Telus Ensures Workers’ Safety Using Edge and 5G
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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.
Google Cloud’s High-performance Compute Speeds Up the Chip Design Process
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Cloud offers a proven way to accelerate end-to-end chip design flows. In a previous blog, we demonstrated the inherent elasticity of the cloud, showcasing how front-end simulation workloads can scale with access to more compute resources. Another benefit of the cloud is access to a powerful, modern and global infrastructure. On-prem environments do a fantastic job of meeting sustained demand but Electronic Design Automation (EDA) tooling upgrades happen much more frequently (every six to nine months) than typical on-prem data center infrastructure upgrades (every three to five years).
What this means is that your EDA tool can provide much better performance if given access to the right infrastructure. This is especially useful in certain phases of the design process.
Take for example, a physical verification workload. Physical verification is typically the last step in the chip design process. In simplified terms, the process consists of verifying design rule checks (or DRCs) against the process design kit (PDK) provided by the foundry. It ensures that the layout produced from the physical synthesis process is ready for handoff to a foundry (in-house or otherwise) for manufacturing. Physical verification workloads tend to require machines with large memories (1TB+) for advanced nodes. Having access to such compute resources enables more physical verification to run in parallel, increasing your confidence in the design that is being taped out (i.e., sent to manufacturing).
At the other end of the spectrum are functional verification workloads. Unlike the physical verification process described above, functional verification is normally performed in the early stages of design and typically requires machines with much less memory. Furthermore, functional verification (dynamic verification in particular) accounts for the most time (translating directly to the availability of compute) in the design cycle. Verifying faster, an ambition for most design teams, is often tied to availability of right-sized compute resources.
The intermittent and varied infrastructure requirements for verification (both functional and physical) can be a problem for organizations with on-prem data centers. On-prem data centers are optimized for maximizing utilization—this does not directly address access to right-sized compute to deliver the best tool performance. Even if the IT and Computer Aided Design (CAD) departments choose to provision additional suitable hardware, the process of provisioning, acquiring and setting up new hardware on-prem typically takes months for even the most modern organizations. A “hybrid” flow that enables use of on-prem clusters most of the time, but provides seamless access to cloud resources as needed would be ideal.
Hybrid chip design in action
You can improve a typical verification workflow simply by utilizing a hybrid environment that provides instantaneous access to better compute. To illustrate, we chose a front-end simulation workflow, and designed an environment that replicates on-prem and cloud clusters. We also took a few more liberties to simplify the environment (described below). The simplified setup is provided in a GitHub repository for you to try out.
In any hybrid chip design flow, there are a few key considerations:
- Connectivity between on-prem infrastructure and the cloud: Establishing connectivity to the cloud is one of the most foundational aspects of the flow. Over the years, this has also become a very well-understood field, and secure, high availability connectivity is a reality in most setups.
In our tutorial, we represent both on-prem and cloud clusters as two different networks in the cloud where all traffic is allowed to pass between these networks. While this is not a real-world network configuration, it is sufficient to demonstrate the basic connectivity model. - Connection to license server: Most chip design flows utilize tools from EDA vendors. Such tools are typically licensed, and you need a license server with valid licenses to operate the tool. License servers may remain on-prem in the hybrid flow, so long as latency to the license server is acceptable. You can also install license servers in the cloud on a Compute Engine VM (particularly sole-tenant nodes) for lower latency. Check with your EDA vendors to understand if you can rehost your license services in the cloud.
In our tutorial, we use an open source tool (Icarus Verilog Simulator) and therefore, do not need a license server. - Identifying data sources and syncing data: There are three important aspects in running EDA jobs: the EDA tools themselves, the infrastructure where the tools run, and the data sources for the tool run. Tools don’t change much, and can be installed on cloud infrastructure. Data sources, on the other hand, are primarily created on-prem and updated regularly. These could be SystemVerilog files that describe the design, the testbenches or the layout files. It is important to sync data between on-prem and cloud to maintain parity. Furthermore, in production environments, it’s also important to maintain a high-performance syncing mechanism.
In our tutorial, we create a file system hierarchy in the cloud that is similar to one you’d find on-prem. We transfer the latest input files before invoking the tool. - Workload scheduler configuration and job submission transparency: Most environments that leverage batch jobs use job schedulers to access a compute farm. An ideal environment finds the balance between cost and performance, and builds parameters in the system to enable predictive (and prescriptive) wrappers to job schedulers (see picture below).
In our tutorial, we use the open-source SLURM job scheduler and an auto-scaling cluster. For simplicity, the tutorial does not include a job submission agent.
Other cloud-native batch processing environments such as Kubernetes can also provide further options for workload management.
Our on-prem network is called ‘onprem’ and the cloud cluster is called ‘burst’. Characteristics of the on-prem and burst clusters are specified below:
Once set up, we ran the OpenPiton regression for single and two-tile configurations. You can see the results below:
Regressions run on “burst” clusters were on average 30% faster than on “onprem”, delivering faster verification sign-off and physical verification turnaround times. You can find details about the commands we used in the repository.
Hybrid solutions for faster time to market
Of course, on-prem data centers will continue to play a pivotal role in chip design. However, things have changed. Cloud-based, high performance compute has proved itself to be a viable and proven technology for extending on-prem data centers during the chip design process. Companies that successfully leverage hybrid chip design flows will be able to better address the fluctuating needs of their engineering teams. To learn more about silicon design on Google Cloud, read our whitepaper “Using Google Cloud to accelerate your chip design process”.
Expect 40 Percent Higher Price-performance than General Purpose VM with Google TAU VMs!
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In November 2021, we announced the general availability of Tau VMs. Since then, Google Cloud’s Tau VMs with Google Kubernetes Engine (GKE) have unlocked value for many customers who are now using Tau VMs for their production workloads, such as: Ascend, who achieved over 125% higher performance; Nylas, who gained over 40% higher price-performance; and OpenX, who achieved 40% better price-performance while at the same time reducing their application latency by 62%.
T2D is the first instance type in the Tau VM family and is built on the latest 3rd generation AMD EPYCTM processors, offering 42% higher price-performance compared to general-purpose VMs from any of the leading public cloud vendors. Tau VMs offer a leading combination of performance, price, and full x86 compatibility, offering customers the lowest cost solution for scale-out workloads. Tau VMs are available in predefined shapes, with up to 60vCPUs per VM, 4GB of memory per vCPU, networking up to 32 Gbps and a slew of storage options including Standard, Balanced and Performance PD. Tau VMs are also available as Spot VMs, offering an over 60% discount compared to on-demand pricing.
For customers looking for advanced container orchestration, GKE delivers high levels of reliability, security, and scalability, and has supported Tau VMs since the day they became available on Google Cloud. Tau VMs are ideal for CPU-bound workloads such as web-serving with encryption, video encoding, compression/decompression, image processing and horizontally-scaled applications. Using Tau VMs along with GKE’s cost-optimization best practices can help lower your total cost of ownership. You can add Tau VMs to new or existing GKE clusters by specifying the Tau T2D machine type in your GKE node-pools through the Cloud console or by using –machine-type in gcloud.
Here is what some of our customers have to say about Tau VMs:
Ascend provides a unified analytics and data engineering platform, and chose Tau VMs along with GKE to run their data-intensive workload — primarily because of Tau’s absolute performance and price-performance advantage.
“Our core capability at Ascend is bringing together data ingestion, transformation, delivery, orchestration and observability into a single platform. To operate at scale and keep pace with our telemetry data production rates, high single-threaded performance is critical. With Google Cloud’s Tau VMs with Google Kubernetes Engine (GKE), we are able to achieve over 125% higher performance than previous generation families. This has completely changed our ability to query historical metrics. Where previously metric queries against historical data over ranges longer than a couple hours were difficult, we can now easily query data ranges of multiple weeks.” – Joe Stevens, Tech Lead – Infrastructure, Ascend.io
Nylas is a pioneer and leading provider of productivity infrastructure solutions for modern software. In the past year, Nylas has been using GKE in their journey to reinvent their architecture and provide their enterprise customers with a bi-directional universal email sync, security compliance with the highest enterprise standards, and industry-specific machine learning services.
“For our core application, Google’s Tau VMs with Google Kubernetes Engine delivers over 40% better price-performance than Amazon’s Graviton-based VMs. Further, Tau VMs maintain x86 compatibility and eliminate the need to maintain a separate stack for ARM. We are moving our workload from Amazon Web Services to Google Cloud to take advantage of these benefits.” – David Ting, SVP of Engineering, Nylas
OpenX operates an independent ad exchange. Operating 100% on Google Cloud has enabled OpenX to achieve improved performance, scalability, speed and global reach.
“At OpenX, our ad-exchange services over 200 billion requests every day. Getting the best combination of performance and price from the infrastructure is critically important for us. We use multiple Google Kubernetes Engine (GKE) clusters across geographic regions with autoscaling to power our ad-delivery components. Running Google Cloud’s Tau VMs with GKE has enabled over 40% better price-performance and 62% latency reduction for our application as compared to the prior generation family. We have made the move to Tau VMs for our application to take advantage of these benefits.” – Paul T.Ryan, CTO, OpenX
We are excited to see Tau VMs adding value for so many of our customers by enabling industry leading price-performance for a variety of workloads.
If you haven’t tried Tau VMs yet, give them a try today in our Iowa, Netherlands and Singapore regions and move your production workloads to Tau VMs. Tau VMs will be arriving in additional regions and zones in the coming weeks. You can provision GKE node pools based on Tau VMs and explore how you can take advantage of improved price-performance for your scale-out containerized workloads.
To get started, go to the Google Cloud Console, select Google Kubernetes Engine, and choose Tau T2D for your GKE nodes. To learn more about Tau VMs or other Compute Engine VM options, check out our machine types and our pricing pages.
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Public cloud, big data, and AI technologies offer competitive advantages and cost savings for capital markets firms ready to make the transition. This paper discusses the three phases capital markets firms go through in transitioning to public cloud, and the workloads, benefits, and cultural changes that characterize the three phases:
Infrastructure Optimizers: The first step on the public cloud journey, where firms focus on migrating specific workloads to save costs.
Cautious Strategists: Firms build on the success of their first public cloud migrations, and begin to change the way they develop technology to increase cost savings and start taking advantage of capabilities only available on public cloud.
Transformative Innovators: Firms shift to a fully public cloud-enabled mentality, and fully leverage the flexibility and agility of the public cloud to build industry-changing solutions and attract top IT talent.
Additionally, we reveal the five things that capital markets innovators who have advanced to the transformation phase do well in their adoption of cloud, big data, and AI technologies across the front, middle, and back office functions.
Cloud Migration and Modernization is ‘Easier Done than Said’ with Google Cloud RAMP!
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The reliance on cloud compute, storage and network has accelerated with the growing usage of digital products and services. To keep up with the customer demands and deliver digital solutions, many companies are struggling through their cloud migration and modernization journeys. But not anymore, says Google Cloud with the Rapid Assessment & Migration Program (RAMP)!
Download the infographic to have succinct view of what cloud migration and modernization would look like by RAMPing up! Bid Adieu to delays and budget overspend with the tools, resources, partners and fundings with RAMP.
IDC: Firms Should Migrate VM-based Enterprise Workloads to Google Cloud for Optimal Price, Performance, and Security
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Public cloud platforms provide the scale, elasticity, and operational efficiency that enable enterprises to both innovate and deliver more products and services to the market faster. This increased ability to innovate provides enterprises a competitive advantage and the businesses agility to stay ahead of their competition. Enterprises should leverage the business agility that public cloud infrastructure enables to their strategic advantage. Thus incorporating a cloud platform is an inherent part of their initiatives to modernize their IT infrastructure.
Enterprise cloud adoption is not without challenges. Enterprises cite a lack of skill set, decision fatigue, operational challenges, security concerns, and unpredictable TCO as significant inhibitors to adopting a public cloud service provider. Information technology decision makers (ITDMs) are caught between the need to innovate faster and the fear of instability and mismatched expectations. This dilemma is why enterprises that partner with a trusted service provider before, during, and after migration are more likely to succeed in their cloud journey.
Google Cloud is an ideal platform for virtual machine (VM)–based applications due to its key technical capabilities, such as high-performing virtual machines, including compute-optimized and memory-optimized VMs; custom sizes for virtual machines; high-performance network infrastructure for faster data transfers; and data protection capabilities.
Download this IDC report to understand why ITDMs should consider Google Cloud as the preferred cloud services partner — owing to Google Cloud being a reliable platform for VM-based applications; a trusted partner before, during, and after application migration; and an innovator to future proof the enterprise IT infrastructure.
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