Do you want to fire up a bunch of techies? Talk about multicloud! There is no shortage of opinions. I figured we should tackle this hot topic head-on, so I recently talked to four smart folks—Corey Quinn of Duckbill Group, Armon Dadgar of Hashicorp, Tammy Bryant Butow of Gremlin, and James Watters of VMware—about what multicloud is all about, key considerations, and why you should (or shouldn’t!) do it.

Five important insights came out of these discussions. If you’re on a multicloud journey or considering one, keep reading.

Do: Choose to do multicloud for the right reasons

Don’t do multicloud because Gartner says so, implores Corey Quinn. Before embarking on a multicloud, define a “why” focused on business value journey, says Armon Dadger. For example, you might want to use services from each public cloud because of their differentiated services, according to Tammy Bryant Butow. Armon also calls out regulatory reasons, existing business relationships, and accommodating mergers and acquisitions. On the topic of M&A, Corey points out that if you acquire a company that uses another cloud, it’s usually expensive and difficult to consolidate. It can be smarter to stay put.

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Don’t: Over-engineer for workload or data portability

Thinking that you’ll build a system that moves seamlessly among the various cloud providers? Hold up, says our group of experts. Armon points out that aspects of your toolchain or architecture may be multicloud—think of some of your workflows or global network routing—but that shifting workloads or data is far from simple. Corey says that trying to engineer for “write once, run anywhere” can slow you down, and ignores the inherent uniqueness that’s part of each platform. Specifically, Corey calls out the per-cloud stickiness of identity management, security features, and even network functionality. And data gravity is still a thing, says James, that causes some to dismiss multicloud outright.

If you’re using multiple public clouds, you take advantage of the distinct value each offers, Armon says. Use native cloud services where possible so that you see the benefits from useful innovations, built-in resilience, and baked-in best practices. The value from that cloud-infused workload may outweigh the benefits of seamless portability.

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Do: Recognize different stakeholder interests and needs

James smartly points out that many multicloud debates happen because people are arguing from different perspectives. Context matters. If you’re an infrastructure engineer who invests heavily in a given cloud’s identity and access management model, multicloud looks tricky. Or if you’re a data engineer with petabytes of data homed in a particular cloud, multicloud may look unrealistic. James highlights that many developers default to multicloud because their local tools—where all the work happens—are multicloud. A developer’s IDE and preferred code framework(s) aren’t tied to any given cloud. Be aware that groups within your organization will come at multicloud from distinct directions. And this may impact your approach!

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Don’t: Go it alone

Corey talks about the importance of asking others what worked, and what didn’t. Tammy offers her best practices around sharing results from experiments. It’s about sharing knowledge and tapping into it for community benefit. Others have probably tried what you’re trying, and can help you avoid common pitfalls. If you’ve just made an architectural choice that didn’t work out, share it, and help others avoid the pain. 

Read research from analysts, go to conferences or watch videos to observe case studies, and join online communities that offer a safe place to share mistakes and learn from others.

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Do: Experiment first using techniques like multi-region deployments

If you think you can operate systems across clouds, how about you first try doing it across regions in a specific cloud, suggests Corey. Getting a system to properly work across cloud regions isn’t trivial, he says, and that experience can help you uncover where you have architectural or operational constraints that will be even worse across cloud providers.

This is great guidance if your multicloud aspirations involve using multiple clouds to power one application—versus the more standard definition of multicloud where you use different clouds for different applications—but can also surface issues in your support process or toolchain that fail when faced with distributed systems. Start with muti-region deployments and chaos engineering experiments before aggressively jumping into multicloud architectures.

The Google Cloud take

Do the things above. It’s great advice. I’ll add three more things that we’ve learned from our customers.

1. Don’t fear multicloud. You’re already doing it. You don’t single-source everything. As Corey mentioned, you probably already have one cloud for productivity tools, another for source code, another for cloud infrastructure. You’ll use software and application services from a mix of providers for a single app. You have that experience in your team and have been doing that for decades. What people do rightly worry about is using more than one infrastructure service beneath an application, as that can introduce latency, security, and logistical hurdles. Make sure you know which model your team is considering.
2. Embrace the right foundational components, including Kubernetes. Will everything run on Kubernetes? Of course not. Don’t try to do that. But it also represents the closest thing we have to a multicloud API. Companies are using Kubernetes to stripe a consistent experience across clouds. And this isn’t just to orchestrate containers, but also to manage infrastructure and cloud-native services. Also, consider where you need other fundamental consistency across clouds, including areas like provisioning and identity federation.
3. Use Google Cloud as your anchor. Here’s a fundamental question you have to decide for yourself: Are you going to bring your on-premises technology and practices to the cloud, or bring cloud technology and practices on-prem? We sincerely believe in the latter. Anchor to where you’re trying to get to. We offer Anthos as a way to build and run distributed Kubernetes fleets in Google Cloud and across clouds. By using a cloud-based backplane instead of an on-prem one, you’re offloading toil, leveraging managed services for scale and security, and introducing modern practices to the rest of your team.

We learned a lot about multicloud through these discussions, and it seems like others did too. That’s why we’re going to do a second round of interviews with a new crop of experts so that we can keep digging deeper into this topic. Stay tuned!

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.

Editor’s note: In this blog post we learn how TapClicks migrated to Google Cloud to offer their marketing customers a unified platform for data management, operations, insights, and analysis.

TapClicks is a smart marketing cloud, powered by data, that unifies our customer’s marketing. By choosing to migrate our core applications last year to Google Cloud, we cut costs, solved data-sharing concerns for our customers, and opened our stack up to a new ecosystem of possibilities. 

The core problem that we’re solving for our customers is how to manage their marketing infrastructures data and operations. Life isn’t easy for marketers now. There are 7,000 different vendors servicing this space today – creating much complexity between digital agencies, media, and brands. Marketers face challenges in navigating all of these systems, logging in and out, understanding pacing goals, and managing the flow of marketing data so they can analyze and report internally as well as to their clients at scale.

We unify omnichannel campaign data (250 API connectors and 6000 Smart Connectors ™ ) from a plethora of marketing sources on an automated data warehousing solution, creating simplicity for organizations. Over 4,000 agencies, media companies, and brands use our Marketing Operations and Data Management Platform, which imports data at scale and creates an automatic data warehouse on Google Cloud. Teams can also leverage TapClicks, like our world class Facebook connector, to import data directly into Google Data Studios.  Beyond importing and storing, we also provide data exporting to other Google solutions like Google Data Studio and Google Sheets.  We also create interactive dashboards that let stakeholders and clients analyze their data, as well as automated, multi-channel reports that go out to clients at specified times. So channel comparisons, optimizations, attribution, and calculations are easily performed.  Some of our customers are able to generate hundreds of thousands of individual reports and dashboards for their clients.

Although we may be best known for our reporting and analytics, we also empower teams managing the marketing operations workflow from customers and internal stakeholders, especially at scale. Our user-friendly, configurable system helps manage their orders and campaigns. Through automation of this process, we deliver tremendous amounts of efficiency, time saving, cost savings, and reduction of errors. The combination of these solutions makes up our unified platform, with additional capabilities like marketing intelligence that offers competitive and brand-level analysis. This is a disruptive solution in use by all leading media companies, agencies and many brands.

Partnering for possibilities

We faced a few challenges with our original tech stack, which included a mix of the leader in web services revenue, leaders in high performance data warehousing, as well as vendors on bare metal servers. 

• One challenge was around costs, which were growing. 
• Second, many of our customers work with multiple brands, and are very hesitant to share their data with the leader in web services, who’s often viewed as their competitor. 
• Third, these vendors are more focused on their own revenue rather than a true long term partnership that would enable their customers to enjoy similar success as they have experienced.

When looking at other cloud providers, Google Cloud emerged for us as the front runner. They were competitive on costs, and their native Kubernetes support was superior— a big selling point for our DevOps team. There’s also a movement in the marketing and advertising industry away from AWS toward Google Cloud because of the data-sharing concern. Finally, most of our customers are already using Google Cloud tools, so there’s brand recognition and familiarity there, and easier integrations with their own systems.

Migrating to Google Cloud

Our migration, which took about five months, involved moving a significant chunk of our infrastructure, including our core applications, using Google Kubernetes Engine (GKE). In our legacy architecture, each of our clients was assigned to one of our virtual machines (VMs), and there was a lot of unused capacity because we had to provision for the max usage. We appreciated GKE’s cloud native capabilities, especially autoscaling, a huge benefit for our web application. We have varying usage patterns during the day, and though our application is mostly used during business hours, there are also days in the month of higher usage, and autoscaling saves us time and costs. GKE also makes deployments much easier, and we anticipate a lot of benefits there for our developer environments. We’ve moved some of our microservices into GKE and plan to move more in the future. All in all, we were able to migrate our core products and the bulk of our AWS spend successfully to Google Cloud. 

We also moved from our other vendors Relational Database Service (RDS) to running MySQL on our own VMs on Google Cloud, which gives us more flexibility in terms of settings and fine tuning. We’re still trying to find the best mix as we’re modernizing our infrastructure, and we took this opportunity to migrate from MySQL 5.7 to 8.0.  

Our next stage is exploring more of the capabilities and services of Google Cloud, including BigQuery, which we’re considering for our own data warehouse. The fact that we could also run Snowflake on Google Cloud, if needed, was another selling point for our migration. 

We’re especially interested in BigQuery ML’s machine learning and natural language processing capabilities, which enabled better predictive insights. Our customers want insights from their campaigns— which are working, which are paying off, where should they invest next? Using our platform, they’re looking not only to generate reporting, but also identify opportunities to improve campaign performance. We plan to use AI and ML to improve those capabilities, so that our customers can seamlessly unlock insight and intelligence from their marketing data and campaigns.

Double-clicking on Google Cloud

For us, being able to deeply leverage and partner with Google Cloud to deliver those solutions on a single stack is critical, and we think our customers will love it. We see TapClicks and Google Cloud partnering at a level beyond what you typically see in a cloud provider relationship. Already, fifty percent of our company is working with various Google Cloud solutions, and we envision TapClicks and Google Cloud as extensions of each other, providing a single, powerful platform solution. 

Google Cloud understands the partnership concept, and their team was able to shine a light on their services and what they could bring to the table. Compared to our previous experiences, dealing with the Google Cloud team has been a true pleasure. Now that we’ve migrated, we’re ready to take our next steps into the services available to us in the Google Cloud ecosystem, and the problems we’ll continue to solve for our customers. Learn more about TapClicks and BigQuery ML.