As you’ve probably heard, 5G is changing everything, and it’s unlocking new opportunities for innovators in one sector after another. By pairing the power of 5G networks with intelligent software, customers are beginning to embrace the next generation of industry, such as powering the IoT boom, enhancing smart factory operations, and more.
But how can companies that are leveraging data for daily operations start using data for innovation? In this article series, we’ll explore how the speed, throughput, reliability, and responsiveness of the Verizon network, paired with the sophistication of the next generation MongoDB developer data platform, are poised to transform industries including manufacturing, agriculture, and automotive.
Mobile edge computing: The basics
Companies everywhere are facing a new cloud computing paradigm that combines the best experiences of hyperscaler compute and storage with the topological proximity of 5G networks.
Mobile edge computing, or MEC, introduces a new mode of cloud deployments whereby enterprises can run applications — through virtual machines, containers, or Kubernetes clusters — within the 5G network itself, across both public and private networks. Before we dive in, let’s define a few key terms:
What is mobile edge computing? The ability to deploy compute and storage closer to the end user
What is public mobile edge computing? Compute and storage deployed with the carrier data centers
What is private mobile edge computing? On-premise provisioned compute and storage
Verizon 5G Edge, Verizon’s mobile edge compute portfolio, takes these concepts from theoretical to practical. By creating a unified compute mesh across both public and private networks, Verizon 5G Edge produces a seamless exchange of data and stateful workloads — a simultaneous deployment of both public and private MEC best characterized as a hybrid MEC. In this article, we’ll primarily focus on public MEC deployment.
Although MEC vastly increases the flexibility of data usage by both practitioners and end users, the technology is not without its challenges, including:
Deployment: Given a dynamic fleet of devices, in an environment with 20-plus edge zones across both public and private MEC, to which edge(s) should the application be deployed?
Orchestration: For Day 2 operations and beyond, what set of environmental changes, — be it on the cloud, network, or on device(s) — should trigger a change to my edge environment?
Edge discovery: Throughout the application lifecycle, for a given connected device, which edge(s) is the optimal endpoint for connection?
Fortunately for developers, Verizon has developed a suite of network APIs tailored to answer these questions. From edge discovery and network performance to workload orchestration and network management, Verizon has drastically simplified the level of effort required to build resilient, highly available applications at the network edge without the undifferentiated heavy lifting previously required.
Edge discovery API workflow
Using the Verizon edge discovery API, customers can let Verizon manage the complexity of maintaining the service registry as well as identifying the optimal endpoint for a given mobile device. In other words, with the edge discovery API workflow in place of the self-implemented latency tests, a single request-response would be needed to identify the optimal endpoint, as shown in Figure 1.
Although this API addresses challenges of service discovery, routing, and some advanced deployment scenarios, other challenges exist outside of the scope of the underlying network APIs. In the case of stateful workloads, for example, how might you manage the underlying data generated from your device fleet? Should all of the data live at the edge, or should it be replicated to the cloud? What about replication to the other edge endpoints?
Using the suite of MongoDB services coupled with Verizon 5G Edge and its network APIs, we will describe popular reference architectures for data across the hybrid edge.
Delivering data with MongoDB
Through Verizon 5G Edge, developers can now deploy parts of their application that require low latency at the edge of 4G and 5G networks using the same APIs, tools, and functionality they use today, while seamlessly connecting back to the rest of their application and the full range of cloud services running in a cloud region.
However, for many of these use cases, a persistent storage layer is required that extends beyond the native storage and database capabilities of the hyperscalers at the edge.
Given the number of different edge locations where an application can be deployed and consumers can connect, ensuring that appropriate data is available at the edge is critical. It is also important to note that where consumers are mobile (e.g., vehicles), the optimal edge location can vary.
At the same time, having a complete copy of the entire dataset at every edge location to cater for this scenario is neither desirable nor practical due to the potentially large volumes of data being managed and the associated multi-edge data synchronization challenges that would be introduced.
The Atlas solution
The solution requires having an instantaneous and comprehensive overview of the dataset stored in the cloud while synchronizing only required data to dedicated edge data stores on demand. For many cases, such as digital twin, this synchronization needs to be bi-directional and may potentially include conflict resolution logic. For others, a simpler unidirectional data sync would suffice.
These requirements mean you need a next-gen data platform, equipped with all the power to simplify data management while also delivering data in an instant. MongoDB Atlas is the ideal solution for the central, cloud-based datastore.
Atlas provides organizations with a fully managed, elastically scalable developer data platform upon which to build modern applications. MongoDB Atlas can be simultaneously deployed across any of the three major cloud providers (Amazon Web Services, Microsoft Azure, and Google Cloud Platform) and is a natural choice to act as the central data hub in an edge or multi-edge based architecture, because it enables diverse data to be ingested, persisted, and served in ways that support a growing variety of use cases.
Central to MongoDB Atlas is the MongoDB database, which combines a flexible document-based model with advanced querying and indexing capabilities. Atlas is, however, more than just the MongoDB database and includes many other components to power advanced applications with diverse data requirements, like native search capabilities, real-time analytics, BI integration, and more.
Read the next post in this blog series to explore the real-world applications and innovations being powered by mobile edge computing.