A Pro’s Tip on Choosing the Right Google Cloud Compute Options
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Where should you run your workload? It depends…Choosing the right infrastructure options to run your application is critical, both for the success of your application and for the team that is managing and developing it. This post breaks down some of the most important factors that you need to consider when deciding where you should run your stuff!
What are these services?
- Compute Engine – Virtual machines. You reserve a configuration of CPU, memory, disk, and GPUs, and decide what OS and additional software to run.
- Kubernetes Engine – Managed Kubernetes clusters. Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. You create a cluster and configure which containers to run; Kubernetes keeps them running and manages scaling, updates and connectivity.
- Cloud Run – A fully managed serverless platform that runs individual containers. You give code or a container to Cloud Run, and it hosts and auto scales as needed to respond to web and other events.
- App Engine – A fully managed serverless platform for complete web applications. App Engine handles the networking, application scaling, and database scaling. You write a web application in one of the supported languages, deploy to App Engine, and it handles scaling, updating versions, and so on.
- Cloud Functions – Event-driven serverless functions. You write individual function code and Cloud Functions calls your function when events happen (for example, HTTP, Pub/Sub, and Cloud Storage changes, among others).
What level of abstraction do you need?
- If you need more control over the underlying infrastructure (for example, the operating system, disk images, CPU, RAM, and disk) then it makes sense to use Compute Engine. This is a typical path for legacy application migrations and existing systems that require a specific OS.
- Containers provide a way to virtualize an OS so that multiple workloads can run on a single OS instance. They are fast and lightweight, and they provide portability. If your applications are containerized then you have two main options.
- You can use Google Kubernetes Engine, or GKE, which gives you full control over the container down to the nodes with specific OS, CPU, GPU, disk, memory, and networking. GKE also offers Autopilot, when you need the flexibility and control but have limited ops and engineering support.
- If, on the other hand, you are just looking to run your application in containers without having to worry about scaling the infrastructure, then Cloud Run is the best option. You can just write your application code, package it into a container, and deploy it.
- If you just want to code up your HTTP-based application and leave the scalability and deployment of the app to Google Cloud then App Engine — a serverless, fully-managed option that is designed for hosting and running web applications — is a good option for you.
- If your code is a function and just performs an action based on an event/trigger, then deploying it with Cloud Functions makes sense.
What is your use case?
- Use Compute Engine if you are migrating a legacy application with specific licensing, OS, kernel, or networking requirements. Examples: Windows-based applications, genomics processing, SAP HANA.
- Use GKE if your application needs a specific OS or network protocols beyond HTTP/s. When you use GKE, you are using Kubernetes, which makes it easy to deploy and expand into hybrid and multi-cloud environments. Anthos is a platform specifically designed for hybrid and multi-cloud deployments. It provides single-pane-of-glass visibility across all clusters from infrastructure through to application performance and topology. Example: Microservices-based applications.
- Use Cloud Run if you just need to deploy a containerized application in a programming language of your choice with HTTP/s and websocket support. Examples: websites, APIs, data processing apps, webhooks.
- Use App Engine if you want to deploy and host a web based application (HTTP/s) in a serverless platform. Examples: web applications, mobile app backends
- Use Cloud Functions if your code is a function and just performs an action based on an event/trigger from Pub/Sub or Cloud Storage. Example: Kick off a video transcoding function as soon as a video is saved in your Cloud Storage bucket.
Need portability with open source?
If your requirement is based on portability and open-source support take a look at GKE, Cloud Run, and Cloud Functions. They are all based on open-source frameworks that help you avoid vendor lock-in and give you the freedom to expand your infrastructure into hybrid and multi-cloud environments. GKE clusters are powered by the Kubernetes open-source cluster management system, which provides the mechanisms through which you interact with your cluster. Cloud Run for Anthos is powered by Knative, an open-source project that supports serverless workloads on Kubernetes. Cloud Functions use an open-source FaaS (function as a service) framework to run functions across multiple environments.
What are your team dynamics like?
If you have a small team of developers and you want their attention focused on the code, then a serverless option such as Cloud Run or App Engine is a good choice because you won’t have to have a team managing the infrastructure, scale, and operations. If you have bigger teams, along with your own tools and processes, then Compute Engine or GKE makes more sense because it enables you to define your own process for CI/CD, security, scale, and operations.
What type of billing model do you prefer?
Compute Engine and GKE billing models are based on resources, which means you pay for the instances you have provisioned, independent of usage. You can also take advantage of sustained and committed use discounts.
Cloud Run, App Engine, and Cloud Functions are billed per request, which means you pay as you go.
Conclusion
It’s important to consider all the relevant factors that play a role in picking appropriate compute options for your application. Remember that no decision is necessarily final; you can always move from one option to another.
To explore these points in more detail, please take a look at the “Where Should I Run My Stuff?” video.
For more #GCPSketchnote, follow the GitHub repo & thecloudgirl.dev. For similar cloud content follow us on Twitter at @pvergadia and @briandorsey
AI-powered Business Messages for Timely, Engaging and Helpful Conversations with Customers
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Over the last two years, we’ve seen a significant uptick in the number of people using messaging to connect with businesses. Whether it was checking hours of operation, verifying what was in stock, or scheduling a pick-up, the pandemic caused a significant shift in consumer behavior.
- 45% of users are spending more time on messaging services because of the pandemic
- 85% of consumers would like to message brands directly
With the rise in demand for messaging, consumers expect communication with businesses to be speedy, simple, and convenient. For businesses, keeping up with customer inquiries can be a labor-intensive process, and offering 24/7 support outside of store hours can be costly.
To help businesses seamlessly deliver helpful, timely, and engaging conversations with customers when and where they need help, we introduced AI-powered Business Messages.
https://youtube.com/watch?v=fcgP3RHjBLY%3Fenablejsapi%3D1%26
What is AI-powered Business Messages?
With AI-powered Business Messages, you can connect with your customers in their moment of need, in the places they’re looking for answers—such as Google Search, Google Maps, or any brand-owned channel. For instance, check out how Walmart customers in the US are able to receive real-time information on product availability, straight from a search results page.
People turn to Google when they are searching for answers to their questions, looking to buy something, or trying to accomplish a particular task with one of our many tools. In fact, 68% of all online experiences begin with a search engine.
At Google, we know how important it is for interactions with a brand to be personalized, helpful, and simple. With AI-powered Business Messages, customers are able to chat with virtual agents that understand, interact, and respond in natural ways.
We are also combining smart automation with the ability for customers to chat with live agents when it’s really needed. This approach saves your customers precious time, while also saving you money. And with Business Messages automatically handling many customer inquiries in the background, businesses have the option to distribute their human customer service agents to address other needs.
Getting started with conversational AI is easy with Bot-in-a-Box
We know it can be difficult to get started with AI. That’s why we are utilizing existing Google AI tools like Dialogflow—part of Google Cloud Contact Center AI—to create the capability within Google’s Business Messages called Bot-in-a-Box, which makes getting started with Conversational AI easy. Bot-in-a-Box allows for fast and effective adoption of automation for businesses of all sizes.
Enabling Business Messages with Bot-in-a-Box can be as simple as leveraging an existing customer FAQ document you already have, whether it’s from a web page or an internal document. And since the conversational AI is powered by Business Messages and Dialogflow working together, your chat bot is able to understand and respond to customer questions automatically without the need to write code.
Bot-in-a-Box also supports other critical journeys like “Custom Intents.” That means that your bot is able to understand the different ways customers express a similar question and respond accurately by using machine learning capabilities.
Finding success
In April 2021, Wake County courthouses in North Carolina partnered with Tango Technology to implement Business Messages when it became apparent that being able to provide the public and attorneys with around-the-clock access to information would significantly reduce the pressure on courthouse staff. Using Bot-in-a-Box, Tango Technology was able to customize a solution for Wake County Courthouse, Justice Center, and Clerk of Superior Court in just four days.
“With the combination of Google’s Business Messages, GCP, and Dialogflow, we were able to spin up an AI-driven bot for the courts in days. And the technology stack allows us to continually improve by adding functionality in an agile process.”— Mike Lotz, Co-Founder, Tango Technology
With Business Messages, North Carolina courthouses saw a 37% decrease in the call volume handled by courthouse staff. With 398,298 fewer phone calls during the first year of operation, the AI-based messages helped Wake County Courthouse work more efficiently and productively.
We’ve seen many brands benefiting from AI-powered Business Messages. For instance, Levi’s saw a 30% increase in off-hours shoppers and surpassed 85% customer satisfaction scores after implementing Business Messages. They also drove 30x more store-related questions than Levi’s website chat.
Bring Google’s conversational AI to your storefront with Business Messages
Google’s Business Messages makes it easier for businesses of all sizes to engage their existing or potential customers in a virtual conversation, when and where they need it.
To learn more, watch our Cloud Next session here or visit us at g.co/businessmessages. We have specialized services to help you get started and can share the wisdom of our channel partners and dedicated experts who specialize in unleashing the potential of conversational AI.
Google Introduces ML-based Predictive Autoscaling to Forecast Capacity and Match Scaling Demands
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At Google Cloud, we believe you get most benefits from the cloud when you scale infrastructure based on changing demand. Compute Engine allows you to configure autoscaling to save costs during periods of low demand, and add capacity to support peak loads.
When you use a managed instance group (MIG), you can have an autoscaler automatically create or delete virtual machine (VM) instances based on increases or decreases in load. However, if your application takes several minutes to initialize, creating VMs in response to growing load might not increase your application’s capacity quickly enough. For example, if there’s a large increase in load (like when users first wake up in the morning), some users might experience delays while your application is initializing on new instances.
A good way to solve this problem would be to create VMs ahead of demand so that your application has enough time to initialize beforehand. This requires knowing upcoming demand. If only we could predict the future… Well, now we can!
Introducing predictive autoscaling
Predictive autoscaling uses Google Cloud’s machine learning capabilities to forecast capacity needs. It creates VMs ahead of growing demand allowing enough time for your application to initialize.
How does it work?
Predictive autoscaling uses your instance group’s CPU history to forecast future load and calculate how many VMs are needed to meet your target CPU utilization. Our machine learning adjusts the forecast based on recurring load patterns for each MIG.
You can specify how far in advance you want autoscaler to create new VMs by configuring the application initialization period. For example, if your app takes 5 minutes to initialize, autoscaler will create new instances 5 minutes ahead of the anticipated load increase. This allows you to keep your CPU utilization within the target and keep your application responsive even when there’s high growth in demand.
Many of our customers have different capacity needs during different times of the day or different days of the week. Our forecasting model understands weekly and daily patterns to cover for these differences. For example, if your app usually needs less capacity on the weekend our forecast will capture that. Or, if you have higher capacity needs during working hours, we also have you covered.
Why should you try it?
Predictive autoscaling continuously adapts forecasted capacity to best match upcoming demand. Autoscaler checks the forecast several times per minute and creates or deletes VMs to match its prediction. The forecast itself is updated every few minutes to match recent load trends so if your growth rate is higher or lower than usual we will adjust the forecast accordingly. This gives you capacity needed to cover peak load while saving on cost when demand goes down.
You can start using predictive autoscaling without worry as it’s fully compatible with the current autoscaler. Autoscaler will calculate enough VMs to cover both forecasted as well as real-time CPU load—whichever is higher. This works with other autoscaling features as well: you can scale based on schedule, your Load Balancer request target or Cloud Monitoring metrics. Autoscaler provides enough capacity to all of your configurations by taking the highest number of VMs needed to meet all your targets.
Getting started
You can enable predictive autoscaling in the Google Cloud Console. Select an autoscaled MIG from the instance groups page and click Edit group. Change predictive autoscaling configuration from Off to Optimize for availability.
To better understand whether predictive autoscaling is good for your application, click the link See if predictive autoscaling can optimize your availability. This will show you a comparison of the last seven days with your current autoscaling configuration vs. with predictive autoscaling enabled.
In the above chart,
- Average VM minutes overloaded per day shows how often your VMs exceed your CPU utilization target. This happens when demand is higher than available capacity. Predictive autoscaling can reduce this by starting VMs ahead of anticipated load.
- Average VMs per day is a proxy for cost. This shows how much additional VM capacity you need to keep your CPU utilization within the target you have set. You can optimize your cost by adjusting Minimum instances andCPU utilization as explained below.
Optimizing your configuration
Make sure your Cool down period reflects how long it takes for your application to initialize from VM boot time until it’s ready to serve the load. Predictive autoscaling will use this value to start VMs ahead of forecasted load. If you set it to 10 minutes (600 seconds) your VMs will start 10 minutes before the load is expected to increase.
Review your autoscaling CPU utilization target and Minimum number of instances. With predictive autoscaling you no longer need a buffer to compensate for the time it takes for a VM to start. If your application works best at 70% CPU utilization you don’t need to set target to a much lower value as predictive autoscaling will start VMs ahead of usual load. A higher CPU utilization and lower Minimum number of instances allows you to reduce the cost as you don’t need to pay for additional capacity to prepare for growing demand.
Try predictive autoscaling today
Predictive autoscaling is generally available across all Google Cloud regions. For more information on how to configure, simulate and monitor predictive autoscaling, consult the documentation.
Google Cloud VMware Engine Achieves HIPAA Compliance
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We are excited to announce that as of April 1, 2021, Google Cloud VMware Engine is covered under the Google Cloud Business Associate Agreement (BAA), meaning it has achieved HIPAA compliance. Healthcare organizations can now migrate and run their HIPAA-compliant VMware workloads in a fully compatible VMware Cloud Verified stack running natively in Google Cloud with Google Cloud VMware Engine, without changes or re-architecture to tools, processes, or applications.
Healthcare organizations increasingly use cloud platforms to personalize patient care, analyze large datasets more effectively, enhance research and development collaboration, and share medical knowledge. Leveraging cloud platforms can also help healthcare organizations increase the privacy and security of information systems, including protected health information (PHI), and, as a result, better comply with applicable laws and regulations while reducing the burden of compliance. For PHI, the Health Insurance Portability and Accountability Act of 1996 (HIPAA) set standards in the United States to protect individually identifiable health information. HIPAA applies to health plans, most healthcare providers, and healthcare clearinghouses that manage PHI electronically, and to persons or entities that perform certain functions on their behalf.
With Google Cloud, organizations can leverage solutions that enable secure, continuous patient care and data-driven clinical and operational decisions with ease, while being empowered with collaboration and productivity tools. Further, Google Cloud Platform supports HIPAA compliance. We offer HIPAA-regulated customers the same products at the same pricing that is available to all customers, unlike many other cloud providers.
For healthcare organizations that leverage VMware on-premises, having a consistent, cloud-integrated platform that provides seamless access to native cloud services unlocks the opportunity to extend, migrate, and modernize healthcare IT infrastructure and applications in a fast, low-risk manner at their own pace. This is especially important for mission-critical healthcare provider workloads, where having a low-risk way to adopt the cloud is important. Google Cloud VMware Engine offers that solution. By achieving coverage under Google Cloud’s BAA, Google Cloud VMware Engine enables healthcare organizations to realize the benefits of cloud computing and stay on track with their HIPAA compliance efforts without additional complexity. This is very relevant in hybrid scenarios, where customers would like to leverage other native cloud services such as analytics and big data processing, without having to enter into multiple BAAs.
Google Cloud VMware Engine offers dedicated, isolated software-defined datacenter environments with fully redundant and dedicated 100 Gbps networking that are suitable for healthcare organizations to run applications storing and processing PHI data. Customers have the ability to encrypt their virtual storage area network (vSAN) using an external key management server. Healthcare customers can run their workloads in a native VMware environment—vSphere, vCenter, vSAN, NSX-T, and HCX—while benefiting from Google Cloud’s highly performant infrastructure to meet the needs of their workloads. Customers can connect their VMware applications to native Google Cloud services such as BigQuery and artificial intelligence (AI) to derive new insights from existing data and quickly make informed decisions.
Protecting against and mitigating the impact of ransomware attacks is top-of-mind for Healthcare organizations. This requires building a cyber resilience program and back-up strategy to prepare for how users can restore core systems or assets affected by a security (in this case, ransomware) incident. This is a critical function for supporting recovery timelines and lessening the impact of a cyber event so organizations can get back to operating their business. Google Cloud VMware Engine in combination with Google Cloud first party solutions such as Actifio Go, or partner solutions such as NetApp CVO can provide an efficient way to recover incremental point-in-time backups along with on-demand provisioning of new compute to recover both data and infrastructure from Ransomware attacks quickly and efficiently.
Healthcare customers can also use Google Cloud VMware Engine as a disaster recovery (DR) target for their on-premises VMware workloads. Healthcare organizations also need a business continuity plan for their mission critical applications. When a disaster occurs, hospitals need their data protected so they can quickly get back to treating patients. It is a HIPAA requirement that healthcare organizations must be able to recover from a natural disaster. Google Cloud VMware Engine offers a like-for-like cost-effective DR target for these customers. The DR environment can be operated without new training using the same tools as their on-premises deployment. Google Cloud VMware Engine is currently available in 12 regions across the globe including three regions in the US, which means our regional and multi-national customers can take advantage of this service for geographic diversification as well.
If you are interested in understanding more and taking advantage of Google Cloud VMware Engine, contact your Google sales team now.
For details, see HIPAA compliance on Google Cloud Platform.
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Modernizing apps on the cloud isn’t an “all or nothing” decision. Businesses want the option to modernize on-premises or choose multi-cloud solutions that meet their needs. That’s why we created a new solution for running apps anywhere – simply, flexibly, and securely. Embracing open standards, Anthos lets you run your applications, unmodified, on existing on-prem hardware investments or in the public cloud. So that you write once and deploy anywhere.
Download this report and find out how to:
- Decouple infrastructure and applications with containers and Kubernetes
- Decouple cloud teams from one another so they can work independently
- Meet the challenges of microservice management using service mesh
- Implement a zero-trust security model to enforce more granular controls while maintaining a consistent user experience
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FinOps is a concept similar to DevOps, but with a different set of goals. Cloud FinOps is an operational framework and cultural shift that brings together technology, finance and business to drive financial accountability and accelerate business value realization. In layman terms, FinOps aims to help companies achieve most out of every dollar invested on cloud technology. It includes a broad set of existing and net-new processes or frameworks that breaks silos across functions, and build a better working model to achieve collaboration, agility, ownership and value.
Rest your anxieties about the changes in mindset and organizational behavior around current financial management practices while taking full flexibility benefits of cloud. Ease your cloud migration journey and value realization as experts at Google Cloud have shared best-practices, insights and action items to implement FinOps on Google Cloud in this whitepaper. Download now!
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