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Google Cloud challenges AWS with new open-source integrations
Google today announced that it has partnered with a number of top open-source data management and analytics companies to integrate their products into its Google Cloud Platform and offer them as managed services operated by its partners. The partners here are Confluent, DataStax, Elastic, InfluxData, MongoDB, Neo4j and Redis Labs.
The idea here, Google says, is to provide users with a seamless user experience and the ability to easily leverage these open-source technologies in Google’s cloud. But there is a lot more at play here, even though Google never quite says so. That’s because Google’s move here is clearly meant to contrast its approach to open-source ecosystems with Amazon’s. It’s no secret that Amazon’s AWS cloud computing platform has a reputation for taking some of the best open-source projects and then forking those and packaging them up under its own brand, often without giving back to the original project. There are some signs that this is changing, but a number of companies have recently taken action and changed their open-source licenses to explicitly prevent this from happening.
That’s where things get interesting, because those companies include Confluent, Elastic, MongoDB, Neo4j and Redis Labs — and those are all partnering with Google on this new project, though it’s worth noting that InfluxData is not taking this new licensing approach and that while DataStax uses lots of open-source technologies, its focus is very much on its enterprise edition.
“As you are aware, there has been a lot of debate in the industry about the best way of delivering these open-source technologies as services in the cloud,” Manvinder Singh, the head of infrastructure partnerships at Google Cloud, said in a press briefing. “Given Google’s DNA and the belief that we have in the open-source model, which is demonstrated by projects like Kubernetes, TensorFlow, Go and so forth, we believe the right way to solve this it to work closely together with companies that have invested their resources in developing these open-source technologies.”
So while AWS takes these projects and then makes them its own, Google has decided to partner with these companies. While Google and its partners declined to comment on the financial arrangements behind these deals, chances are we’re talking about some degree of profit-sharing here.
“Each of the major cloud players is trying to differentiate what it brings to the table for customers, and while we have a strong partnership with Microsoft and Amazon, it’s nice to see that Google has chosen to deepen its partnership with Atlas instead of launching an imitation service,” Sahir Azam, the senior VP of Cloud Products at MongoDB told me. “MongoDB and GCP have been working closely together for years, dating back to the development of Atlas on GCP in early 2017. Over the past two years running Atlas on GCP, our joint teams have developed a strong working relationship and support model for supporting our customers’ mission critical applications.”
As for the actual functionality, the core principle here is that Google will deeply integrate these services into its Cloud Console; for example, similar to what Microsoft did with Databricks on Azure. These will be managed services and Google Cloud will handle the invoicing and the billings will count toward a user’s Google Cloud spending commitments. Support will also run through Google, so users can use a single service to manage and log tickets across all of these services.
Redis Labs CEO and co-founder Ofer Bengal echoed this. “Through this partnership, Redis Labs and Google Cloud are bringing these innovations to enterprise customers, while giving them the choice of where to run their workloads in the cloud, he said. “Customers now have the flexibility to develop applications with Redis Enterprise using the fully integrated managed services on GCP. This will include the ability to manage Redis Enterprise from the GCP console, provisioning, billing, support, and other deep integrations with GCP.”
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Google Cloud is bringing two new data centers online in 2020
At Google Cloud Next today, the company announced it is bringing two brand new data centers online in the 2020 time frame, with one in Seoul, South Korea and one in Salt Lake City, Utah.
The company, like many of its web scale peers, has had the data center building pedal to the metal over the last several years. It has grown to 15 regions, with each region hosting multiple zones for a total of 45 zones. In all, the company has a presence in 13 countries and says it has invested an impressive $47 billion (with a B) of CAPEX investment from 2016-2018.
Google Data Center Map. Photo: Google
“We’re going to be announcing the availability in early 2020 of Seoul, South Korea. So we are announcing a region there with three zones for customers to build their applications. Again, customers, either multinationals that are looking to serve their customers in that market or local customers that are looking to go global. This really helps address their needs and allows them to serve the customers in the way that they want to,” Dominic Preuss, director of product management said.
He added, “Similarly, Salt Lake City is our third region in the western United States along with Oregon and Los Angeles. And so it allows developers to build distributed applications across multiple regions in the western United States.”
In addition, the company announced that its new data center in Osaka, Japan is expected to come online some time in the coming weeks. One in Jakarta, Indonesia, currently under construction, is expected to come online the first half of next year.
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Google Cloud Run brings serverless and containers together
Two of the biggest trends in applications development in recent years have been the rise of serverless and containerization. Today at Google Cloud Next, the company announced a new product called Cloud Run that is designed to bring the two together. At the same time, the company also announced Cloud Run for GKE, which is specifically designed to run on Google’s version of Kubernetes.
Oren Teich, director of product management for serverless, says these products came out of discussions with customers. As he points out, developers like the flexibility and agility they get using serverless architecture, but have been looking for more than just compute resources. They want to get access to the full stack, and to that end the company is announcing Cloud Run.
“Cloud Run is introducing a brand new product that takes Docker containers and instantly gives you a URL. This is completely unique in the industry. We’re taking care of everything from the top end of SSL provisioning and routing, all the way down to actually running the container for you. You pay only by the hundred milliseconds of what you need to use, and it’s end-to-end managed,” Teich explained.
As for the GKE tool, it provides the same kinds of benefits, except for developers running their containers on Google’s GKE version of Kubernetes. Keep in mind, developers could be using any version of Kubernetes their organizations happen to have chosen, so it’s not a given that they will be using Google’s flavor of Kubernetes.
“What this means is that a developer can take the exact same experience, the exact same code they’ve written — and they have G Cloud command line, the same UI and our console and they can just with one-click target the destination they want,” he said.
All of this is made possible through yet another open-source project the company introduced last year called Knative. “Cloud Run is based on Knative, an open API and runtime environment that lets you run your serverless workloads anywhere you choose — fully managed on Google Cloud Platform, on your GKE cluster or on your own self-managed Kubernetes cluster,” Teich and Eyal Manor, VP of engineering, wrote in a blog post introducing Cloud Run.
Serverless, as you probably know by now, is a bit of a misnomer. It’s not really taking away servers, but it is eliminating the need for developers to worry about them. Instead of loading their application on a particular virtual machine, the cloud provider, in this case, Google, provisions the exact level of resources required to run an operation. Once that’s done, these resources go away, so you only pay for what you use at any given moment.
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Pixeom raises $15M for its software-defined edge computing platform
Pixeom, a startup that offers a software-defined edge computing platform to enterprises, today announced that it has raised a $15 million funding round from Intel Capital, National Grid Partners and previous investor Samsung Catalyst Fund. The company plans to use the new funding to expand its go-to-market capacity and invest in product development.
If the Pixeom name sounds familiar, that may be because you remember it as a Raspberry Pi-based personal cloud platform. Indeed, that’s the service the company first launched back in 2014. It quickly pivoted to an enterprise model, though. As Pixeom CEO Sam Nagar told me, that pivot came about after a conversation the company had with Samsung about adopting its product for that company’s needs. In addition, it was also hard to find venture funding. The original Pixeom device allowed users to set up their own personal cloud storage and other applications at home. While there is surely a market for these devices, especially among privacy-conscious tech enthusiasts, it’s not massive, especially as users became more comfortable with storing their data in the cloud. “One of the major drivers [for the pivot] was that it was actually very difficult to get VC funding in an industry where the market trends were all skewing towards the cloud,” Nagar told me.
At the time of its launch, Pixeom also based its technology on OpenStack, the massive open-source project that helps enterprises manage their own data centers, which isn’t exactly known as a service that can easily be run on a single machine, let alone a low-powered one. Today, Pixeom uses containers to ship and manage its software on the edge.
What sets Pixeom apart from other edge computing platforms is that it can run on commodity hardware. There’s no need to buy a specific hardware configuration to run the software, unlike Microsoft’s Azure Stack or similar services. That makes it significantly more affordable to get started and allows potential customers to reuse some of their existing hardware investments.
Pixeom brands this capability as “software-defined edge computing” and there is clearly a market for this kind of service. While the company hasn’t made a lot of waves in the press, more than a dozen Fortune 500 companies now use its services. With that, the company now has revenues in the double-digit millions and its software manages more than a million devices worldwide.
As is so often the case in the enterprise software world, these clients don’t want to be named, but Nagar tells me they include one of the world’s largest fast food chains, for example, which uses the Pixeom platform in its stores.
On the software side, Pixeom is relatively cloud agnostic. One nifty feature of the platform is that it is API-compatible with Google Cloud Platform, AWS and Azure and offers an extensive subset of those platforms’ core storage and compute services, including a set of machine learning tools. Pixeom’s implementation may be different, but for an app, the edge endpoint on a Pixeom machine reacts the same way as its equivalent endpoint on AWS, for example.
Until now, Pixeom mostly financed its expansion — and the salary of its more than 90 employees — from its revenue. It only took a small funding round when it first launched the original device (together with a Kickstarter campaign). Technically, this new funding round is part of this, so depending on how you want to look at this, we’re either talking about a very large seed round or a Series A round.
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Cloud Foundry ❤ Kubernetes
Cloud Foundry, the open-source platform-as-a-service project that more than half of the Fortune 500 companies use to help them build, test and deploy their applications, launched well before Kubernetes existed. Because of this, the team ended up building Diego, its own container management service. Unsurprisingly, given the popularity of Kubernetes, which has become somewhat of the de facto standard for container orchestration, a number of companies in the Cloud Foundry ecosystem starting looking into how they could use Kubernetes to replace Diego.
The result of this is Project Eirini, which was first proposed by IBM. As the Cloud Foundry Foundation announced today, Project Eirini now passes the core functional tests the team runs to validate the software releases of its application runtime, the core Cloud Foundry service that deploys and manages applications (if that’s a bit confusing, don’t even think about the fact that there’s also a Cloud Foundry Container Runtime, which already uses Kubernetes, but which is mostly meant to give enterprise a single platform for running their own applications and pre-built containers from third-party vendors).
“That’s a pretty big milestone,” Cloud Foundry Foundation CTO Chip Childers told me. “The project team now gets to shift to a mode where they’re focused on hardening the solution and making it a bit more production-ready. But at this point, early adopters are also starting to deploy that [new] architecture.”
Childers stressed that while the project was incubated by IBM, which has been a long-time backer of the overall Cloud Foundry project, Google, Pivotal and others are now also contributing and have dedicated full-time engineers working on the project. In addition, SUSE, SAP and IBM are also active in developing Eirini.
Eirini started as an incubation project, and while few doubted that this would be a successful project, there was a bit of confusion around how Cloud Foundry would move forward now that it essentially had two container engines for running its core service. At the time, there was even some concern that the project could fork. “I pushed back at the time and said: no, this is the natural exploration process that open-source communities need to go through,” Childers said. “What we’re seeing now is that with Pivotal and Google stepping in, that’s a very clear sign that this is going to be the go-forward architecture for the future of the Cloud Foundry Application Runtime.”
A few months ago, by the way, Kubernetes was still missing a few crucial pieces the Cloud Foundry ecosystem needed to make this move. Childers specifically noted that Windows support — something the project’s enterprise users really need — was still problematic and lacked some important features. In recent releases, though, the Kubernetes team fixed most of these issues and improved its Windows support, rendering those issues moot.
What does all of this mean for Diego? Childers noted that the community isn’t at a point where it’ll hold developing that tool. At some point, though, it seems likely that the community will decide that it’s time to start the transition period and make the move to Kubernetes official.
It’s worth noting that IBM today announced its own preview of Eirini in its Cloud Foundry Enterprise Environment and that the latest version of SUSE’s Cloud Foundry-based Application Platform includes a similar preview as well.
In addition, the Cloud Foundry Foundation, which is hosting its semi-annual developer conference in Philadelphia this week, also announced that it has certified it first to systems integrators, Accenture and HCL as part of its recently launched certification program for companies that work in the Cloud Foundry ecosystem and have at least 10 certified developers on their teams.
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Google’s hybrid cloud platform is now in beta
Last July, at its Cloud Next conference, Google announced the Cloud Services Platform, its first real foray into bringing its own cloud services into the enterprise data center as a managed service. Today, the Cloud Services Platform (CSP) is launching into beta.
It’s important to note that the CSP isn’t — at least for the time being — Google’s way of bringing all of its cloud-based developer services to the on-premises data center. In other words, this is a very different project from something like Microsoft’s Azure Stack. Instead, the focus is on the Google Kubernetes Engine, which allows enterprises to then run their applications in both their own data centers and on virtually any cloud platform that supports containers.
As Google Cloud engineering director Chen Goldberg told me, the idea here it to help enterprises innovate and modernize. “Clearly, everybody is very excited about cloud computing, on-demand compute and managed services, but customers have recognized that the move is not that easy,” she said and noted that the vast majority of enterprises are adopting a hybrid approach. And while containers are obviously still a very new technology, she feels good about this bet on the technology because most enterprises are already adopting containers and Kubernetes — and they are doing so at exactly the same time as they are adopting cloud and especially hybrid clouds.
It’s important to note that CSP is a managed platform. Google handles all of the heavy lifting like upgrades and security patches. And for enterprises that need an easy way to install some of the most popular applications, the platform also supports Kubernetes applications from the GCP Marketplace.
As for the tech itself, Goldberg stressed that this isn’t just about Kubernetes. The service also uses Istio, for example, the increasingly popular service mesh that makes it easier for enterprises to secure and control the flow of traffic and API calls between its applications.
With today’s release, Google is also launching its new CSP Config Management tool to help users create multi-cluster policies and set up and enforce access controls, resource quotas and more. CSP also integrates with Google’s Stackdriver Monitoring service and continuous delivery platforms.
“On-prem is not easy,” Goldberg said, and given that this is the first time the company is really supporting software in a data center that is not its own, that’s probably an understatement. But Google also decided that it didn’t want to force users into a specific set of hardware specifications like Azure Stack does, for example. Instead, CSP sits on top of VMware’s vSphere server virtualization platform, which most enterprises already use in their data centers anyway. That surely simplifies things, given that this is a very well-understood platform.
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Google doubles down on its Asylo confidential computing framework
Last May, Google introduced Asylo, an open-source framework for confidential computing, a technique favored by many of the big cloud vendors because it allows you to set up trusted execution environments that are shielded from the rest of the (potentially untrusted) system. Workloads and their data basically sit in a trusted enclave that adds another layer of protection against network and operating system vulnerabilities.
That’s not a new concept, but, as Google argues, it has been hard to adopt. “Despite this promise, the adoption of this emerging technology has been hampered by dependence on specific hardware, complexity and the lack of an application development tool to run in confidential computing environments,” Google Cloud Engineering Director Jason Garms and Senior Product Manager Nelly Porter write in a blog post today. The promise of the Asylo framework, as you can probably guess, is to make confidential computing easy.
Asylo makes it easier to build applications that can run in these enclaves and can use various software- and hardware-based security back ends like Intel’s SGX and others. Once an app has been ported to support Asylo, you should also be able to take that code with you and run it on any other Asylo-supported enclave.
Right now, though, many of these technologies and practices around confidential computing remain in flux. Google notes there are no set design patterns for building applications that then use the Asylo API and run in these enclaves, for example.The different hardware manufacturers also don’t necessarily work together to ensure their technologies are interoperable.
“Together with the industry, we can work toward more transparent and interoperable services to support confidential computing apps, for example, making it easy to understand and verify attestation claims, inter-enclave communication protocols, and federated identity systems across enclaves,” write Garms and Porter.
And to do that, Google is launching its Confidential Computing Challenge (C3) today. The idea here is to have developers create novel use cases for confidential computing — or to advance the current state of the technologies. If you do that and win, you’ll get $15,000 in cash, $5,000 in Google Cloud Platform credits and an undisclosed hardware gift (a Pixelbook or Pixel phone, if I had to guess).
In addition, Google now also offers developers three hands-on labs that teach how to build apps using Asylo’s tools. Those are free for the first month if you use the code in Google’s blog post.
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Google’s still not sharing cloud revenue
Google has shared its cloud revenue exactly once over the last several years. Silence tends to lead to speculation to fill the information vacuum. Luckily there are some analyst firms who try to fill the void, and it looks like Google’s cloud business is actually trending in the right direction, even if they aren’t willing to tell us an exact number.
When Google last reported its cloud revenue, last year about this time, they indicated they had earned $1 billion in revenue for the quarter, which included Google Cloud Platform and G Suite combined. Diane Greene, who was head of Google Cloud at the time, called it an “elite business.” but in reality it was pretty small potatoes compared to Microsoft’s and Amazon’s cloud numbers, which were pulling in $4-$5 billion a quarter between them at the time. Google was looking at a $4 billion run rate for the entire year.
Google apparently didn’t like the reaction it got from that disclosure so it stopped talking about cloud revenue. Yesterday when Google’s parent company, Alphabet, issued its quarterly earnings report, to nobody’s surprise, it failed to report cloud revenue yet again, at least not directly.
Google CEO Sundar Pichai gave some hints, but never revealed an exact number. Instead he talked in vague terms calling Google Cloud “a fast-growing multibillion-dollar business.” The only time he came close to talking about actual revenue was when he said, “Last year, we more than doubled both the number of Google Cloud Platform deals over $1 million as well as the number of multiyear contracts signed. We also ended the year with another milestone, passing 5 million paying customers for our cloud collaboration and productivity solution, G Suite.”
OK, it’s not an actual dollar figure, but it’s a sense that the company is actually moving the needle in the cloud business. A bit later in the call, CFO Ruth Porat threw in this cloud revenue nugget. “We are also seeing a really nice uptick in the number of deals that are greater than $100 million and really pleased with the success and penetration there. At this point, not updating further.” She is not updating further. Got it.
That brings us to a company that guessed for us, Canalys. While the firm didn’t share its methodology, it did come up with a figure of $2.2 billion for the quarter. Given that the company is closing larger deals and was at a billion last year, this figure feels like it’s probably in the right ballpark, but of course it’s not from the horse’s mouth, so we can’t know for certain. It’s worth noting that Canalys told TechCrunch that this is for GCP revenue only, and does not include G Suite, so that would suggest that it could be gaining some momentum.
Frankly, I’m a little baffled why Alphabet’s shareholders actually let the company get away with this complete lack of transparency. It seems like people would want to know exactly what they are making on that crucial part of the business, wouldn’t you? As a cloud market watcher, I know I would, and if the company is truly beginning to pick up steam, as Canalys data suggests, the lack of openness is even more surprising. Maybe next quarter.
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Nvidia’s T4 GPUs are now available in beta on Google Cloud
Google Cloud today announced that Nvidia’s Turing-based Tesla T4 data center GPUs are now available in beta in its data centers in Brazil, India, Netherlands, Singapore, Tokyo and the United States. Google first announced a private test of these cards in November, but that was a very limited alpha test. All developers can now take these new T4 GPUs for a spin through Google’s Compute Engine service.
The T4, which essentially uses the same processor architecture as Nvidia’s RTX cards for consumers, slots in-between the existing Nvidia V100 and P4 GPUs on the Google Cloud Platform . While the V100 is optimized for machine learning, though, the T4 (as its P4 predecessor) is more of a general-purpose GPU that also turns out to be great for training models and inferencing.
In terms of machine and deep learning performance, the 16GB T4 is significantly slower than the V100, though if you are mostly running inference on the cards, you may actually see a speed boost. Unsurprisingly, using the T4 is also cheaper than the V100, starting at $0.95 per hour compared to $2.48 per hour for the V100, with another discount for using preemptible VMs and Google’s usual sustained use discounts.
Google says that the card’s 16GB memory should easily handle large machine learning models and the ability to run multiple smaller models at the same time. The standard PCI Express 3.0 card also comes with support for Nvidia’s Tensor Cores to accelerate deep learning and Nvidia’s new RTX ray-tracing cores. Performance tops out at 260 TOPS and developers can connect up to four T4 GPUs to a virtual machine.
It’s worth stressing that this is also the first GPU in the Google Cloud lineup that supports Nvidia’s ray-tracing technology. There isn’t a lot of software on the market yet that actually makes use of this technique, which allows you to render more lifelike images in real time, but if you need a virtual workstation with a powerful next-generation graphics card, that’s now an option.
With today’s beta launch of the T4, Google Cloud now offers quite a variety of Nvidia GPUs, including the K80, P4, P100 and V100, all at different price points and with different performance characteristics.
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Google’s Cloud Spanner database adds new features and regions
Cloud Spanner, Google’s globally distributed relational database service, is getting a bit more distributed today with the launch of a new region and new ways to set up multi-region configurations. The service is also getting a new feature that gives developers deeper insights into their most resource-consuming queries.
With this update, Google is adding to the Cloud Spanner lineup Hong Kong (asia-east2), its newest data center location. With this, Cloud Spanner is now available in 14 out of 18 Google Cloud Platform (GCP) regions, including seven the company added this year alone. The plan is to bring Cloud Spanner to every new GCP region as they come online.
The other new region-related news is the launch of two new configurations for multi-region coverage. One, called eur3, focuses on the European Union, and is obviously meant for users there who mostly serve a local customer base. The other is called nam6 and focuses on North America, with coverage across both costs and the middle of the country, using data centers in Oregon, Los Angeles, South Carolina and Iowa. Previously, the service only offered a North American configuration with three regions and a global configuration with three data centers spread across North America, Europe and Asia.
While Cloud Spanner is obviously meant for global deployments, these new configurations are great for users who only need to serve certain markets.
As far as the new query features are concerned, Cloud Spanner is now making it easier for developers to view, inspect and debug queries. The idea here is to give developers better visibility into their most frequent and expensive queries (and maybe make them less expensive in the process).
In addition to the Cloud Spanner news, Google Cloud today announced that its Cloud Dataproc Hadoop and Spark service now supports the R language, in addition to Python 3.7 support on App Engine.
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