Mobile Edge Computing, Part 1: Delivering Data Faster with Verizon 5G Edge and MongoDB

Jessica Baumgardner and Stephen Dalby
July 21, 2022 | Updated: December 12, 2022

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.

Diagram of how a single request-response is used to identify the optimal edge endpoint.
Figure 1.   A single request-response is used to identify the optimal endpoint

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.

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Mobile Edge Computing, Part 2: Computing in the Real World

It would be easy to conceptualize mobile edge computing (MEC) as a telecommunications-specific technology ; but, in fact, edge computing has far-reaching implications for real-world use cases across many different industries. Any organization that requires a solution to common data usage challenges, such as low-latency data processing, cloud-to-network traffic management, Internet of Things (IoT) application development, data sovereignty, and more, can benefit from edge-based architectures. In our previous article , we discussed what mobile edge computing is, how it helps developers increase data usage flexibility, and how Verizon 5G Edge and MongoDB work in conjunction to enable data computing at the edge, as shown in Figure 1. Figure 1.  Verizon and MongoDB work in conjunction to deliver data to consumers and producers faster than ever with mobile edge computing. In this article, we’ll look at real-world examples of how mobile edge computing is transforming the manufacturing, agriculture, and automotive industries. Smart manufacturing Modern industrial manufacturing processes are making greater use of connected devices to optimize production while controlling costs. Connected IoT devices exist throughout the process, from sensors on manufacturing equipment to mobile devices used by employees on the factory floor to connected vehicles transporting goods — all generating large amounts of data. For companies to realize the benefits of all this data, it is critical that the data be processed and analyzed in real time to enable rapid action. Moving this data from the devices to the cloud for processing introduces unnecessary latency and data transmission that can be avoided by processing at the edge. As seen in Figure 2, for example, sensors, devices, and other data sources in the smart factory use the Verizon 5G Edge Discovery Service to determine the optimal edge location. After that, data is sent to the edge where it is processed before being persisted and synchronized with MongoDB Atlas — all in an instant. Figure 2.   Data sources in smart factories use the Verizon 5G Edge Discovery Service to determine the optimal edge location. Process optimization Through real-time processing of telemetry data, it’s possible to make automated, near-instantaneous changes to the configuration of industrial machinery in response to data relayed from a production line. Potential benefits of such a process include improved product quality, increased yield, optimization of raw material use, and ability to track standard key performance indicators (KPIs), such as overall equipment efficiency (OEE). Preventative maintenance Similar to process optimization, real-time processing of telemetry data can enable the identification of potential impending machinery malfunctions before they occur and result in production downtime. More critically, however, if a situation has the potential either to damage equipment or pose a danger to those working in the vicinity, the ability to automatically perform shut downs as soon as the condition is detected is vital. Agriculture One of the most powerful uses of data analytics at scale can be seen in the agriculture sector . For decades, researchers have grappled with challenges such as optimal plant breeding and seed design, which to date have been largely manual processes. Through purpose-built drones and ground robotics, new ways to conduct in-field inspection using computer vision have been used to collect information on height, biomass, and early vigor, and to detect anomalies. However, these robots are often purpose-built with large data systems on-device, requiring manual labor to upload the data to the cloud for post-processing. Using the edge, this entire workflow can be optimized. Starting with the ground robotics fleet, the device can be retrofitted with a 5G modem to disintermediate much of the persistent data collection. Instead, the device can collect data locally, extract relevant metadata, and immediately push data to the edge for real-time analytics and anomaly detection. In this way, field operators can collect insights about the entirety of their operations — across a given crop field or nationwide — without waiting for the completion of a given task. Automotive Modern vehicles are more connected than ever before, with almost all models produced today containing embedded SIM cards that enable even more connected experiences. Additionally, parallel advances are being made to enable roadside infrastructure connectivity. Together, these advances will power not just increased data sharing between vehicles but also between vehicles and the surrounding environment (V2V2X). In the shorter term, edge-based data processing has the potential to yield many benefits both to road users and to vehicle manufacturers . Data quality and bandwidth optimization Modern vehicles have the ability to transmit large amounts of data not only in terms of telemetry relating to the status of the vehicle but also in regard to the observed status of the roads. If a vehicle detects that it is in a traffic jam, for example, then it might relay this information so that updates can be made available to other vehicles in the area to alert drivers or replan programmed routes, as shown in Figure 3. Figure 3.  Mobile edge computing enables data generated from multiple sources within a vehicle to be shared instantly. Although this is a useful feature, many vehicles may be reporting the same information. By default, all of this information will be relayed to the cloud for processing, which can result in large amounts of redundant data. Instead, through edge-based processing: Data is shared more quickly between vehicles in a given area using only local resources. Costs relating to cloud-based data transfer are better controlled. Network bandwidth usage is optimized. While improving control of network usage is clearly beneficial, arguably a more compelling use of edge-based processing in the automotive industry relates to aggregating data received from many vehicles to improve the quality of data sent to the cloud-based data store. In the example of a traffic jam, all of the vehicles transmitting information about the road conditions will do so based on their understanding gained through GPS as well as internal sensors. Some vehicles will send more complete or accurate data than others, but, by aggregating the many different data feeds at the edge, this process results in a more accurate, complete representation of the situation. Read Part 1 of this blog series . Download our latest book on computing at the edge .

July 21, 2022
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Collaborating to Build AI Apps: MongoDB and Partners at Google Cloud Next '24

From April 9 to April 11, Las Vegas became the center of the tech world, as Google Cloud Next '24 took over the Mandalay Bay Convention Center—and the convention’s spotlight shined brightest on gen AI. Between MongoDB’s big announcements with Google Cloud (which included an expanded collaboration to enhance building, scaling, and deploying GenAI applications using MongoDB Atlas Vector Search and Vertex AI ), industry sessions, and customer meetings, we offered in-booth lightning talks with leaders from four MongoDB partners—LangChain, LlamaIndex, Patronus AI, and Unstructured—who shared valuable insights and best practices with developers who want to embed AI into their existing applications or build new-generation apps powered by AI. Developing next-generation AI applications involves several challenges, including handling complex data sources, incorporating structured and unstructured data, and mitigating scalability and performance issues in processing and analyzing them. The lightning talks at Google Cloud Next ‘24 addressed some of these critical topics and presented practical solutions. One of the most popular sessions was from Harrison Chase , co-founder and CEO at LangChain , an open-source framework for building applications based on large language models (LLMs). Harrison provided tips on fixing your retrieval-augmented generation (RAG) pipeline when it fails, addressing the most common pitfalls of fact retrieval, non-semantic components, conflicting information, and other failure modes. Harrison recommended developers use LangChain templates for MongoDB Atlas to deploy RAG applications quickly. Meanwhile, LlamaIndex —an orchestration framework that integrates private and public data for building applications using LLMs—was represented by Simon Suo , co-founder and CTO, who discussed the complexities of advanced document RAG and the importance of using good data to perform better retrieval and parsing. He also highlighted MongoDB’s partnership with LlamaIndex, allowing for ingesting data into the MongoDB Atlas Vector database and retrieving the index from MongoDB Atlas via LlamaParse and LlamaCloud . Guillaume Nozière - Patronus AI Andrew Zane - Unstructured Amidst so many booths, activities, and competing programming, a range of developers from across industries showed up to these insightful sessions, where they could engage with experts, ask questions, and network in a casual setting. They also learned how our AI partners and MongoDB work together to offer complementary solutions to create a seamless gen AI development experience. We are grateful for LangChain, LlamaIndex, Patronus AI, and Unstructured's ongoing partnership. We look forward to expanding our collaboration to help our joint customers build the next generation of AI applications. To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub and stop by our Partner Ecosystem Catalog to read about our integrations with these and other AI partners.

April 23, 2024