As a Google Cloud customer, your carbon footprint is already carbon neutral: Google first achieved carbon neutrality in 2007, and has been purchasing enough solar and wind energy to match 100% of its global electricity consumption since 2017. Now, Google is targeting a new sustainability goal: operating on carbon-free energy (CFE) 24/7, everywhere, by 2030. 

We want to empower you to make more sustainable decisions and progress with us towards this 24/7 carbon-free future. Earlier this year, we published the carbon characteristics of our Google Cloud regions. Later, we introduced a simple tool to help you pick a Google Cloud region, taking variables like price, latency and sustainability into account. Our next question was: what’s the best way to surface that sustainability info when you’re actually picking a region for your cloud resources? 

Starting today, we are indicating regions with the lowest carbon impact inside Cloud Console location selectors. Available today for Cloud Run and Datastream, you’ll see it roll out to more Google Cloud offerings over time:

Regions that feature the “Lowest CO2” and the leaf have a CFE% of at least 75%, or, if the information is not available for this region yet, a grid carbon intensity of maximum 200 gCO2eq/kWh. You can read more about how we calculate these metrics in our documentation.

Before releasing this feature, we ran experiments to measure its impact: Users who were exposed to the enhanced region picker were 19% more likely to select a “low carbon” region for their Cloud Run service—a significant lift. These results show that by displaying carbon information in context of when you make the decision of picking a region, we are helping you make more sustainable decisions.

By sharing and displaying carbon information of Google Cloud regions, together we’re making tangible progress towards our goal of a carbon-free future. Learn more about Carbon free energy for Google Cloud regions.

Many enterprises are looking to better manage extensions on their corporate devices. Extensions themselves are a great tool for productivity and customization of Chrome. However, some extensions can have the potential for far reaching rights to sites your users visit and devices they browse from, giving IT the desire to closely manage which extensions are in their environment and how they behave. 

In an earlier post in this series, we’ve detailed how Chrome Browser Cloud Management is the easiest way to audit installed extensions and manage them. However, some enterprises may need to still use Group Policy on Windows or plists on Mac to manage extensions. So lets touch on management through those methods, for organizations that haven’t quite made the move to Chrome Browser Cloud Management yet. 

For starters, here are some of the most used options for managing extensions (some also apply to apps) via Windows Group Policy or via Plists on Macs:

Installing or allowing extensions

• Extension Install Allow List: These are the extensions that you have approved to be installed within your environment. 
• Extension Install Force List: This will install the extension in the managed instance of Chrome. This setting overrides the extension block list policy, and the extension can’t be disabled on uninstalled. 
• Extension Allowed Types: Here you can create a list of what types of extensions and apps you will allow to be installed. Extensions, themes, user scripts, hosted applications, legacy packaged applications and platform applications are the values that are supported. Note that whatever you want to allow must be included in the list. Anything left off the list will not be installed. For more information on the different types, here is a link on Extensions and Apps in the Chrome web store. 
• Extension Install Sources: This policy allows you to get that older install functionality for specific URLs that you specify in this policy. Here is a link on the URL match patterns that can be used in this policy. 

Blocking extensions

• Extension Install Block List: These are the extensions that you will not allow to be installed. If they are installed already, they will be disabled. If a user tries to install them, it will be blocked. In the Chrome web store, the Add to Chrome button will be red and advise the user that the extension can’t be installed. 
• Block External Extensions: This setting will block extensions from external sources being installed. An example of this is if an installed application is adding an extension to Chrome via the registry, this setting will block that extension from loading. 

Advanced management options

• Extensions Settings: This policy provides a varied amount of functionality and requires a JSON script to be created and formatted in a single line string. This setting can be complex. We recommend using Chrome Browser Cloud Management as almost all of the functionality is included without needing to write JSON as well as the ability to audit installed extensions. If you do want to use this policy, it is covered in detail in the Managing Extensions in your Enterprise technical document. Some of the functionality that you can use within this policy are:
  • Install types: (Allowed, Blocked, Force Installed, Normal Installed) 
    • You can also display a custom message when the extension is blocked via the blocked installed message function.
  • Prevent extensions from altering websites: You can prevent all or specific extensions from running on specific websites. 
  • Managing by permissions: You can allow or block extensions by the specific rights or “permissions” that they require to run. 
    • This provides a baseline of functionality that you will or will not allow extensions to run on your users machines. 
    • If an extension is updated, bought, sold and updates the permissions that it requires, this will dynamically protect your users from permissions that you will not allow. 

Even if you decide that on-premises policy management is how you want to manage extensions long term, you can still use Chrome Browser Cloud Management to get much needed visibility into extensions that may exist in your environment. The newly improved apps and extension list is a great way to get a view of your current extension landscape, giving you more data to make better decisions around extension management.

You can enroll browsers to see this information, but still set policy through your existing tools if that’s your preference. 

Well, that’s it for this edition of Chrome Insider. Here’s where you can find more posts in the series, or you can learn more about Chrome Browser Cloud Management.

^{A note on Google’s commitment to inclusive naming conventions. The following policies have been deprecated, however will still continue to work until Chrome 95 to give administrators time to migrate to the new policies.– ExtensionInstallWhitelist replaced with ExtensionInstallAllowlist
– ExtensionInstallBlacklist replaced with ExtensionInstallBlocklist}

Whether with the cloud or within their own data centers, enterprises have undergone a period of remarkable consolidation and centralization of their compute resources. But with the rise of ever more powerful mobile devices, and increasingly capable cellular networks, application architects are starting to think beyond the confines of the data center, and looking out to the edge. 

What exactly do we mean by edge? Think of the edge as distributed compute happening on a wide variety of non-traditional devices — mobile phones of course, but also equipment sensors in factories, industrial equipment, or even temperature and reaction monitoring in a remote lab. Edge devices are also connected devices, and can communicate back to the mothership over wireless or cellular networks. 

Equipped with increasingly powerful processors, these edge devices are being called upon to perform tasks that have thus far been outside the scope of traditional IT. For enterprises, this could mean pre-processing incoming telemetry in a vehicle, collecting video in kiosks at a mall, gathering quality control data with cameras in a warehouse, or delivering interactive media to retail stores. Enterprises are also relying on edge to ingest data from outposts or devices that have even more intermittent connectivity, e.g., oil rigs or farm equipment, filtering that data to improve quality, reducing it to right-size information load, and processing it in the cloud. New data and models are then pushed back to the edge; in addition, we can also push configuration, software, and media updates and decentralize processing workload.

Edge isn’t all about enabling new use cases – it’s also about right-sizing environments and improving resource utilization. For example, adopting an edge model can also relieve load on existing data centers. 

But while edge computing is full of promise for enterprises, there are many pieces that are still works in progress. Further, developing edge workloads is very different from developing traditional applications, which enjoy the benefits of persistent data connections and run on well-resourced hardware platforms. As such, cloud architects are still in the early days of figuring out how to use and implement edge for their organizations. 

Fortunately, there are tools you can use to help ease the transition to edge computing — and that likely fit into your organization’s existing computing systems. Kubernetes, of course, but also higher level management tools like Anthos, which provides a consistent control plane across cloud, private data center and edge locations. Other parts of the Anthos family – Anthos Config Management and Anthos Service Mesh — go one step further and provide consistent, centralized management to your edge and cloud deployments. And there’s more to come.

For the remainder of this blog post, we’ll dive deeper into the past and current state of edge computing, and the benefits that architects and developers can expect to see from edge computing. In a next post, we’ll take a deeper look at some of the challenges that designing for edge introduces, and some of the advantages the average enterprise has in adopting the edge model. Finally, we’ll look at the Google Cloud tools that are available today to help you build out your edge environment, and look at some early customer examples that highlight what’s possible today — and that will spark your imagination for what to do tomorrow. 

The evolution of edge computing 

The edge is not a new concept. In fact, it’s been around for the last two decades, spanning many use cases that are prevalent today. One of the first applications for edge was to use content delivery networks (CDN) to cache and serve daily static website pages near clients, for example, web servers in California data centers serving financial data to European customers. 

As connectivity has improved and software evolved, the edge has evolved too, and the focus has shifted towards using edge to distribute services. First, simple services expanded from static HTML to javascript libraries or image repositories. Common functions like image transformation, credit and address validation support services followed. Soon, organizations were deploying more complex cloudlet and clustered microservices installations, as well as distributed and replicated datasets. The term “endpoint” became ubiquitous, and APIs profilerated. 

In parallel, there’s been an explosion of creativity in hardware, microcontrollers and dedicated edge devices. Fit-for-purpose products were deployed globally. Services like Google Cloud IoT Core extended our ability to manage and securely connect these dispersed devices, allowing platform managers to register tools and leverage managed services like Pub/Sub and Dataflow for data ingestion. And with Kubernetes, large remote clusters — mini private clouds in and of themselves — operate as self-healing, autoscaling services across the broader internet, opening the door to new models for applications and architectural patterns. In short, both distributed asynchronous systems and economies have blossomed.

What does this mean for enterprises? For the purposes of this series, edge means you can now go beyond the corporate network, beyond cloud VPCs, and beyond hybrid. The modern edge is not sitting at a major remote data center, nor is it a CDN, cloud provider, or in a corporate data center rack — it’s just as likely to look like 100 of these attached to a thousand sensors.

Edge, in short, is about having hardware and devices installed at remote locations that can process and communicate back the information they collect and generate. The edge management challenge, meanwhile, is being able to push configuration and software/model/media updates to these remote locations when they are connected.

Enable new use cases

Today, we have reached a new threshold for edge computing — one where micro-data-processing centers are deployed as the edge of a fractal arm, as it were. Together, they form a broad, geographically distributed, always-on framework for streaming, collecting, processing and serving asynchronous data. This big, loosely coupled application system lives, breathes and grows. Always changing, always learning from the data it collects — and always pushing out updated models when the tendrils are connected. 

Right now, the rise of 5G is pushing the limits of edge even further. Devices enabled with 5G can transmit using a mobile network — no ISP required — enabling connectivity anywhere within reach of a cell tower. Granted, these networks have lower bandwidth, but they are often more than adequate for certain types of data, for example fire sensors in forests bordering remote towns that emit temperature or carbon monoxide data periodically. Recently, Google Cloud partnered with AT&T to enhance business use of 5G edge technology but there is so much more that can be done. 

Reduce data center investments

In addition to enabling the digitization of a broad range of new use cases, adopting edge can also benefit your existing data center.

Let’s face it: data centers are expensive to maintain. Moving some data center load to edge locations can reduce your data center infrastructure investment, as well as compute time spent there. Edge services tend to have much lower service level objectives (SLOs) than data center services, driving lower levels of hardware investment. Edge installations also tend to tolerate disconnectedness, and thus function perfectly well with lower SLOs — and lower costs. 

Let’s look at an example of where edge can really reduce costs: big data. Back in the day, we used to build monolithic serial processors — state machines — that had to keep track of where they were in processing in case of failure. But time and again, we’ve seen that smaller, more distributed processing can break down big, expensive problems into smaller, more cost-effective chunks. 

Starting with the explosion of MapReduce almost 20 years ago, big-data workloads were parallelized across clusters on a network, and state management was simplified with intermediate output to share, wait for, or restart processing from checkpoints. Those monolithic systems were replaced by cheaper, smarter, networked clusters and data repositories where parallel work could be executed and rendered into workable datasets. 

Flash forward to today, and we are seeing those same concepts applied and distributed to edge data-collection points. In this evolution of big data processing, we are scaling up and out to the point where observation data is so massive that it must first be prefiltered, and then preprocessed down to a manageable size and still be actionable. Only then should it be written back to the main data repositories for more resource-intensive processing and model building.

In short, data collection, cleanup, and potentially initial aggregation happens at the edge location, which reduces the amount of junk data sitting in costly data stores. This increases performance of the core data warehouse, and reduces the size and cost of network transfers and storage! 

The edge is a huge opportunity for today’s enterprises. But designing environments that can make effective use of the edge isn’t without its challenges. Stick around for part two of this series, where we look at some of the architectural challenges typically encountered while designing for the edge and how we begin to address them.

Over the last two years, we’ve seen a significant uptick in the number of people using messaging to connect with businesses. Whether it was checking hours of operation, verifying what was in stock, or scheduling a pick-up, the pandemic caused a significant shift in consumer behavior.

• 45% of users are spending more time on messaging services because of the pandemic 
• 85% of consumers would like to message brands directly

With the rise in demand for messaging, consumers expect communication with businesses to be  speedy, simple, and convenient. For businesses, keeping up with customer inquiries can be a labor-intensive process, and offering 24/7 support outside of store hours can be costly.  

To help businesses seamlessly deliver helpful, timely, and engaging conversations with customers when and where they need help, we introduced AI-powered Business Messages.

https://youtube.com/watch?v=fcgP3RHjBLY%3Fenablejsapi%3D1%26

What is AI-powered Business Messages?

With AI-powered Business Messages, you can connect with your customers in their moment of need, in the places they’re looking for answers—such as Google Search, Google Maps, or any brand-owned channel. For instance, check out how Walmart customers in the US are able to receive real-time information on product availability, straight from a search results page.

People turn to Google when they are searching for answers to their questions, looking to buy something, or trying to accomplish a particular task with one of our many tools. In fact, 68% of all online experiences begin with a search engine.

At Google, we know how important it is for interactions with a brand to be personalized, helpful, and simple. With AI-powered Business Messages, customers are able to chat with virtual agents that understand, interact, and respond in natural ways.

We are also combining smart automation with the ability for customers to chat with live agents when it’s really needed. This approach saves your customers precious time, while also saving you money. And with Business Messages automatically handling many customer inquiries in the background, businesses have the option to distribute their human customer service agents to address other needs. 

Getting started with conversational AI is easy with Bot-in-a-Box

We know it can be difficult to get started with AI. That’s why we are utilizing existing Google AI tools like Dialogflow—part of Google Cloud Contact Center AI—to create the capability within Google’s Business Messages called Bot-in-a-Box, which makes getting started with Conversational AI easy. Bot-in-a-Box allows for fast and effective adoption of automation for businesses of all sizes. 

Enabling Business Messages with Bot-in-a-Box can be as simple as leveraging an existing customer FAQ document you already have, whether it’s from a web page or an internal document. And since the conversational AI is powered by Business Messages and Dialogflow working together, your chat bot is able to understand and respond to customer questions automatically without the need to write code. 

Bot-in-a-Box also supports other critical journeys like “Custom Intents.” That means that your bot is able to understand the different ways customers express a similar question and respond accurately by using machine learning capabilities.

Finding success 

In April 2021, Wake County courthouses in North Carolina partnered with Tango Technology to implement Business Messages when it became apparent that being able to provide the public and attorneys with around-the-clock access to information would significantly reduce the pressure on courthouse staff. Using Bot-in-a-Box, Tango Technology was able to customize a solution for Wake County Courthouse, Justice Center, and Clerk of Superior Court in just four days.

“With the combination of Google’s Business Messages, GCP, and Dialogflow, we were able to spin up an AI-driven bot for the courts in days. And the technology stack allows us to continually improve by adding functionality in an agile process.”— Mike Lotz, Co-Founder, Tango Technology

With Business Messages, North Carolina courthouses saw a 37% decrease in the call volume handled by courthouse staff.  With 398,298 fewer phone calls during the first year of operation, the AI-based messages helped Wake County Courthouse work more efficiently and productively.

We’ve seen many brands benefiting from AI-powered Business Messages. For instance, Levi’s saw a 30% increase in off-hours shoppers and surpassed 85% customer satisfaction scores after implementing Business Messages. They also drove 30x more store-related questions than Levi’s website chat. 

Bring Google’s conversational AI to your storefront with Business Messages

Google’s Business Messages makes it easier for businesses of all sizes to engage their existing or potential customers in a virtual conversation, when and where they need it. 

To learn more, watch our Cloud Next session here or visit us at g.co/businessmessages. We have specialized services to help you get started  and can share the wisdom of our channel partners and dedicated experts who specialize in unleashing the potential of conversational AI.