Google Cloud’s Professional Service Organization: How it Accelerates Operational Health Review Cloud Migration
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Introduction
The Google Cloud Professional Services Organization’s (PSO) mission is to help our customers get the most out of Google products. PSO is responsible for customer success by sharing our technical expertise in order to unlock business value from the cloud by providing cloud strategy and best practice advice, implementation guidance, and training using our proven methodology.
In this blog post, we want to focus specifically on Operational Health Reviews and how PSO engages in a myriad of activities to ensure the overall success and health of a customer engagement while ensuring we are meeting customer expectations and objectives, managing risk, and ultimately delivering value during or after the Google Cloud migration. We will discuss the assets, methodology, and tools that we leverage to ensure this success.
What is an Operational Health Review and what purpose does it serve?
Operational Health Reviews (OHR) are regular reviews to proactively address the below topics:
- A customer’s support experience
- Analysis of trends in key operational metrics
- Analysis of trends in Google Cloud usage
- Status reports for high-priority cloud projects
- Discussion of upcoming events
- Discussion of potential opportunities for training
- Review of feature requests
The core aim of an OHR is to measure progress and advise customers on the overall health of the account and migration progress while providing recommendations as a team on what adjustments should be considered to ensure continued success. During the OHR, we will address any pain points as well as identify and remediate any negative trends in support interfaces and usage metrics.
The intention of the activity is to also perform a blameless reflection for continuous improvement. This supports the effort to maintain common ground and obtain mutual optimism for the next phase of the customer’s journey on Google Cloud.
Let’s dive a little deeper into some of the activities that go into an OHR.
Support experience
One of the Google Cloud’s Support Organization’s goals is to simplify and streamline our customer’s support experience with a scalable and flexible set of offerings built with the customer needs at the center. This includes supporting an ongoing partnership model, with a proactive and collaborative approach. Premium Support is our top-tier support model which is a paid support offering designed for enterprises that run mission critical workloads and require fast response times, platform stability, and increased operational efficiencies. As part of this offering, customers are provided with a Technical Account Manager (TAM) who is in charge of delivering frequent touchpoints, including OHRs.
During the support experience section of an OHR, some of the following topics may be reviewed:
- Case volume by priority
- Case volume by product
- Cases linked to Google Cloud incidents
- Escalated cases or incidents
- Case initial response time (IRT)
- Case IRT SLO Met Rate
- Case total resolution time (TRT) hours
The purpose of this activity is to better understand the efficiency of both the customer’s and Google’s cloud operations from a support trend perspective. The goal is to celebrate any positive trends, but also to identify potential negative trends and proactively determine a remediation strategy.
Status reports for high-priority cloud projects
The purpose of this part of the OHR is to ensure the senior management stakeholders have continued visibility into the status of all their high-priority cloud initiatives with an easy-to-read, sometimes color-coded assessment on the status of each project. The assessment [Figure 1] may include, but is not limited to potential identified risks, key next steps, key stakeholders, and a mitigation plan for any high priority issues that may occur during their migration engagement. Additionally, the TAM may also cover any upcoming milestones or events, as well as any potential anticipated future blockers [Figure 2].
Review trends in Google Cloud usage
This part of the OHR is meant to provide the customer with a holistic view of their overall Google Cloud usage. The purpose is to provide understanding around what products are being used on Google Cloud and also where cost is generally being allocated for overall cost management and budget tracking purposes. This can help customers with their overall cost optimization efforts. Some of the metrics we may cover in this section are:
- Current Google Cloud usage by service
- Google Cloud growth trends
- Google Cloud growth trends by service
- Google Cloud growth trends by project
- Review of used and available service credits
Feature request review and advocacy
TAMs will work with customers to help identify product needs and advocate for feature requests with Google Product Management and Engineering teams. While feature request advocacy is an ongoing activity, during the OHR, the TAM will review any high priority feature requests that have previously been submitted, providing updates on status including potential release dates.
When a feature is coming up for the release, the TAM can help the customer prepare for implementation (providing early Preview access, coordinating a meeting with the Product Manager to review the feature, etc.) and then continue to provide support until the feature is successfully implemented in the customer environment. Similarly, if a high priority or technical blocking feature is not yet coming available, the TAM can help offer an alternative solution that can unblock the customer in the short term.
The OHR is also a great time for the TAM to work with the customer to ensure they have early access to new and exciting features that are in Alpha or Beta, and that would be beneficial to the customer and their environment.
Training strategy
Training is a primary component to ensuring the overall success in adopting Google Cloud. During the OHR, a review of training metrics will be provided. This may include tracking metrics to determine how the customer is tracking towards an initially agreed upon learning plan or coming up with additional upskilling strategies as necessary. Additionally, throughout an engagement, the customer’s TAM will provide opportunities for both free and paid Google Cloud training.
Conclusion
PSO is the voice of customer health, maintaining priority to proactively and strategically guide large enterprise customers to operate effectively and efficiently in the cloud, both during and after the migration process. The OHR is an efficient and effective way to maintain alignment, ensure expectations and objectives are being met, manage risk, and overall ensure the success of the customer.
Learn more about our methodology and get started with Google Cloud by completing a free discovery and assessment of your migration. Alternatively, if you’d like to engage directly with our PSO team on your migration, contact us!
Making Weather Predictions Easy with Weather Research and Forecasting (WRF) Models on Google Cloud!
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Weather forecasting and climate modeling are two of the world’s most computationally complex and demanding tasks. Further, they’re extremely time-sensitive and in high demand — everyone from weekend travelers to large-scale industrial farming operators wants up-to-date weather predictions. To provide timely and meaningful predictions, weather forecasters usually rely on high performance computing (HPC) clusters hosted in an on-premises data center. These on-prem HPC systems require significant capital investment and have high long-term operational costs. They consume a lot of electricity, have largely fixed configurations, and the underlying computer hardware is replaced infrequently.
Using the cloud instead offers increased flexibility, constantly refreshed hardware, high reliability, geo-distributed compute and networking, and a “pay for what you use” pricing model. Ultimately, cloud computing allows forecasters and climate modelers to provide timely and accurate results on a flexible platform using the latest hardware and software systems, in a cost effective manner. This is a big shift compared with traditional approaches to weather forecasting, and can appear challenging. To help, weather forecasters can now run the Weather Research and Forecasting (WRF) modeling system easily on Google Cloud using the new WRF VM image from Fluid Numerics, and achieve the performance of an on-premises supercomputer for a fraction of the price. With this solution, weather forecasters can get a WRF simulation up and running on Google Cloud in less than an hour!
A closer look at WRF
Weather Research and Forecasting (WRF) is a popular open-source numerical weather prediction modeling system used by both researchers and operational organizations. While WRF is primarily used for weather and climate simulation, teams have extended it to support interactions with chemistry, forest fire modeling, and other use cases. WRF development began in the late 1990s through a collaboration between the National Center for Atmospheric Research (NCAR), National Oceanic and Atmospheric Administration (NOAA), U.S. Air Force, Naval Research Laboratory, University of Oklahoma, and the Federal Aviation Administration. The WRF community comprises more than 48,000 users spanning over 160 countries, with the shared goal of supporting atmospheric research and operational forecasting.
The Google Cloud WRF image is built using Google’s MPI best practices for HPC, with the exception that hyperthreading is not disabled by default, and is easily integrated with other HPC solutions on Google Cloud, including SchedMD’s Slurm-GCP. Normally, installing WRF and its dependencies is a time consuming process. With these new WRF VM images, deploying a scalable HPC cluster with WRF v4.2 pre-installed is quick and easy with our Codelab. OpenMPI 4.0.2 was used throughout this work. Google has had good success with Intel MPI, and we intend to study whether further performance gains can be achieved in this context.
Optimizing WRF
Determining the optimal architecture and build settings for performance and cost was a key part of the process in developing the WRF images. We evaluated how to select the ideal compiler, right CPU platform, and the best file system for handling file IO, so you don’t have to. As a test case for assessing performance, we used the CONUS 2.5km benchmark.
Below, the CONUS 2.5km runtime and cost figure shows the run time required for simulating WRF over a two-hour forecast using 480 MPI ranks (a way of numbering processes) for different machine types available on Google Cloud. For each machine type, we’re showing the lowest measured run time from a suite of tests that varied compiler, compiler optimizations, and task affinity.
We found that compute-optimized c2 instances provided the shortest run time. The Slurm job scheduler allows you to map the MPI tasks to compute hardware using task affinity flags. When optimizing the runtime and cost for each machine type, we compared using srun –map-by core –bind-to core to launch WRF, which maps each MPI process to a physical core (two vCPU per MPI rank), and srun –map-by thread –bind-to thread, which maps each MPI process to a single vCPU. Mapping by core and binding MPI ranks to cores is akin to disabling hyperthreading.
The ideal simulation cost and runtime for CONUS 2.5km for each platform is found when each MPI rank is subscribed to each vCPU. When binding to vCPUs, half as many compute resources are needed when compared to binding to physical cores lowering the per-second cost for the simulation. For CONUS 2.5km, we also found that although mapping MPI ranks to cores results in reduced runtime for the same number of MPI ranks, the performance gains are not significant enough to outweigh the cost savings. For this reason, the WRF-GCP solution does not disable hyperthreading by default.
Runtime and simulation cost can be further reduced by selecting an ideal compiler: the figure below (CONUS 2.5km Compiler Comparisons) shows the simulation runtime for the WRF CONUS 2.5km benchmark on eight c2-standard-60 instances, using GCC 10.30, GCC 11.2.0 and the Intel® OneAPI® compilers (v2021.2.0). In all cases, WRF is built using level 3 compiler optimizations and Cascade Lake target architecture flags. By compiling WRF with the Intel® OneAPI® compilers, the WRF simulation runs about 47% faster than the GCC builds, and at about 68% of the cost, on the same hardware. We’ve used OpenMPI 4.0.2 with each of the compilers as the MPI implementation in this work. With other applications, Google has seen good performance with Intel MPI 2018, and we intend to investigate performance comparisons with this and other MPI implementations.
File IO in WRF can become a significant bottleneck as the number of MPI ranks increases. Obtaining the optimal file IO performance requires using parallel file IO in WRF and leveraging a parallel file system such as Lustre.
Below, we show the speedup in file IO activities relative to serial IO on an NFS file system. For this example, we are running the CONUS 2.5km benchmark on c2-standard-60 instances with 960 MPI ranks. By changing WRF’s file IO strategy to parallel IO, we accelerate file IO time by a factor of 60.
We further speed up IO and reduce simulation costs by using a Lustre parallel file system deployed from open-source Lustre Terraform infrastructure-as-code from Fluid Numerics. Lustre is also available with support from DDN’s EXAScaler solution in the Google Cloud Marketplace. In this case, we use four n2-standard-16 instances for the Lustre Object Storage Server (OSS) instances, each with 3TB of Local SSD. The Lustre Metadata Server (MDS) is an n2-standard-16 instance with a 1TB PD-SSD disk. After mounting the Lustre file system to the cluster, we set the Lustre stripe count to 4 so that file IO can be distributed across the four OSS instances. By switching to the Lustre file system for IO, we speed up file IO by an additional factor of 193, which is orders of magnitude faster than a single NFS server with serial IO.
Adding compute resources and increasing the number of MPI ranks reduces the simulation run time. Ideally, with perfect linear scaling, doubling the number of MPI ranks would cut the simulation time in half. However, adding MPI ranks also increases communication overhead, which can increase the cost per simulation. The communication overhead is due to the increased amount of communication necessitated by splitting the problem more finely across more machines.
To assess the scalability of WRF for the CONUS 2.5km benchmark, we can execute a series of model forecasts where we successively double the number of MPI ranks. Below, we show two- hour forecasts on the c2-standard-60 instances with the Lustre file system, varying the number of MPI ranks from 480 to 1920. In all of these runs, MPI ranks are bound to vCPUs so that the number of vCPUs dedicated to each simulation increases with the increase in MPI ranks. While many HPC workloads run best with simultaneous multithreading (SMT) disabled, we find the best performance for CONUS 2.5km with SMT enabled. Thus, the number of MPI ranks in our runs equals the total number of vCPUs.
As you can see, the CONUS 2.5km Runtime & Cost Scaling figure shows that the run time (blue bars) decreases as the number of MPI ranks and the amount of compute resources increases, at least up to 1920 ranks. When transitioning from 480 to 960 MPI ranks, the run time drops, yielding a speedup of about 1.8x. Doubling again to 1920 MPI ranks, though, we obtained an additional speedup of just 1.5x. This declining trend in the speedup with increasing MPI ranks is a signature of MPI overhead, which increases with more MPI ranks.
Determining your best fit
Most tightly-coupled MPI applications such as WRF exhibit this kind of scaling behavior, where scaling efficiency decreases with increasing MPI ranks. This makes assessing cost-scaling alongside performance-scaling critical when considering Total Cost of Ownership (TCO). Thankfully, per-second billing on Google Cloud makes this kind of analysis a little bit easier. As shown above, a second doubling of the count from 960 cores to 1920 cores can provide an additional 1.5x speedup, but at a 32% higher cost. In some circumstances, this faster turnaround may be needed and worth the extra cost.
If you want to get started with WRF quickly and experiment with the CONUS 2.5km benchmark, we’ve encapsulated this deployment in Terraform scripts and prepared an accompanying codelab.
You can learn more about Google Cloud’s high performance computing offerings at https://cloud.google.com/hpc, and you can find out more about Google’s partner Fluid Numerics at https://www.fluidnumerics.com.
Melbourne Joins Google’s 26 Cloud Regions
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We opened our Sydney cloud region in 2017 and, since then, we have continued to invest and expand across Australia and New Zealand to support the digital future of organizations of all sizes. In Australia, Google Cloud supports almost A$3.2 billion in annual gross benefits to businesses and consumers. This includes A$686 million to businesses using Google Workspace and Google Cloud Platform, another A$698 million to Google Cloud partners, and A$1.8 billion to consumers.1
For customers in Australia, New Zealand and across Asia Pacific, we’re excited to announce that our new Google Cloud region in Melbourne is now open. Designed to help businesses build highly available applications for their customers, the Melbourne region is our second Google Cloud region in Australia and 11th to open in Asia Pacific.
We’re celebrating the occasion with a digital event where federal minister for the Digital Economy, Jane Hume, and customers Australia Post, Trade Me, Bendigo and Adelaide Bank, The Australian Football League and Macquarie Bank will share their perspectives. Come join us!
A global network of regions
Melbourne joins the existing 26 Google Cloud regions connected via our high-performance network, helping customers better serve their users and customers throughout the globe. With this our second region in Australia, customers benefit from improved business continuity planning with distributed, secure infrastructure needed to meet IT and business requirements for disaster recovery, all the while maintaining data sovereignty in-country.
With this new region, Google Cloud customers operating in Australia and New Zealand will benefit from low latency and high performance of their cloud-based workloads and data. Designed for high availability, the region opens with three zones to protect against service disruptions, and offers a portfolio of key products, including Compute Engine, Google Kubernetes Engine, Cloud Bigtable, Cloud Spanner, and BigQuery.
We also continue to invest in expanding connectivity across the Australia and New Zealand region by working with partners to establish subsea cables and new Dedicated Cloud Interconnect locations and points of presence in major cities including Sydney, Melbourne, Perth, Canberra, Brisbane and Auckland.
Collectively, this will deliver geographically distributed and secure infrastructure to customers across Australia and New Zealand – which is especially important for those in regulated industries such as Financial Services and the Public Sector.
What customers and partners are saying
Navigating this past year has been a challenge for companies as they grapple with changing customers demands and greater economic uncertainty. Technology has played a critical role, and we’ve been fortunate to partner with and serve people, companies, and government institutions around the world to help them adapt. The Google Cloud region in Melbourne will help our customers adapt to new requirements, new opportunities and new ways of working.
“We moved to Google Cloud to improve the stability and resilience of our infrastructure and become more cloud-native as part of a digital transformation program that keeps the customer at the heart of our business. We welcome Google Cloud’s investment in ANZ and the opportunities the Google Cloud Melbourne region presents to improve Trade Me’s agility and performance. – Paolo Ragone, Chief Technology Officer, Trade Me
“We initially turned to Google Cloud to help us process parcels faster and gain deeper insights into our business and its processes. The relationship has continued to deliver benefits to our customers and our organization and we welcome Google Cloud’s opening of the Melbourne region as presenting even more opportunities for businesses to innovate and generate efficiencies.” – Munro Farmer, Chief Information Officer, Australia Post.
“We are well progressed with our multi-year strategy to grow and transform our organization to be Australia’s bank of choice. Google Cloud’s advanced data capabilities and renowned culture of innovation are strongly aligned to this strategy and will allow us to become even more innovative and agile in responding to our customers’ ever-changing needs. We were quick to run our workloads out of the Melbourne cloud region and we believe Google Cloud’s expanded investment in local infrastructure will further assist us on our business transformation journey.” – Andrew Cresp, Chief Information Officer, Bendigo and Adelaide Bank.
“We have a clear vision when it comes to innovating to deliver world-class service to our customers, and our partnership with Google Cloud is core to that strategy. The company’s continued investments in local infrastructure and technology present new opportunities for us as we advance our transformation journey in this digital-first era.” – Chris Smith, Vice President, Digital Service, Optus
Our global ecosystem of channel partners has expanded by more than 400% in the last two years, and we look forward to continuing our close relationships with partners in Australia and New Zealand as we help customers modernize, innovate, scale and grow.
“Australian companies are increasingly realising the benefits of their cloud investments and are now looking to transform their organisations at scale. We are excited about the potential and new value that the Google Melbourne Cloud region will bring to our clients as we continue to work together on delivering intelligent and innovative solutions to Australian organisations.” – Tara Brady, CEO of Accenture Australia and New Zealand
“Google Cloud has always been there for its customers for the long haul and the opening of the new Melbourne Cloud region is great news. This increased resilience and scale will empower companies of all sizes to be bold in accelerating their digital transformation plans.” – Tony Nicol, CEO of Servian
“We’re excited about the launch of the Melbourne Cloud region. It will cater to the needs of industries we work closely with including healthcare and financial services, and will further enhance how we jointly deliver on the compliance, privacy and security requirements of companies as they advance their digital transformation.” – Simon Poulton, CEO of Kasna
“The opening of the new Google Cloud region in Melbourne is fantastic news as it now enables DXC customers access to enhanced services for their mission critical application and data solutions across two regions within Australia. As our customers modernise their application estate, many are seeking dual region cloud services, and DXC is excited to partner with Google Cloud to deliver these services to customers in Australia and New Zealand.” – Tim Fraser, Google Practice Lead ANZ at DXC Technology
Helping customers build their transformation clouds
Google Cloud is here to support businesses, helping them get smarter with data, deploy faster, connect more easily with people and customers throughout the globe, and protect everything that matters to their businesses. The cloud region in Melbourne offers new technology and tools that can be a catalyst for this change. Click here to learn more about all our Google Cloud locations.
1. AlphaBeta, The Economic Impact of Google Cloud to Australia, July 2021
Modernize your Windows Server Workloads using Google Cloud Platform
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Application Modernization is an important enabler of Digital Transformations (DX), which fuel competitive advantage through increased productivity and business agility. Public cloud infrastructure proves to be a solid foundation for application modernization by providing Self-Service Provisioning capabilities, cloud-based & cloud-native technologies, and easier access to technology innovations such as AI/ML.
Windows Server-based enterprise applications rely on the underlying infrastructure for platform performance, security, and availability. A better performing cloud platform enables them to perform better and hence prove to be more resource-optimized and cost-effective.
Download this IDC report to understand why you should move your Windows Server workloads to Google Cloud and the benefits you can derive.
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Enterprises developing digital leadership are increasingly moving out of their data centers to focus on core business innovation, and save on complex infrastructure costs. This puts challenges related to demand peaks and business continuity under a magnifying glass.
Many CIOs are under pressure to complete migrations quickly — 68% of CIOs are seeking to migrate existing applications to the cloud, according to Forrester. Motivations range from cost or risk reductions, to refocusing on agility and speed.
As they assess their options, many enterprises face an enormous challenge of balancing the function of their existing infrastructure with a new operating model in the cloud. This involves thousands of variables, different technologies, different processes and skills, disparate teams, and competing interests.
Download this guide and see hoo to craft a strategy out of the data center and into public cloud. It surfaces typical industry patterns, key dimensions to be taken into account while designing the journey, as well as Google’s capabilities and approach to executing a successful modernization, to help you drive lower costs and increased agility.
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Target Leverages Google Cloud to Create Market-defining Online Experience
In the hyper-competitive world of online retail sales, ease-of-use and transaction speed can make or break business outcomes. However, a few years ago US Retail giant Target was going through a period of uncertainty.
While the company had over 1800 stores across the US with an estimated 85% of US consumers shopping at a Target store and over 25 million people visiting the Target website or using its app each month, it was still losing ground.
In spite of having millions of loyal customers, the company was dangerously late on digital and its technology wasn’t keeping pace with unstable systems to boot. The company faced the twin challenges of trying to operate today’s business as efficiently as possible and creating tomorrow’s business as quickly as possible. On the one hand it needed productivity and stability and on the other it wanted speed and disruption. Not an easy task to accomplish.
That’s when Target decided to use Google Cloud to solve its challenges. See how Target leveraged Google Cloud to create a market-defining online experience that has made customers happier and more loyal.
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