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!

https://youtube.com/watch?v=I9sqXDqkKBM%3Fenablejsapi%3D1%26

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!

Organizations have been moving their on-premises data and applications to the cloud for the past several years, driven by reasons as varied as application modernization and content delivery to archival. In particular, we have seen migration momentum pick up in sectors like media and entertainment, where customers are rethinking how they monetize and store their valuable historical content, often while exploring Google Cloud’s many analytical and AI solutions.

Many of these customers are interested in moving their unstructured data from on-premises appliances to Google’s Cloud Storage. Over the past year, we’ve noticed an uptick in larger migrations, where customers move tens of petabytes or more of on-premises file data to Google’s flexible, secure object storage. 

For customers like Telecom Italia/TIM Brasil, Google’s fully managed Storage Transfer Service played a key role in making this transformation possible by moving data from on-premises filesystems to extensible, low-cost Cloud Storage over the network. 

“Storage Transfer Service helped us move petabyte-scale data from on-premises filesystem to Google Cloud in a highly performant and fully-managed way,” said Auana Mattar, CIO at Telecom Italia/TIM Brasil. “Setting up the transfer pipeline required performing some tests to figure out the ideal number of agents and networking settings in our on-prem environment. Once the initial setup was done, transferring data was seamless, and the service was able to saturate a 20 Gbps Partner Interconnect link.” 

While large-scale cloud migrations can be intimidating, there are a number of actions that customers can take to ensure that a multi-petabyte data transfer goes as smoothly as possible. In the past, we’ve shared general architectural guidance for customers new to their cloud journey. And for customers looking for options, Google has multiple paths to move on-premises file data to the cloud, including our fully offline Transfer Appliance.  

In this blog post, we’ll provide an updated perspective focused on how to use Storage Transfer Service to move data from on-prem to the cloud. Specifically, we’ll look at three different factors to consider prior to moving large amounts of data from your on-premises filesystem to Cloud Storage with our Storage Transfer Service. 

Understanding the source files and filesystem

If you are moving data from an on-premises filesystem, you should be aware of how your source files, and your source filesystem, can impact transfer performance. 

Each copy you make to Cloud Storage incurs some overhead from associated operations like metadata transfer, checksumming, and encryption. This means that, for a given amount of storage, transferring large numbers of very small files will take longer. As a rule of thumb, Storage Transfer Service will be most performant when moving files that are 16 MB or larger. 

If you have a large number of smaller files, you may choose to batch them using tools like tar and upload as a single object. This will improve transfer performance but will limit how you can use those files in Cloud Storage. It’s an option best considered for use cases like archival storage, where the data transferred to Google Cloud may not be managed or accessed regularly. Our Nearline, Coldline, and Archive archival tiers offer excellent performance should you ever need to retrieve the archived data. 

The source filesystem may also slow down transfer performance, particularly if the filesystem has limited read throughput. Tools like Fio can be used to test read throughput. We’ve included a command below to run a series of 1MB sequential read operations in Fio and to generate a report:

  #Install fio
> sudo apt install -y fio

#Create a new directory fiotest
> TEST_DIR=/mnt/mnt_dir/fiotest
> sudo mkdir -p $TEST_DIR

#Test read throughput
> sudo fio --directory=$TEST_DIR --direct=1 --rw=randread --randrepeat=0 --ioengine=libaio --bs=1M --iodepth=8 --time_based=1 --runtime=180 --name=read_test --size=1G

Fio will then generate a report. The final line labeled ‘bw’ represents the total aggregate bandwidth of all threads, and it can be used as a proxy for read throughput. In general, you should strive for read throughput (‘bw’) that is 1.5x of your desired upload throughput or speed. (And one easy way to increase read throughput is to ensure that the filesystem itself is not imposing any limits on maximum throughput.)

Optimizing Storage Transfer Service resources

Within the Storage Transfer Service, there are a few settings that we can adjust ahead of time to ensure optimal performance for a larger workload.

First, we should ensure that we have the right number of transfer agents for our source data. We would advise that, for any transfer job larger than 1 GB, you start with at least three agents in separate VMs, with each agent assigned at least 4 vCPU and 8 GB of RAM. In addition to providing a foundation for performant data transfer, this architecture also ensures that the transfer is fault tolerant should one agent machine become unavailable. 

Google Cloud supports up to 100 concurrent agents for a given Google Cloud project. To help you identify the right number of agents to support your workload, you should start your larger transfer first, then wait three minutes after adding each agent to ensure that throughput has stabilized. 

In general, each agent can facilitate roughly 1 Gbps of throughput for up to 10 agents, at which point it may be necessary to add more agents for a very large amount of network bandwidth. For example, in one larger migration, a customer with 20 Gbps of dedicated network capacity ran ~30 agents at once. These numbers illustrate what was required at one enterprise data center. Across all of your environments, it is important to test and monitor your throughput via Cloud Monitoring to ensure you have the right configuration for your transfer goals. 

Another area to optimize is where and how you install your agents. As we mentioned earlier, agents should be installed in separate VMs, and each host machine should dedicate at least 4 vCPUs and 8 GB of memory per agent. This is a starting off point, and larger, long-running transfers may require additional CPU or memory. For those longer jobs, we advise that you monitor CPU utilization and unused memory closely to ensure optimal performance. You should provision more CPUs when utilization exceeds 70%. Similarly, you should be ready to provision additional memory when the agent has less than 1GB of unused memory. 

Preparing your network for large-scale data transfer

The third and final area to consider is your network connectivity. While it can be easy to reduce this to the bandwidth between the source filesystem and the Google Cloud bucket, the network includes two other components that can be easier to configure: first, the network interface from the on-premises agents to the WAN; and second, the agents’ connection to the on-premises filesystem.

For the first component, the network interface from the on-premises agents to the WAN, the general guidance is to not let this become a bottleneck. Specifically, you should ensure that this interface is greater than or equal to the bandwidth you require to read from the filesystem, plus the upload bandwidth to write to Google Cloud. In other words, if you plan on moving 10 Gbps of data from on-premises to Google Cloud, you will need 20 Gbps of bandwidth between the on-premises agents to the WAN: 10 Gbps to read from the networked filesystem, and 10 Gbps to transfer and write to Google Cloud.

On-premises filesystems, and on-premises networks, come in many flavors. For the second component, how the agents connect to an on-premises filesystem, our general rule is to be mindful of latency and to test regularly. It is essential to ensure that agents run on machines that can access a networked filesystem with very low latency. 

Finally, if you are trying to maximize transfer performance, make sure you’ve configured your network to avoid bandwidth restrictions between on-premises filesystem and Google Cloud that might impact transfer speed. Storage Transfer Service will allow you to cap the bandwidth used by transfer, making it easy to minimize any impact on other production applications. Consider using tools like lperf3, tcpdump, and gsutil to measure the network bandwidth available to upload to Cloud Storage. 

In particular, gsutil is worth some additional detail. Gsutil is a Python tool that can help you perform a number of object storage management tasks in Google Cloud, including checking your agent’s connection to the Cloud Storage APIs. It can be installed via the Google Cloud SDK, or separately. In this case, you should also ensure that gsutil is available in the same on-premises VM as the Storage Transfer Service agent. 

If you’d like to use gsutil to test connectivity to Google Cloud, here’s the command:

  gsutil cp test.txt gs://my-bucket

Replace:
my-bucket with the name of your Cloud Storage bucket.

CP is a copy command that lets you copy data from on-premises to the cloud. In this case, gsutil is copying a test document, test.txt, to ensure that you have a connection with the Google Cloud APIs. You will need to create a test document before running this command. 

Test twice, transfer once

One overarching theme across all factors is that testing can help you understand performance. For many customers, a large-scale data transfer from an on-premises filesystem to Google Cloud is an unusual event. And as with any unusual event in enterprise IT, it is a great idea to make sure that each party – from network administrators to filesystem and storage experts to cloud architects – is able to test their domain multiple times, to ensure the event will proceed seamlessly. 

As you fine tune your testing in advance of a data transfer, you may want to learn more about your options for obtaining more network bandwidth, orchestrating transfer from SMB filesystems, or even how to make the right choices to save money on object storage. And we plan to share more in our blog about how customers have used our transfer offerings in the months ahead. Advanced agent setup | Cloud Storage Transfer Service Documentation

For more information about Storage Transfer Service and how to get started, please take a look at our documentation or get started via the Google Cloud console.

While the promise of artificial intelligence transforming the manufacturing industry is not new, long-ongoing experimentation hasn’t yet led to widespread business benefits. Manufacturers remain in “pilot purgatory,” as Gartner reports that only 21% of companies in the industry have active AI initiatives in production. 

However, new research from Google Cloud reveals that the COVID-19 pandemic may have spurred a significant increase in the use of AI and other digital enablers among manufacturers. According to our data—which polled more than 1,000 senior manufacturing executives across seven countries—76% have turned to digital enablers and disruptive technologies due to the pandemic such as data and analytics, cloud, and artificial intelligence (AI). And 66% of manufacturers who use AI in their day-to-day operations report that their reliance on AI is increasing.

The top three sub-sectors deploying AI to assist in day-to-day operations are automotive/OEMs (76%), automotive suppliers (68%), and heavy machinery (67%).

In fact, Bryan Goodman, Director of Artificial Intelligence and Cloud, Ford Global Data & Insight and Analytics shares, “Our new relationship with Google will supercharge our efforts to democratize AI across our business, from the plant floor to vehicles to dealerships. We used to count the number of AI and machine learning projects at Ford. Now it’s so commonplace that it’s like asking how many people are using math. This includes an AI ecosystem that is fueled by data, and that powers a ‘digital network flywheel.’”

Moving from edge cases to mainstream business needs

Why are manufacturers now turning to AI in increasing numbers? Our research shows that companies who currently use AI in day-to-day operations are looking for assistance with business continuity (38%), helping make employees more efficient (38%), and to be helpful for employees overall (34%). It’s clear that AI/ML technology can augment manufacturing employees’ efforts, whether by providing prescriptive analytics like real-time guidance and training, flagging safety hazards, or detecting potential defects on the assembly line.

In terms of specific AI use cases called out by the research, two main areas emerged: quality control and supply chain optimization. In the quality control category, 39% of surveyed manufacturers who use AI in their day-to-day operations use it for quality inspection and 35% for product and/or production line quality checks. At Google Cloud, we often speak with manufacturers about AI for visual inspection of finished products. Using AI vision, production line workers can spend less time on repetitive product inspections and can instead focus on more complex tasks, such as root cause analysis. 

In the supply chain optimization category, manufacturers said they tapped AI for supply chain management (36%), risk management (36%), and inventory management (34%).

In our day-to-day work, we’re seeing many manufacturers rethink their supply chains and operating models to better accommodate for the increased volatility that has been brought about by the pandemic and support the secular trend of consumers asking for increasingly individualized products. We’ll share more on deglobalization in the third installment of our manufacturing insights series.

AI use differs by geography, but not for the reasons you may think

The extent to which AI is already being used today varies quite strongly between geographies, according to our research. While 80% and 79% of manufacturers in Italy and Germany respectively report using AI in day-to-day operations, that percentage plummets in the United States (64%), Japan (50%) and Korea (39%).

It’s tempting to state this disparity is due to an “AI talent gap.” Although the most common barrier, just a quarter (23%) of manufacturers surveyed believe they don’t have the talent to properly leverage AI. Cost, too, does not appear to be a roadblock (21% of those surveyed). Rather, from our observations, the missing link appears to be having the right technology platform and tools to manage a production-grade AI pipeline. This is obviously the focus of our efforts and others in the space, as we believe the cloud can truly help the industry make a step change.

Looking ahead: The Golden Age of AI for manufacturing

The key to widespread adoption of AI lies in its ease of deployment and use. As AI becomes more pervasive in solving real-world problems for manufacturers, we see the industry moving away from “pilot purgatory” to the “golden age of AI.” The manufacturing industry is no stranger to innovation, from the days of mass production, to lean manufacturing, six sigma and, more recently, enterprise resource planning. AI promises to bring even more innovation to the forefront. 

To learn more about these findings and more, download our infographic here and our full report here. 

^{Research methodology
The survey was conducted online by The Harris Poll on behalf of Google Cloud, from October 15 – November 4, 2020, among 1,154 senior manufacturing executives in France (n=150), Germany (n=200), Italy (n=154), Japan (n=150), South Korea (n=150), the UK (n=150), and the U.S. (n=200) who are employed full-time at a company with more than 500 employees, and who work in the manufacturing industry with a title of director level or higher. The data in each country were weighted by number of employees to bring them into line with actual company size proportions in the population. A global post-weight was applied to ensure equal weight of each country in the global total.}