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.
