Google Cloud Results

• Enables accurate allocation of marketing spend to underserved areas
• Supports expansion into international markets
• Helps ensure accurate forecasting of inventory levels
• Improves accuracy of forecasted delivery times by at least 60%

Operating in India since 2011, Rebel Foods has grown from a brick-and-mortar business that provided wraps to customers to a cloud kitchen that delivers cuisine to about one million consumers per month. “We started with the Faasos food brand and now we have scaled up to 10 brands,” says Soumyadeep Barman, Chief Technology Officer at Rebel Foods. “We have doubled our revenue every year from 2014 until now, and we operate kitchens in 15 cities across India. Each kitchen offers at least seven of our brands to customers.”

Barman attributes Rebel Foods’ success to the fact that it is a full stack company. “We procure, we have our own inventory, we prepare the food, we deliver the food to customers, and we make sure customers are delighted every time they order,” he says.

The rapid emergence and adoption of mobile technologies and services in India gave the business its opportunity to expand quickly. “The boom in applications and the web really got going in India in about 2014,” Barman says. “The subsequent emergence of smart devices and mobile applications opened up new markets, including older people who had not really used a computer until then.”

The business released the first iteration of its mobile application in 2013 on servers in an on-premises data center. “However, we experienced breakages because our infrastructure was not scalable or dependable enough, and we decided to move to another solution,” Barman says.

Google Maps Platform delivers opportunity

In 2014, Rebel Foods decided to move to the cloud and selected Google Cloud because of its stability, reliability, and scalability.

The business also wanted to take advantage of the opportunities Google Maps Platform presented to improve the efficiency and effectiveness of its delivery service. With 175 kitchens delivering to about 900 locations across India, Rebel Foods needs to provide estimated delivery times and meet delivery guarantees, while accounting for all the factors that might affect how quickly a rider can reach a customer’s doorstep.

The business turned to Google Maps Platform Premier Partner Searce for support in leveraging Google Maps Platform APIs to deliver a compelling customer experience and improve its efficiency. “Searce helped us determine the Google Maps Platform APIs we should use across our mobile applications and websites, and how many licenses we needed to conduct activities like calculating estimated delivery time and reviewing order heat maps,” Barman says. “Thanks to the firm’s support, Google Maps Platform APIs were a game changer for us.”

Mapping customer locations

Customers accurately pinpoint their location in a map through functionality made available through the Places API and Geocoding API, in conjunction with the JavaScript API. Drivers use the Directions API to identify the quickest route to customers.

Customers can also track the progress of delivery and estimated time of arrival using an Android or iOS application, or the brand websites.

Deploying Google Maps Platform APIs enabled Rebel Foods to improve by up to 60 percent the accuracy of forecasted delivery times. “Rather than tell a customer we can reach them in, say, 45 minutes, based on previous experience and gut feeling, we can retrieve an accurate traffic scenario and calculate delivery times based on traffic congestion levels and likely average speeds,” Barman says

Allocating budget effectively

Google Maps Platform also allows the business to combine mapping of customers to individual kitchens and to how often customers place orders – and for what value. This enabled the business to understand where to allocate budget for local marketing to stimulate demand in underserved areas.

Google Maps Platform technologies complement Rebel Foods’ use of Google Cloud Platform services such as the BigQuery analytics data warehouse to process data used to forecast inventory levels and provide recommendations to customers based on previous usage and behaviors. The business also runs its key applications in Kubernetes Engine to achieve cost-effective scalability, so it can expand to international markets.

“We are targeting growth into a range of international markets in January 2019, including Australia, the Middle East, and Southeast Asia,” Barman says. “With the user data and experience provided by Google Maps Platform in particular, we are poised for success.”

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.

Casper Labs announced a collaboration with Google Cloud that will allow developers to launch public and/or private Casper nodes directly from Google Cloud. This enables a much more seamless and highly secure process for the millions of developers who want to build in blockchain environments without having to learn new, highly specialized programming languages. Additionally, Google Cloud will provide its scalable and reliable infrastructure to developers building on the Casper Protocol. 

Blockchain technology is maturing 

As blockchain technology matures, a growing number of businesses are embracing it as a key way to drive new efficiencies and realize cost savings. 

According to a recent Casper Labs study, 87% of executives polled in the United States, United Kingdom and China reported plans to invest in a blockchain solution in 2023. This is due in no small part due to recent innovations that help organizations overcome the so-called Blockchain Adoption Trilemma, which previously held that it was impossible for any blockchain to be simultaneously a) decentralized, b) scalable, and c) secure.

Thanks to the rise of proof-of-stake blockchains like Casper, new models have emerged that enable a more scalable and secure architecture that no longer forces a compromise on decentralization. 

Another trend facilitating these growing adoption rates is the rise of WebAssembly (WASM) as a baseline technology for newer blockchains, including Casper. WASM (created by W3C) makes application development in blockchain environments far more accessible and interoperable to the millions of developers worldwide who specialize in languages like Java, Javascript, C++ and Rust. Previously, any blockchain-based build required a high degree of specialized developer knowledge, which made it a much more challenging option for most organizations. 

Meet Casper

Casper is a permissionless, decentralized public blockchain based on WASM that was built explicitly to foster enterprise adoption of blockchain technology. Beyond its more accessible model, Casper is the first and only blockchain to offer native upgradable smart contracts. This means that organizations can have the option to securely and consistently update software code even after it is running on Casper. This gives organizations the control and flexibility to use industry best practices, such as continuous deployment and continuous integration, which are already in use in their IT departments. Casper is also highly configurable and allows organizations to support public, private, and/or hybrid deployments. 

Casper is also noteworthy for the presence of Casper Labs, a software development and professional services firm that supports organizations building on the Casper network. Unlike most blockchains that follow a more traditional open-source project, Casper Labs provides around-the-clock support and bespoke software development for enterprise organizations. Recently, Casper Labs helped patent management company IPwe execute the largest-ever blockchain deployment, featuring more than 25 million patents being added as custom NFTs to the Casper Blockchain. 

How to get started with Casper on Google Cloud

Developers who want to start building on Casper can find a comprehensive series of tutorials here.

The Casper Association also recently announced a $25 million grant program to support projects and developers building on Casper. Interested participants can apply here.

Enterprises today have a very broad mix of networks — from SD-WANs, dedicated WANs such as MPLS, cloud interconnects, to VPNs. At the same time, they’re moving those WANs to the cloud to take advantage of faster turn-up, lower cost, and increased feature velocity. As workloads migrate to the cloud and multi-cloud environments, we believe that it’s critical to simplify enterprises’ networking model.

Each major cloud provider uses distinct abstraction models to configure networks or connections between your resources. Some use gateways, some use connections or links. Network Connectivity Center, launched last year, provides a simple management solution for your network connection, and is now Generally Available.

In this post, we outline the typical connectivity use cases for customers to help you select and set up the best connectivity option for your environment.

Understanding cloud network connectivity

Cloud networking refers to the ability to connect two resources together inside a cloud, across clouds and with on-premises data centers. A cloud provider needs to provide three main types of connectivity:

• Site-to-cloud – Between on-premises equipment and cloud resources
• Site-to-site – To connect on-premises resources together
• VPC-to-VPC – Connectivity between cloud resources
• Let’s take a look at each one.

Site-to-cloud connectivity

Site-to-cloud connectivity traditionally is done via a cloud interconnect or a cloud VPN. The automatic exchange of routes between on-premises and multiple VPCs can be done using a transit VPC.

A newer approach is to add cloud providers into an SD-WAN mesh using a router virtual appliance in Google Cloud. Network Connectivity Center brings the capacity to synchronize the appliance routes dynamically via BGP to Cloud Router and hence their VPCs. It enables connectivity between on-premises data centers and branch offices and their cloud workloads via SD-WAN-enabled connectivity. This capability is available globally across all 29+ Google Cloud regions. Several of our partners also support this capability in their router appliances.

Site-to-site connectivity

Site-to-site connectivity enables network connectivity directly between two or more hybrid connection points (VPN, Interconnect or SD-WAN). Network Connectivity Center simplifies this model by automating the routing announcements in this environment, such that all sites connected to a single global Network Connectivity Center hub are able to communicate freely in any-any fashion. You can see an example of this for a specific market vertical use case in a recent blog, Voice trading in the cloud — digital transformation of private wires.

VPC-to-VPC connectivity

You can create a full or partial mesh of VPC connections using multiple technologies, with VPC peering being the most common. VPC peering provides highly performant, low latency, private connectivity for customer networks connected via hybrid connectivity and Network Connectivity Center to multiple VPCs containing workloads, which can be segmented via granular firewall policies as needed. Alternatively, you can use a transit VPC model to connect multiple VPCs together in a hub and spoke topology.

With tight integration with third-party router appliances as mentioned earlier, you can also leverage their third-party supported solutions such as next-generation firewalls to connect your VPCs together to meet specific compliance and segmentation requirements. Network Connectivity Center allows you to synchronize the routing tables of these appliances with your VPC’s routing table, simplifying the process of setting up redundant configurations.

What’s next for cloud networking connectivity in Google Cloud?

As enterprises continue to migrate different types of workloads to public cloud providers, networking topologies are becoming more complex. In summary, we have solutions for all connectivity needs. We aim to keep our models and solutions understandable and simple. Over time, look for Network Connectivity Center to become Google Cloud’s single point of configuration for all your connectivity needs, with capabilities to handle the most complex network.