The digital media and entertainment industry is experiencing dramatic growth, as audiences migrate to online experiences and content providers seek to deliver new and innovative content. According to The Global Internet Phenomena Report, streaming video accounted for 53.7% of internet bandwidth traffic, up by 4.8% from a year ago. This rapid growth of over-the-top content is straining existing infrastructure, fueling media companies’ shift to the public clouds with their global presence and greater distribution capacities. In addition, other use cases such as gaming, social networks, AR/VR experiences, and education continue to fuel the need for intelligent media services and operations.

Today, at the 2022 NAB Show Streaming Summit, we’re excited to announce the general availability of Media CDN — a modern, extensible platform for delivering immersive experiences with unparalleled scale and intelligence. Media CDN will enable media and entertainment customers to efficiently and intelligently deliver streaming experiences to viewers anywhere in the world. The same infrastructure that Google has built over the last decade to serve YouTube content to over 2 billion users is now being leveraged to deliver media at scale to Google Cloud customers with Media CDN.

Unparalleled planet-scale reach and scale

Media CDN’s foundational advantage is the Google network. We have invested decades of resources to build tremendous capacity and reach in over 200 countries and more than 1,300 cities around the globe. Modern video applications are sensitive to fluctuations in latency, so getting content closer to users enables higher bitrates and reduces rebuffers, resulting in a superior experience for the end user. Media CDN builds on the success of the existing Cloud CDN portfolio for web and API acceleration and complements it by enabling delivery of immersive media experiences.

In addition to running on planet-scale infrastructure, Media CDN tailors delivery protocols to individual users and network conditions. Media CDN includes out-of-the-box support for QUIC (HTTP/3), TLS 1.3, and BBR, optimizing for last-mile delivery . When the Chrome team rolled out widespread support for QUIC, video rebuffer time decreased by more than 9% and mobile throughput increased by over 7%.

Media CDN also achieves industry-leading offload rates. With multiple tiers of caching, we minimize calls to origin — even for infrequently accessed content. This alleviates performance or capacity stress in the content origin and saves costs. These features are built into the product and seamlessly support customer content hosted on Google Cloud, on-premises, or on a third-party cloud.

“We are excited to leverage Media CDN to continue to deliver an exceptional streaming experience for Stan users across Australia. With Google’s massive network, and a deep reach into the ISPs, we are able to deliver the highest quality video for our users, no matter where they are”—John Hogan, Chief Technology Officer, Stan

“Our mission at U-NEXT is to deliver the highest quality and most entertaining content to our users. Google Cloud’s Media CDN helps us efficiently scale our infrastructure, which is challenging with a vast library of content. Media CDN offloaded 98.3% of requests from our origin server while delivering consistent great quality.”—Rutong Li, Chief Technology Officer, U-NEXT

Broader platform for monetization and immersive experiences

While global distribution is critical for a high-quality end-user experience, it’s only one piece of delivering a world-class platform for immersive experiences. Media CDN offers additional capabilities to enable this transformation — ad insertion, ecosystem integrations and platform extensibility, and powerful AI/ML analytics for interactive experiences.

Streaming providers can improve monetization through integrated ad serving via the Video Stitcher API, which allows manipulation of video content to dynamically insert ads.

Through extensible ecosystem integrations, Media CDN connects customers to key capabilities to simplify their operations. For example, the Transcoder API supports custom streaming formats, while the Live Stream API transcodes mezzanine live signals into direct-to-consumer streaming formats, for multiple device platforms.

Media CDN is built with AI/ML that will give viewers more control over how they see, experience, and even interact with content. For example, sports fans watching a game can obtain real-time stats and analytics, viewers can purchase items from virtual billboards, etc.

Cloud-native and developer-friendly operations

Media companies are under pressure to develop and deploy innovative experiences at a furious pace. Media CDN was built by developers, for developers, with automation and observability built in, giving media providers the speed and flexibility they need to integrate delivery provisioning and management into their content release processes.

Media CDN offers comprehensive APIs and automation tools such as Terraform. Detailed, pre-aggregated metrics and playback tracing make it easy to diagnose performance across the entire infrastructure stack. Real-time visibility is provided via Google Cloud’s operations suite, and integrates with tools that developers already use such as Grafana and ElasticSearch.

“Leveraging the same infrastructure as YouTube, Google Cloud’s Media CDN combines geographic reach, API-first architecture and integration with the Cloud operations suite. This is a transformative move that is aligned with the future of the CDN industry.”— Ghassan Abdo, Research Vice President, WW Telecom, Virtualization and CDN, IDC

“Viewers around the world are demanding best-in-class video quality and performance across modes of consumption. A video-first delivery network can be a game changer in this space. We’re excited to partner with Google Cloud and to leverage Media CDN to enable premium video experiences and customer engagements.”—Juan Martin, Founder and CTO, Firstlight Media

Planet-scale advanced security

Media CDN lets streaming media providers take advantage of Google’s decades-long experience delivering video safely, securely, and reliably. The platform includes deep integration with Google Cloud Armor for planet-scale DDoS protection and a rich set of capabilities to detect and mitigate attacks, prevent abuse, manage risk, and comply with regulatory or licensing requirements.

If you want to deliver rich, immersive experiences to global audiences with an extensible, modern delivery platform, we’d love to hear from you. For more information, including technical specifications and platform architecture, please visit cloud.google.com/media-cdn. To get started with Media CDN, contact your sales team.

Cloud offers a proven way to accelerate end-to-end chip design flows. In a previous blog, we demonstrated the inherent elasticity of the cloud, showcasing how front-end simulation workloads can scale with access to more compute resources. Another benefit of the cloud is access to a powerful, modern and global infrastructure. On-prem environments do a fantastic job of meeting sustained demand but Electronic Design Automation (EDA) tooling upgrades happen much more frequently (every six to nine months) than typical on-prem data center infrastructure upgrades (every three to five years). 

What this means is that your EDA tool can provide much better performance if given access to the right infrastructure. This is especially useful in certain phases of the design process.

Take for example, a physical verification workload. Physical verification is typically the last step in the chip design process. In simplified terms, the process consists of verifying design rule checks (or DRCs) against the process design kit (PDK) provided by the foundry. It ensures that the layout produced from the physical synthesis process is ready for handoff to a foundry (in-house or otherwise) for manufacturing. Physical verification workloads tend to require machines with large memories (1TB+) for advanced nodes. Having access to such compute resources enables more physical verification to run in parallel, increasing your confidence in the design that is being taped out (i.e., sent to manufacturing).

At the other end of the spectrum are functional verification workloads. Unlike the physical verification process described above, functional verification is normally performed in the early stages of design and typically requires machines with much less memory. Furthermore, functional verification (dynamic verification in particular) accounts for the most time (translating directly to the availability of compute) in the design cycle. Verifying faster, an ambition for most design teams, is often tied to availability of right-sized compute resources. 

The intermittent and varied infrastructure requirements for verification (both functional and physical) can be a problem for organizations with on-prem data centers. On-prem data centers are optimized for maximizing utilization—this does not directly address access to right-sized compute to deliver the best tool performance. Even if the IT and Computer Aided Design (CAD) departments choose to provision additional suitable hardware, the process of provisioning, acquiring and setting up new hardware on-prem typically takes months for even the most modern organizations. A “hybrid” flow that enables use of on-prem clusters most of the time, but provides seamless access to cloud resources as needed would be ideal.

Hybrid chip design in action

You can improve a typical verification workflow simply by utilizing a hybrid environment that provides instantaneous access to better compute. To illustrate, we chose a front-end simulation workflow, and designed an environment that replicates on-prem and cloud clusters. We also took a few more liberties to simplify the environment (described below). The simplified setup is provided in a GitHub repository for you to try out.

In any hybrid chip design flow, there are a few key considerations:

1. Connectivity between on-prem infrastructure and the cloud: Establishing connectivity to the cloud is one of the most foundational aspects of the flow. Over the years, this has also become a very well-understood field, and secure, high availability connectivity is a reality in most setups. 
   
   In our tutorial, we represent both on-prem and cloud clusters as two different networks in the cloud where all traffic is allowed to pass between these networks. While this is not a real-world network configuration, it is sufficient to demonstrate the basic connectivity model.
   
2. Connection to license server: Most chip design flows utilize tools from EDA vendors. Such tools are typically licensed, and you need a license server with valid licenses to operate the tool. License servers may remain on-prem in the hybrid flow, so long as latency to the license server is acceptable. You can also install license servers in the cloud on a Compute Engine VM (particularly sole-tenant nodes) for lower latency. Check with your EDA vendors to understand if you can rehost your license services in the cloud.
   
   In our tutorial, we use an open source tool (Icarus Verilog Simulator) and therefore, do not need a license server.
   
3. Identifying data sources and syncing data: There are three important aspects in running EDA jobs: the EDA tools themselves, the infrastructure where the tools run, and the data sources for the tool run. Tools don’t change much, and can be installed on cloud infrastructure. Data sources, on the other hand, are primarily created on-prem and updated regularly. These could be SystemVerilog files that describe the design, the testbenches or the layout files. It is important to sync data between on-prem and cloud to maintain parity. Furthermore, in production environments, it’s also important to maintain a high-performance syncing mechanism.
   
   In our tutorial, we create a file system hierarchy in the cloud that is similar to one you’d find on-prem. We transfer the latest input files before invoking the tool.
   
4. Workload scheduler configuration and job submission transparency: Most environments that leverage batch jobs use job schedulers to access a compute farm. An ideal environment finds the balance between cost and performance, and builds parameters in the system to enable predictive (and prescriptive) wrappers to job schedulers (see picture below).
   
   In our tutorial, we use the open-source SLURM job scheduler and an auto-scaling cluster. For simplicity, the tutorial does not include a job submission agent.

Other cloud-native batch processing environments such as Kubernetes can also provide further options for workload management.

Our on-prem network is called ‘onprem’ and the cloud cluster is called ‘burst’. Characteristics of the on-prem and burst clusters are specified below:

Once set up, we ran the OpenPiton regression for single and two-tile configurations. You can see the results below:

Regressions run on “burst” clusters were on average 30% faster than on “onprem”, delivering faster verification sign-off and physical verification turnaround times. You can find details about the commands we used in the repository. 

Hybrid solutions for faster time to market

Of course, on-prem data centers will continue to play a pivotal role in chip design. However, things have changed. Cloud-based, high performance compute has proved itself to be a viable and proven technology for extending on-prem data centers during the chip design process. Companies that successfully leverage hybrid chip design flows will be able to better address the fluctuating needs of their engineering teams. To learn more about silicon design on Google Cloud, read our whitepaper “Using Google Cloud to accelerate your chip design process”.