects of a monolithic application running on AWS through a sustainability lens.
The Green IT Analyzer platform enables clients to transform their traditional IT into more energy-efficient, sustainable green IT. Serving as a one-stop shop, it measures, reports, creates baselines and provides a unified dashboard view of the carbon footprint across the hybrid cloud environment—including private data centers, public cloud and user devices. The platform can measure the carbon footprint of the IT estate at both a granular and virtual machine (VM) level. It helps identify energy or carbon hotspots to develop an optimization roadmap. The carbon assessment technique that it uses aligns with greenhouse gas (GHG) (link resides outside ibm.com) principles for the information and communication technology sector.
Understanding the carbon emissions from IT workloads requires familiarity with several key concepts and metrics. Here’s a high-level overview:
Let’s consider a major client challenge. Every organization is committed to achieving net-zero emissions, and IT plays a crucial role in achieving the sustainability agenda. This can involve reducing the carbon footprint of the IT estate itself—especially relevant for financial customers with high IT-driven emissions—or creating a sustainable platform that runs on green IT.
Older monolithic applications, typically running on VM-based platforms in either on-prem data centers or public clouds, are a key focus area. A crucial question arises: how can we reduce IT resource consumption from these older monolithic applications, which generally hold 20–30% of the entire IT portfolio? It is more energy-efficient to move from VM-based monolithic applications toward a more energy-efficient, microservice-based architecture running on a container platform. However, it’s essential to evaluate each case individually, as a one-size-fits-all approach is not always effective.
This criteria can be used to select application transformation candidates:
Consider the example of a simple e-Store application running on AWS in an Elastic Compute Cloud (EC2) VM. This application, an e-CART, experiences seasonal workloads and has been rehosted (lift-and-shift) from on premises to an AWS EC2 instance. Monolithic applications like this package all business functions into a single deployable unit.
The following table describes the key characteristics of e-Store legacy applications.
The carbon emissions of a workload are directly linked to the consumption of resources such as computing, storage and network, with computing often being the most significant contributor. This varies based on workload characteristics; for instance, in the media or streaming industry, data transmission over the network and storing large unstructured data sets consume considerable energy.
The graph shows the utilization pattern of the CPU when minimal user activity is happening on the monolithic application running in a single EC2 instance.
We used the Green IT Analyzer platform to conduct a carbon accounting of the as-is state of the monolithic application, comparing it to the target state of the same application when rearchitected into a microservice architecture running on the Amazon Elastic Kubernetes Services (EKS) (link resides outside ibm.com) platform.
First, we focus on examining the current carbon footprint of a monolithic workload under various operating conditions. This provides us with a baseline for identifying areas for improvement.
Let’s calculate the estimated carbon footprint for our monolithic workload when we have minimal user transactions and 45% of CPU utilization:
In a scenario where peak-level transactions were created as per non-functional requirements (NFR) to test the system’s ability to support daily peaks, CPU utilization surged to 80% during concurrent user activity. This situation triggered an auto-scaling rule set to activate at 80% CPU utilization. The rule provisions extra VMs to help ensure that the load on each VM remains below 60%. The load balancer then efficiently distributes the load among both the existing and new VMs.
Due to the auto-scaling of the new EC2 instances, an additional t2.large VM became available, which led to a drop in the average utilization to 40%.
This step explores a range of sustainability recommendations and their practical implementation for the monolithic application. We use the Custom Lens assessment for Sustainability to guide these recommendations.
First, we consider decomposing monolithic applications into action-based reactive microservices. This approach is tailored to the application’s seasonal behavior and varying usage patterns, which is particularly useful during peak periods such as festive seasons when traffic surges and a focus on browsing artifacts over backend transactions is observed.
Second, the plan involves reducing energy consumption by scheduling batch processing during idle periods, especially when the data center grid operates on green energy. This approach aims to conserve power by minimizing the duration of long-running transactions.
Finally, the strategy emphasizes the importance of choosing a flexible platform, such as AWS EKS or Red Hat® OpenShift® on AWS (ROSA), that is capable of dynamically scaling resources based on network traffic. Such a platform choice helps ensure optimized resource allocation and is beneficial for hosting the action-based reactive microservices.
In summary, the proposed strategies include microservice decomposition aligned with usage patterns, energy-conscious transaction scheduling, and a flexible platform choice to enhance application efficiency and resource utilization.
The application refactored into microservices is shown in the image:
Now let’s calculate the carbon emission after transforming the monolithic application to microservices-based architecture following sustainable design principles while refactoring the application under the umbrella of sustainable modernization.
Observations: When there is no load on the system, an application running on a VM is more carbon efficient than microservices running on an EKS cluster.
Similar to the load testing of monolithic applications, we onboarded 500 users and triggered concurrent transactions to meet the NFR requirements in the microservices that we built.
Here, autoscaling of pods occurred for UI services, but cart services did not require more resources to scale up. In monolithic applications, scaling up the entire platform is necessary regardless of which business functions or services require more resources, leading to increased utilization of 20%.
Observations: Let’s compare both scenarios.
This scenario is an example of how IBM® Custom Lens assessment for Sustainability on AWS workload helps to design your sustainable modernization path and reduce the total carbon footprint of your IT estate.
For organizations that value sustainability, responsible computing and green IT are not just vital; they are entirely feasible. IT leaders can achieve these goals by pursuing environmentally friendly activities that encompass IT strategy, operations and platforms.
The IBM Consulting Green IT Transformation framework, Custom Lens for Sustainability, and the Green IT Analyzer platform collectively help clients on their decarbonization journey. Both frameworks help assess workloads, identify optimization levers that can lower energy consumption, and create an application modernization roadmap that enables you to achieve your sustainability goals.