Engineers that build last-mile delivery services belong to an elite order, a hallowed subcategory. Delivery customers are incredibly demanding when it comes to speed and convenience, and the services they use must take variables like increased traffic, road conditions, human error, and even driver availability into account every day.

Nuro is a company with a new approach to delivery services. Nuro has a fleet of autonomous vehicles designed to address many of the problems related to last-mile delivery. And every day, these vehicles — and their sensors — generate a lot of data before parking for the night. For Nuro engineers, that data can help them understand the impact of new on-road features, make improvements to their vehicles’ software, and ensure even better deliveries for their customers.

For Nuro, the key challenge is how to move petabytes of data as quickly, securely, and easily as possible from their edge environments, like vehicle depots, to Google’s Cloud Storage. For this delivery effort, Nuro selected Google’s Transfer Appliance with its new online transfer capability, now generally available.

Helping Nuro to speed up data delivery from the edge to the cloud

Like many Google Cloud customers, Nuro collects data from remote environments, like vehicle depots, that have different networking and storage capabilities when compared to a traditional data center. For a transfer solution to be effective moving unstructured data from these environments to the cloud, the solution needs to be easy to deploy and automate, while still providing similar performance as a more complicated alternative.

The Transfer Appliance was built for this use case. It arrives to customers as a physical appliance with a preconfigured version of Google’s Storage Transfer Service software already installed. Customers can move files to the appliance by using SFTP or SCP, or, alternately, can mount the appliance as an NFS share and copy target. Data can be stored locally on the appliance or transferred over the network, and secure encryption — at-rest and in-flight — is enabled by default.

With these new appliances, Nuro will be able to automate much of their storage transfer needs. When their autonomous vehicles return to the depot, they can move data like software logs, LIDAR data, and sensor data — all ideal fits for Google’s Cloud Storage — from parked vehicles to the Transfer Appliance. Online transfers can then be performed throughout the day, ensuring a steady stream of valuable data in the cloud for developers to analyze and use in their nightly builds. All of this will help Nuro’s engineering leaders like Jie Pan to run more productive development teams with less operational overhead.

“Our autonomous vehicles generate a tremendous amount of useful data, and our goal is to get that data to our engineers as soon as possible,” said Jie Pan, Engineering Manager at Nuro. “When vehicles return to the depot, we can move data hourly into Cloud Storage over the network. We also have the flexibility to return the Transfer Appliance back to Google Cloud. Most importantly, this rapid transfer architecture gives a meaningful boost to engineering productivity and development velocity.”

Going the extra mile

Engineering and infrastructure leaders understand the value of delivering the right data to the right teams, as fast as possible. By adding preconfigured, over-the-network transfer into a turnkey Transfer Appliance, Google Cloud customers can more easily automate these data deliveries by scheduling regular migrations of on-premises files, objects, and other unstructured data to our Cloud Storage.

As Nuro continues to grow their manufacturing and testing footprint, they plan to use Transfer Appliances to further scale and simplify their data migration from on-premises to Google Cloud. Cutting the time to migrate their data by more than half will make for happier, more productive developers, and that will help Nuro bring us all the future of delivery a little faster.

If you’d like to learn more about Transfer Appliance and its new online transfer capability, click here or reach out to your Google Cloud account team.

On April 6th, 2022, Google Cloud established a new partnership with CAST, to help accelerate the migration and application modernization programs of customers worldwide, complementing the Google capabilities already available through the Google Cloud Application Modernization Program (CAMP).

Application Rationalization (App Rat) is the first step towards a cloud adoption or migration journey, through which you go over the application inventory to determine which applications should be Retired, Retained, Refactored, Replatformed, or Reimagined.

Why is this important to you?

Have the majority of your in-house applications still not moved to the cloud? How much time does your development team spend on support (bug fix, tickets, etc.) versus feature(s) development? Have Infrastructure/Platform dependencies ever delayed product rollout? Would an auto-scalable, managed cloud, increase stakeholder buy-in?

Can Google simplify this journey?

Google Cloud Application Modernization Program (CAMP) has been designed as an end-to-end framework to help guide organizations through their modernization journey by assessing where they are today, and provide a path forward. When it comes to App Rationalization, this depends on what your role is.

Step 1 (Assess): Who is the target audience? The Platform team (or) the Application team?

This determines what kind of challenges we are trying to solve. For e.g. the centralized platform team wants to set some guardrails on how the App teams deploy their apps. Streamlining this would allow the platform team to mature themselves into the SRE territory. The application team, on the other hand, loves flexibility, and the ability to perform Continuous Delivery.

These examples are only the tip of the iceberg. Most of the enterprise customers have a majority of their applications in the legacy world. Unless we move those business critical applications to the cloud, it’s impossible to mature as an enterprise. For more information, check State of DevOps 2021 report.

Step 2 (Analyze): Google Cloud offers the tooling and the framework to analyze your legacy applications.

Platform Owner (persona), usually have very little information on which workloads are a good fit for modernization.

Google’s StratoZone® SaaS platform provides customers with a data-driven cloud decision framework. The StratoProbe® Data Collector Application delivers the ability to easily deploy and scale the discovery of a customer’s IT environment for Private, Public, or Hybrid-cloud planning. To ease and accelerate the VM migration journey, Google Cloud offers assistance and guidance in making the right decisions when deciding to go to cloud.

Google’s mFit aims at unblocking customers in their transformation by providing workload selection for successful on-boarding, at scale, to Anthos, GKE and Cloud Run , in both pre-sales (e.g. proof-of-concept/proof-of-value) and post-sales (e.g. pilot and at scale execution) scenarios.

App and/or Business Owners (persona), get involved in a 1-week workshop, using CAST Highlight, which would provide rapid portfolio assessment through automated source code analysis for Cloud Readiness, Open Source risks, Resiliency, and Agility.

Step 3 (Plan & execute): Each organization is different. Some may follow the “Migration Factory” approach, and some may follow “Modernization Factory”, and some may follow both. Irrespective of which approach you choose to follow, it is important to plan just enough, so that you can start your execution. Ensure to set the OKRs, that would help with the right measurements, before you start the execution. The actual learning from the execution helps the team(s) to learn more about the cloud migration process, and refine it based on their organization.

Using CAST Highlight in the assessment step previously, we get the recommendation for the analyzed applications. From there, for certain workloads, we can use Migrate to Containers, to automate the containerization of suitable workloads. However, there are certain applications that require manual code changes. You have a few options for that,

• Our experts can help you get started.
• Our partners can help you

Step 4 (Measure & reiterate): Measure the progress using the predefined metrics in the previous step. Celebrate the wins. Consistently share the learnings and best practices with the developer community. Pick the next challenge

Take the next step

Tell us what you’re solving for. A Google Cloud expert will help you find the best solution.

Founded in 2015, Rapido operates a mobile app-based two-wheeler taxi service across more than 40 cities in India. More than 100,000 drivers provide Rapido two-wheeler taxi services, while more than 400,000 current or prospective drivers have downloaded the company’s app. Consumers have downloaded the app more than 5 million times, and the business receives about 5 million orders per month for taxi services.

“If a consumer wants to travel from point A to point B, they simply open up the app on their smartphone, select pickup and dropoff locations, and tap ‘request,'” explains Rishikesh SR, Co-Founder, Rapido. “We can track that request to the nearest available two-wheeler taxi and allocate the job to the right driver.”

Google a compelling opportunity

The business started operations on a cloud service. However, Google presented a compelling opportunity to Rapido to improve its location-based intelligence through a wide range of cloud-based map APIs, infrastructure, and mobile app development services. “Google Maps Platform was particularly interesting to our business and we saw enormous potential to use it to improve our service and gain a competitive edge,” says Rishikesh.

Rapido elected to use Geocoding API to enable its app to convert addresses to geographic coordinates, and the reverse, allowing consumers to identify point A and point B on their journey. The company also uses Directions API to identify the fastest route between pickup and dropoff locations, which enables Rapido to provide users with approximate prices for trips. Distance Matrix API is used to calculate the travel times and distances between locations, and Maps SDKs for Android and iOS to add interactive maps to the app.

Google Cloud Results

• Enables the business to optimize use of Maps Platform APIs
• Takes 5 million orders per month
• Establishes a robust platform for expansion

“Google Maps Platform is particularly useful for us as it identifies optimum routes for two-wheel services,” says Rishikesh. “This saves us time and money, while helping us deliver a better consumer experience.”

In addition, Rapido uses the Snap to Roads service to deliver best-fit geometry for sets of GPS coordinates within the Roads API, which identifies and provides metadata about the roads on which drivers travel. “Snap to Roads in Roads API allows us to optimize the path drivers take and helps ensure the fare charged is accurate,” says Rishikesh.

Improving user experiences

As Rapido matured, Rishikesh considered how to improve the user experience of the app while reducing the business’ costs. In 2017, Rapido asked Google Cloud Premier Partner and Google Maps Platform Partner Searce to help meet these objectives. Searce provided the technical and business advice that prompted Rapido to take advantage of the unlimited API calls, 24-hours-a-day, 7-days-a-week support, and strict SLAs available through the Google Maps APIs Premium Plan for Asset Tracking.

Searce also helped Rapido optimize its API calls and integrate directions and distance calls with the Roads API to smooth out variations in GPS readings received from the handsets of two-wheeler taxi owners. Finally, Searce helped Rapido deploy the Firebase mobile and web development platform to automate the mode configuration of the API keys, rather than maintain a time-consuming manual process. This move also minimized the likelihood of any issues arising if Rapido decided to start multiple projects or add more licenses, or combine Google Maps APIs Premium Plan for Asset Tracking with an external license.

Technical skill and experience

Searce finalized its initial engagement with Rapido last year and provides ongoing support and advice. “Searce’s technical skill with Google Maps Platform put us on the right path to provide a better, more relevant user experience,” says Rishikesh.

Rapido also now uses a range of Firebase services for app development, testing, and modification. The business employs Google Analytics for Firebase to measure customer use of and engagement with its app, enabling the business to make informed decisions about where to direct its resources. In addition, the business is using the Firebase Test Lab app-testing infrastructure to test its Android and iOS apps across a range of device configurations, view the outcomes, and make changes as needed.

Rapido uses Firebase Remote Config to change the app’s behavior and appearance on the fly in response to the results of A/B testing across sections of its user base. Furthermore, Rapido uses Firebase Crashlytics to provide app crash reports to its Firebase console.

Firebase Dynamic Links allows Rapido to direct users to linked content in the iOS or Android version of the app, while Firebase Cloud Messaging enables Rapido to deliver notifications and other messages to users. Cloud Firestore provides a NoSQL cloud database to store and sync data for the Rapido app.

Rapido has also started using the Firebase Realtime Database to store and sync information about customers that can be used to provide a more informed, personalized service.

At the same time, Rapido uses a BigQuery data warehouse to process about 10TB of data every month for analysis and reporting that supports decision-making across the business. The organization is also using application containerization through Google Kubernetes Engine on Google Cloud Platform.

“We are moving our apps from dedicated virtual machines to a scalable containerized environment that consumes fewer resources,” says Rishikesh. “It makes sense to work with Google – the business that designed the Kubernetes container-orchestration system.”

Rapido has also started using the Cloud Functions event-driven serverless compute platform to process smaller jobs. “This is ideal for small use cases in particular as we do not have to spin up virtual machine instances,” says Rishikesh.

Finally, Rapido is using image classification through the Vision AI service to analyze riders’ documentation, such as driving licenses. This allows the business to verify details such as names, addresses, and expiry dates with more than 90 percent accuracy – a high rate in a country where each state has its own license template.

Expanding rapidly to new cities

The Rapido app enables drivers to pick up customers quickly – in most cases, between 2 and 5 minutes.

Overall, the high-quality experience for consumers and drivers delivered by Google Maps Platform, combined with Firebase, has helped power Rapido to robust growth. The business now takes more than 5 million orders per month.

“With Google, we are delivering the right experience to users through Google Maps Platform, Firebase, and Google Cloud Platform services,” concludes Rishikesh. “We have realized our vision faster and now have a robust platform to grow in the future.”

Today’s enterprise applications are often assembled across distributed environments. This includes the integration of services across multi-cloud, multi-SaaS and on-premises environments. While the approach has the advantage of enabling enterprises to choose the best service available to support their applications, it adds the complexity of delivering services across heterogeneous environments. To solve this, Cloud Load Balancing supports an open cloud strategy, which includes:

• Supporting universal traffic management policies across heterogeneous environment by leveraging open source and open standards
• Enabling a global front-end so applications can leverage a common set of policies and security postures
• Providing tools that give your users the highest possible performance and reliability

Universal traffic management with open source and open standards

Kubernetes is a great solution for managing containers across environments. We believe that traffic management policies should also be supported across environments. Cloud Load Balancing creates homogeneous traffic policies across highly distributed heterogeneous environments by supporting standard-based traffic management in a fully managed solution, and allowing open source Envoy Proxy sidecars to be used on-premises or in a multi-cloud environment, using the same traffic management as our fully managed Cloud Load Balancers.

As enterprises start modernizing services and refactor monolithic applications, they require solutions that can provide consistent traffic management across distributed systems at scale. But organizations want to invest their time and resources innovating and building new applications — not on the infrastructure and networking required to deploy and manage these services. Envoy is an open-source high-performance proxy that runs alongside the application to deliver common platform-agnostic networking capabilities, including:

• New load balancing algorithms, (e.g. round robin, ring hash, least conns)
• Weighted traffic splitting
• Fault injection
• Request mirroring
• Outlier detection
• Additional header transformation options
• Request retries
• Additional backend session affinity options
• Cross-origin resource sharing (CORS)

Hybrid Load Balancing across multi-cloud and private clouds

Over the years Google has deployed Load Balancers across 173+ Edge Pop locations, delivering customer applications at massive-scale on Google infrastructure. And now Google Cloud has introduced Hybrid Load Balancing, extending our Load Balancing capabilities beyond Google’s network to on-premises private clouds and multi-cloud solutions. This allows our customers to migrate applications to the cloud iteratively, or build hybrid applications that are assembled from services that are running across heterogeneous environments.

Supporting modern application delivery with HTTP3/QUIC

Cloud Load Balancing is a fully distributed load balancing solution that balances user traffic (HTTP(s), HTTPS/2, HTTPS/3 with gRPC, TCP/SSL, UDP, and QUIC) to multiple backends to avoid congestion, reduce latency, increase security, and reduce costs. It is built on the same frontend-serving infrastructure that powers Google services, supporting millions of queries per second with consistent high performance and low latency.

To serve massive amounts of traffic, Google built the first scaled-out software-defined load balancing, Maglev, which has been serving global traffic since 2008. It has sustained the rapid global growth of Google services, and it also provides network load balancing functions for Google Cloud Platform customers. To accommodate ever-increasing traffic, Maglev is specifically optimized for packet processing performance with Linux Kernel Offload. Maglev is also equipped with consistent hashing and connection tracking features, to minimize the negative impact of unforeseen faults and failures on connection-oriented protocols.

Another key enabler to support this global-scale is that our Cloud Load Balancers are built on top of QUIC(RFC9000), a protocol developed from the original Google QUIC) (gQUIC). HTTP/3 is supported between the External HTTP(S) Load Balancer, Cloud CDN, and end clients. And once enabled, customers typically see dramatic improvements in performance and throughput.

Google Cloud already supports HTTP3 in Cloud Load Balancer. To use HTTP/3 for your applications, you can enable it on your external HTTPS Load Balancers via the Google Cloud Console or the gCloud SDK with a single click.

If your service is sensitive to latency, QUIC will make it faster because it establishes connections with reduced handshakes. When a web client uses TCP and TLS, it requires two to three round trips with a server to establish a secure connection before the browser can send a request. With QUIC, if a client has connected with a given server before, it can start sending data without any round trips, so your web pages will load faster.

QUIC has advantages over legacy TCP as follows.

Summary

Since 2008, Google has been an innovator in software-defined networking, supporting applications running at massive scale. Google Cloud Load Balancers support HTTP3 and QUIC as a next generation web transport protocol, which significantly improves customer traffic latency. Google Load Balancers also have incorporated the Envoy proxy as a foundational technology, providing our customers with advanced traffic management that’s compatible with the open source Envoy ecosystem. This allows our users to have the choice to combine Google’s fully-managed Cloud Load Balancers with open source Envoy Proxies, to enable consistent traffic management capabilities across a multi-cloud distributed environment. And with Hybrid Load Balancing, customers can leverage our 173+ world wide PoPs to seamlessly manage traffic across Google Cloud, on-premises and other cloud providers.

Google Cloud Load Balancers include all these capabilities natively. And when used together, they support globally-scaled applications that run seamlessly across the heterogeneous environments many enterprises deploy today.