With more than 2.5 million customers, Italian utility company A2A is committed to delivering electricity, gas, clean water, and waste collection every day. More recently, the company made another significant commitment: To incorporate the principles of the “circular economy” into its way of doing business — part of the UN 2030 Agenda’s Sustainable-Development Goals — all while also aiming to double its client base by 2030. Growing rapidly but sustainably requires operating as efficiently as possible at every level of the organization, from operating smart meters to generating accurate demand projections. That’s why A2A chose to deploy its SAP S/4HANA ERP and the SAP BW/4HANA data warehouse on Google Cloud.

Roadblocks to innovation

Instead of a linear consumption model that starts with raw materials and ends with use and disposal, the circular economy is a continuous cycle that emphasizes repair, recycling, and the creation of materials rather than their disposal. To take an example from A2A’s own success story: The company keeps 99.7 percent of collected waste out of landfills.1 Of the UN’s sustainability goals, A2A is committing to the three most relevant to its industries:

• Ensuring availability and sustainable management of water and sanitation for all
• Ensuring sustainable consumption and production patterns
• Protecting, restoring, and promoting sustainable use of terrestrial ecosystems

Achieving A2A’s sustainability and customer-first strategies requires high scalability, rapid data ingestion, and rich, accurate analytics. None of this could be reliably supported with the company’s legacy on-premises SAP and Data Warehouse, especially given A2A’s projected growth and the increasing complexity of the data landscape, including IoT deployments and energy market liberalization.

Provisioning data infrastructure was also slow and complex. Simply adding a new metric could require increasing capacity by an order of magnitude. And analytical and transactional data lived in siloes, which created a fragmented and out-of-date view of each customer across sales and customer support teams. A2A’s fragmented data also made it difficult to take proactive action when changing priorities or processes required shifting focus from one data source to another.

With a data warehouse that refreshed only once every 24 hours, simple processes such as responding to a customer calling because their power has been cut off due to an unpaid bill became cumbersome.

Scalability was also a concern. With the on-premises solution, A2A needed to define the budget for its data warehouse over a two-year timeframe, but the rollout of new electricity meters — each sending data every 10 minutes — across Italy made those data requirements hard to predict.

The move to the cloud: From monolith to microservices

The move has been a giant step forward in A2A’s goal of meeting its data-driven, customer-centric strategy. In deploying its SAP systems to Google Cloud, A2A can take advantage of a highly flexible hybrid environment and powerful data management and analytics. It can replicate data from Salesforce, SAP, and other systems in BigQuery, which operates as a data lake with Google Cloud SQL, connected directly to Google Analytics and Google Ads for data-driven customer service, decision-making, and marketing.

“From BigQuery we can feed relevant information directly to the people who need it. Our customer operators work on Salesforce, so we use an OData protocol to embed real-time data in that platform. Elsewhere, we present the information through a dashboard, or with a BI component delivering one-page reports.” —Vito Martino, Head of CRM, Marketing and Sales B2C & B2B, A2A

By running SAP on Google Cloud, A2A can also count on an infrastructure platform that provides:

• Scalability. The robust data architecture on Google Cloud adapts to shifting and increasing demands without compromising on speed or availability, so A2A doesn’t have to worry about over- or under-provisioning as the rollout of smart meters proceeds.
• Speed. The new A2A data solution refreshes every five minutes instead of 24 hours, so the company can respond to its customers’ needs without delays. Customer operators working in Salesforce now receive real-time data from Google BigQuery so that, when a customer calls, operators can see accurate information in seconds. They can now offer value-added services and sustainable options tailored to the customer’s needs, from energy consumption to their preferred method of communication.
• Availability. With microservices orchestrated by Google Kubernetes Engine, the team can update the solution through continuous integration and delivery (CI/CD), eliminating the need for downtime when changes are required.
• Security and control. The A2A IT team uses Google Kubernetes Engine to orchestrate clusters of instances on Google Compute Engine, with Google Cloud Load Balancing and backups on Google Cloud Persistent Disk. Google Cloud Anthos ensures operational consistency across on-premises and cloud platforms.

Ready to grow the sustainable way

By moving to Google Cloud — the industry’s cleanest cloud, with zero net emissions — A2A is ready to grow quickly while locking down the efficiency it will need to meet its ambitious sustainability goals. “To bring sustainable utilities to market, we need to be both responsive to our customers and responsive to the internal needs of A2A,” explains Davide Rizzo, Head of IT Governance and Strategy at A2A. “Understanding what customers need in detail means we can improve their services and reduce their environmental impact at the same time.”

Learn more about the ways Google Cloud can transform your organization’s SAP solutions with scalability, speed, and advanced analytics capabilities.

^{1.  Circular Economy: one of the four founding pillars of A2A’s 2030 sustainability policy | Drupal}

Editor’s note: In February 2021, Google Cloud and TELUS announced a 10-year strategic alliance to drive innovation of new services and solutions across data analytics, machine learning, and go-to-market strategies that support digital transformation within key industries, including communications technology, healthcare, agriculture, and connected home. By December 2021, TELUS had completed a pilot for a use case that leveraged Google Cloud AI and Machine Learning solutions and Telco Edge Anthos to increase safety in the workplace and save lives in manufacturing facilities. The use case leverages Multi-Access Edge Computing (MEC) to move the processing and management of traffic from a centralized cloud to the edge of TELUS’ 5G network, making it possible to deploy applications and process content closer to its customers, and thus yielding several benefits including better performance, security, and customization. Today, we invite Samer Geissah, Head of Technology Strategy and Architecture at TELUS, to share how the company is delivering on its promise to use this technology to drive meaningful change, starting with workers’ well-being.

Whenever a new technology buzzword comes along I think: what problems does this solve, and for whom is this going to make a real difference? That’s because at TELUS, we see innovation as a means to act on our social purpose to drive meaningful change, from modernizing healthcare and making our food supply more sustainable, to reducing our environmental footprint and connecting Canadians in need. Multi-Access Edge Computing (MEC) is a buzzword that offers an opportunity to do just this. That’s why we want to leverage cloud capabilities and optimize our network’s edge computing potential, tapping into our award-winning high-speed 5G connectivity to help solve some of industry’s most complex challenges.

The reason why this presents such a great opportunity is that companies across industries still rely on maintenance-heavy on-premises systems to manage core computing tasks. But, with cloud capabilities delivered at the edge of our 5G network, we open a new world of possibilities for them. For example, manufacturers who currently rely on IoT-enabled equipment in their facilities can deliver new experiences by running advanced AI-based visual inspections directly from 5G-enabled devices–all without the need for local processing power or extra on-site space. In fact, it’s this example that inspired our new use case, where our Connected Worker Safety solution can be applied across a range of business verticals to help improve safety, prevent injury, and save lives, demonstrating how the perfect combination of skilled people and digital technology can make the world a safer place.

Empowering intelligent decision making at the edge

Be it a farm, manufacturing facility, hospital, or a factory floor, workers should be able to work in environments where their health and safety are held as the highest priority. But how can employers ensure that their remote, frontline, and in-office employees are safe and healthy at all times? We’ve found the answer by combining Google Cloud AI/ML capabilities and Anthos as a platform for delivering workloads, with our network’s infrastructure.

Together with Google Cloud, we have been leveraging solutions with the power of MEC and 5G to develop a workers’ safety application in our Edmonton Data Center that enables on-premise video analytics cameras to screen manufacturing facilities and ensure compliance with safety requirements to operate heavy-duty machinery. The CCTV (closed-circuit television) cameras we used are cost-effective and easier to deploy than RTLS (real time location services) solutions that detect worker proximity and avoid collisions. This is a positive, proactive step to steadily improve workplace safety. For example, if a worker’s hand is close to a drill, that drill press will not bore holes in any surface until the video analytics camera detects that the worker’s hand has been removed from the safety zone area.

A few milliseconds could make all the difference when you are operating heavy equipment without guards in place. So, to power the solution’s predetermined actions with immediate response times, we worked with Accenture and hosted the application on an Anthos bare metal Google Cloud environment running on our TELUS multi-edge access computing.

Because all the conditions in our model are programmable, this solution can be replicated at scale across a variety of practical scenarios other than factory floors. The actions in response to the analysis are also programmable, which means companies can use this technology to look at workers’ conditions and decide the best course of action to educate, assist, and protect them. All this is done through a single pane of glass ecosystem, making it easy to customize this solution to meet various business needs.

Meanwhile, leveraging our existing global networks to process data and compute cycles at the edge eliminates the need to transport data to a central location for real-time computation. This means that we can offer this solution to partners while optimizing latency and lowering costs.

Powering blink-of-an-eye communication with Anthos

To put the importance of lowering speed into perspective, consider that the average latency of blinking your eye is about 300 milliseconds. From a safety point of view, preventative processes need to be much faster than that. For this use case, our machine learning models running on edge are currently processing data at a tenth of the time it takes for you to blink your eyes, and we’re aiming to lower that latency further to help build even safer systems.

Our plan is to deploy Anthos clusters on bare metal to our customers across Canada to take advantage of our existing enterprise infrastructure, making it possible for us to run our solution closer to partners and eventually enable just one millisecond of latency.

At that point, we’ll be able to power new use cases that require near real-time feedback, leaving absolutely no room for error. This could include remote surgery, platooning of fleets on autonomous vehicles, and many other cellular vehicle-to-everything (V2X) solutions that require high-speed communication for platform operators to manage remote edge fleets in far-away places.

Improving workers’ safety while enabling new sources of revenue

Although edge computing and 5G have been around for a while, we believe that use cases like this are only just starting to demonstrate the incredible speed of change and high potential that these models provide. The next step for us is to develop our workers’ safety solution and get it to market, making TELUS an early adopter of new 5G solutions at the edge that can help our business and industry partners make workplaces safer.

It’s a great win to be able to combine efforts with Google Cloud and reduce latency in a context where timing can impact and save lives, and I’m confident that workers’ safety is just the beginning of a series of industry challenges that we’ll address together.

High Performance Computing (HPC) is prevalent today across many industries, including financial services, life sciences, higher education research, manufacturing, and energy. More and more businesses are deploying HPC workloads in the cloud to take advantage of its elasticity, scalability, and availability. Job schedulers are critical for HPC applications given the nature of these workloads that create, process, and tear down thousands, and sometimes millions, of vCPU and network resources with TB to PB of storage capacity. Job scheduling tools lead to improved operational efficiency and a degree of certainty that a particular HPC job, which can run for hours to weeks, will complete successfully. 

IBM Spectrum LSF is used extensively in the manufacturing and semiconductor industry to manage Electronic Design Automation (EDA) workloads. Dynamically running workloads on-premises and in the cloud, also known as cloud bursting, is becoming a more common practice to address capacity and provisioning time constraints within data centers and enable enterprises to take advantage of virtually unlimited resources. 

However, the challenge with cloud bursting is integrating and maintaining operational consistency across on-premises and cloud environments. IBM Spectrum LSF, in combination with Google Cloud, addresses this problem head on. 

Google Cloud is excited to announce, in collaboration with IBM, enhanced capabilities to IBM Spectrum LSF that enables organizations to integrate their on-premises job scheduling scripts with resources deployed in Google Cloud. Customers are now able to fully leverage Google Cloud’s highly scalable and secure Compute Engine, networking and storage infrastructure. 

The LSF-Google Cloud resource connector patch supports key Google Cloud differentiators including Local SSDs, GCE instance templates, Preemptible VMs, and more: 

• Bulk API support– Deploy large fleets of VM instances in a matter of seconds.
• Instance Templates – Simplify VM configuration by creating reusable templates. 
• All Machine Types – Supports all GCE VM families and machine types, including Custom Machine Types.
• GPUs – Attach up to 16 GPUs per instance, including the largest A2 instances with up to 16 NVIDIA A100 GPUs
• Preemptible VMs – Preemptible VMs are provisioned from excess Compute Engine capacity and is a significant way to save money on GCE resources.
• Local SSD – Attach up to 9TB of NVMe SSD per instance
• Hyperthreading – Supports the “threads-per-core” option in GCE, which allows per-VM hypervisor level Hyperthread configuration (when supported by Instance Templates)
• Images – Supports custom disk images, including full support for Windows, for all attached Persistent Disks
• Placement Policies – Control where the instances are physically located relative to each other within a zone for improved low-latency performance
• Labels – Supports GCE Labels, which can be used for management of firewall rules, tracking billing, etc
• Minimum CPU Platform – Supports the ability to specify a minimum CPU Platform for your Virtual Machines.

Getting Started

This improvement to the IBM LSF Resource Connector was developed by IBM in coordination with Google. Find out more about the new supported features and their operation in the official IBM Spectrum LSF Resource Connector Documentation. You can also find additional documentation and download the software in the IBM Spectrum Computing Community. If you have further questions, you can contact IBM, or Google Cloud Sales.

Special thanks to Annie Ma-Weaver, Mark Mims, and Wyatt Gorman for their contributions.