4 Ways Telcos Deliver Mission-Critical Network Performance and Reliability

Tech leaders like Google, Apple, and Netflix set a new standard for customer service. Today’s customers expect intuitive, always-on, seamless service that challenges telecommunications companies’ network performance and reliability. This article examines several ways that companies can meet these challenges through an automated, data-driven approach.

How a modern data platform can help

A fully integrated, customer-centric, and data-driven approach to service delivery and assurance is needed to remain competitive. Modern telecommunications enterprises are tackling this problem by investing in areas like AI and machine learning, for example, which can help them identify correlations between disparate, diverse sources of data and automate end-to-end network operations, including:

  • Network security

  • Fraud mitigation

  • Network optimization

  • Customer experience

Furthermore, by adopting a modern data platform, companies can easily answer questions, such as the following, that are nearly impossible to resolve when relying on legacy technology:

  • Is an event likely to have a customer impact?

  • Are customer-facing service SLAs being met?

  • Where should cell sites be placed for maximum ROI?

  • Is new equipment deployed and configured correctly?

MongoDB’s developer data platform can help companies provide the necessary performance and reliability to meet customers’ expectations in four key areas: reducing data complexity, service assurance automation, network intelligence and automation, and TM Forum Open APIs.

Reducing data complexity

One recent study found that data scientists spend about 45% of their time loading and cleansing data. For a true impact to your organization, you need to free up that time to enable data scientists to focus on mission-critical projects and innovation. Additionally, architectural complexity, with bolted-on solutions and legacy technology, prevents you from harnessing your data and having a true impact on network performance and reliability.

MongoDB’s developer data platform solves the great complexity problem by supporting a diverse range of workloads from a single data platform. Reducing the channels for data flow allows companies to establish a single source of truth, achieve a customer-centric approach that is critical for competitive advantage, and increase service assurance.

Diagram of MongoDB's Developer Data Platform. The platform includes (from top to bottom) the Document Model with features such as Key Value Pairs, Graphs, Relationships, Geospatial, Time Series, and Objects. This is followed by Unified interface with features such as Serverless Applications, Mobile Applications, Distributed Applications, Analytical, Search, and Transactional. At the base, our platform is built upon being secure, resilient & elastic, global, and multi-cloud.
Figure 1. MongoDB’s developer data platform reduces complexity in telecommunications workloads, resulting in more reliable network service for customers.

With continuous uptime and advanced automation, MongoDB’s developer data platform ensures performance, no matter the scale.

Service assurance automation

In telecommunications, always-on, always-available service both for the end user and internal IT teams is critical. While outdated service assurance processes may have been viable decades ago, the volume of data and number of users have grown exponentially, making manually intensive processes of the past no longer possible. This volume increase will continue to stress existing business support systems, and without modernization, it will hamper the development of new revenue streams.

Moving from a reactive to proactive and then predictive model, as shown in Figure 2, will enhance service assurance and enable organizations to meet the expectations of the digital-native customer.

Graphic of the transition from Reactive to Proactive to predictive data model. Reactive is defined as responding to alerts generated by network components or service complaints raised by end customers. A Reactive data model is associated with Legacy systems, which are defined as being highly structured, normalized data. Analytical functions performed via batch-base systems on EDW data. The KPI for reactive is time to resolve fault and the outcome is reestablish QoS. The Proactive data model is defined as predicting spikes, congestion and equipment failure which has the ability to impact end customers before it happens. A Proactive data model is associated with Interactive data science, which is defined as supervised data science tools and techniques used to derive predictive models based on historical data. The KPI for the Proactive model is reduced uptime challenges with the outcome being to prevent degradation in QoS. The Predictive data model is defined as Automatically take preventative action to prevent customer service interruption or to limit the impact of any interruption. The Predictive data model is associated with Industrial AI Ops, which is defined as Using advanced AI techniques over diverse, flexible data sets to autonomously act on real-time events. The KPI for the Predictive model is optimized service availability with the outcome being cost reduction and infrastructure optimization.
Figure 2. The transition from a reactive to proactive to predictive data model opens up new opportunities to use innovative technologies like artificial intelligence.

Network intelligence and automation

Consider the essential task of configuration and management of radio access networks. On a daily basis, engineers change the angles of antenna towers, the configuration of the radio, the nearest neighbor relations, and other events your system tracks and manages.

With an intuitive developer data platform, any change in the configuration is saved in the data mediation layer (DML) for anyone to see and track, making it easy for engineers to go to the DML to check the configuration for a particular tower. Information that was previously captured in one snapshot per day is now propagated in real time.

Another example — intent-based automation — abstracts the complexity of underlying software-defined networking components by allowing intent to be specified and by providing automatic translation. This type of automation allows teams to process intent generated either by end user activity or via service assurance processes, and that intent is translated into the underlying network state.

Network events determine whether the network is in the desired, stable state, and that unintended states are addressed via automation, potentially using TM Forum Network-as-a-Service APIs.

TM Forum Open APIs

The TM Forum (TMF) is an alliance of more than 850 companies that accelerates digital innovation through its TMF Open APIs, which provide a standard interface for the exchange of different telco data models. The use of TMF Open APIs ranges from providers of off-the-shelf software to proprietary developments of the largest telecommunications providers.

In working with many of the world’s largest communication service providers (CSPs) and their related software provider ecosystems, MongoDB has seen a significant number of organizations leverage these APIs to develop new microservices in days, rather than weeks or months. Through exposing common interfaces, CSPs are able to adopt a modular architecture made up of best-of-breed components (either internally or externally developed) while minimizing the time, effort, and cost required to integrate them.

The TMF Network-as-a-Service APIs, in particular, hold significant potential for network automation. This API component suite supports a set of operational domains exposing and managing network services. The abstraction layer between network automation tooling and the underlying network infrastructure provides a flexible, modular architecture.

Network optimization is vital to the survival of telcos in today’s competitive market. However, with a modern developer data platform underpinning your network, you’ll be equipped to meet and exceed customer expectations.

Read our ebook to learn more about implementing TM Forum Open APIs with MongoDB.

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7 Ways to Reduce Your Database's Carbon Footprint

In his technology predictions for 2022 , Werner Vogels, VP and CTO of Amazon, reminded the industry that sustainability is no longer a secondary consideration — nor something someone else is responsible for. “As developers, we are trained to think about how to optimize our architectures for factors like security, performance, reliability, and cost,” he wrote. “In 2022, you can add sustainability to that list. What we will begin to see in the coming years is developers taking an active role in building sustainability-conscious architectures that take into account not just the problems they are solving, but the planet as well.” Vogels’s vision makes it clear that there is a difference between how we measure the efficiency of databases and, perhaps, how we should. In this article, we’ll look at several steps organizations can take to reduce the carbon footprint of their databases. It all starts with the cloud. Move off premises Migrating workloads to a public cloud creates an instant return on sustainability efforts, with an average 84% immediate reduction in carbon emissions as a result. This reduction results from a combination of factors, including the fact that cloud data centers use less physical space and resources than those on-premises; the use of more efficient servers and higher server usage; and the fact that public cloud providers are themselves committed to sustainable infrastructure and a reduced carbon footprint, which enhances and supports their customers’ efforts as well. For example, the three major public cloud providers have made the following commitments: Amazon Web Services (AWS) is committed to being powered with 100% renewable energy by 2025 and has pledged to be carbon neutral by 2040 across all Amazon properties. Microsoft Azure has also pledged to be completely powered by renewable energy by 2025 and is committed to being water-positive and zero-waste by 2030. Google Cloud already boasts the use of 100% renewable energy in all regions, with a goal to use only carbon-free energy by 2030. Consider serverless Businesses can amplify that carbon reduction by selecting a database platform that aligns with their energy goals. MongoDB is currently in 80-plus regions, with 12 of those regions being powered by renewable energy as well as being low-carbon and/or zero-waste. Furthermore, MongoDB Atlas Serverless instances are optimized to use the underlying infrastructure — RAM, CPU, disks, network bandwidth — to its maximum before consuming additional cloud resources. Currently, 60% of organizations globally have yet to adopt any serverless technology; however, pushing to serverless not only helps to reduce carbon footprint but also can provide a competitive advantage. Consolidate your platform Databases require management on many fronts, from transactional data and relational data to search, analytics, graph use cases, time series, and more. Each of these facets is frequently handled in its own separate database, necessitating management, monitoring, and resources. Not only is this a technical pain point, but it can also affect the environmental goals of an organization and its carbon footprint overall. To start, fewer workloads result in a lower carbon emission total. Thus, simply paring down the number of databases used is an easy way to reduce the consumption of resources and associated carbon emissions. MongoDB Atlas brings all of those requirements into a single, consolidated database platform that solves for disparate and siloed databases and reduces resources and consumption. Strategize around prevention The greenest energy is the energy that is never used in the first place. Toward that end, organizations can work to shift their architectural strategy to be more mindful of carbon and energy considerations, and adjustments can be made to reduce usage without affecting performance. For example, about 40% of instances are at least one size larger than needed . Businesses can reduce such excess consumption by avoiding over-provisioned databases. This change can be done easily through automatic scaling with MongoDB Atlas cluster and storage auto-scaling, as well as with Atlas Triggers , which allows users to schedule scaling events. Another consideration is the consumption of non-production environments. Currently, more than 44% of total compute resources are being used in non-production environments like dev, testing, staging, and pre-production. This consumption typically does not occur outside of work hours, and it’s easy to pause these resources outside of peak hours in Atlas, either on-demand or with an Atlas Trigger. Continue to iterate These major adjustments to the structure of your database architecture represent impactful steps toward creating a sustainable database, but there are many, more granular ways to improve database technology and better align with environmental goals. Implementing smaller changes will chip away at carbon emissions and create significant impact over time. Such easy-to-execute suggestions include the following: Consider the impact of data transfer over longer distances and find new regions to house data. Determine the proper location of data and leverage the flexibility to deploy in any region, which grants more control over replication of data. Use Atlas Global Clusters to ensure that data stays within a defined geography, thereby minimizing the infrastructure required to deploy a globally distributed application. Employ Atlas Data Federation to minimize the need to replicate data between cloud object storage and Atlas by allowing users to query both sources from a single interface. Leverage Atlas Analytics Nodes to offload workloads to separate nodes. And note that, with MongoDB 6.0 , you can manually size those nodes to trim even more resources. Use analytic nodes that are deployed in more environmentally sustainable regions. Leverage Atlas’s many dashboards and alerts to be proactively notified when system resources are rising beyond acceptable thresholds. Introduce an “eco mode” in your apps, giving users the ability to agree to potentially lower response times in exchange for a positive environmental impact. Avoid peak times for predictable workloads; schedule these during a time that will not drive over-consumption. Use data tiering; users can automate data lifecycles using Atlas Online Archive and reduce exhaustion of resources. Don't wait The time to act is now. Studies estimate that between 2% and 4% of global emissions come from the information and communications technology industry — about the same as from aviation. Much of that impact is concentrated in data centers, which consume 10 times to 50 times the energy per square foot compared to a typical office building. With every day that businesses do not improve their carbon footprint, the environmental problem gets worse. For example, IDC found that the number of data centers worldwide has grown from 500,000 in 2012 to more than 8 million today. And data itself is exploding, with 2.5 quintillion new bytes of data created daily . Because of this, the amount of energy used by data centers will continue to double every four years, meaning data centers have the fastest-growing carbon footprint of any area within the technology industry sector. Organizations need a proper database framework to combat these negative environmental trends without creating additional and unnecessary carbon emissions that add to the global warming crisis. As a developer data platform, MongoDB provides businesses with the database architecture to reduce consumption of resources, and MongoDB Atlas offers additional features to enhance carbon reduction efforts. Get inspired Businesses are a strong lever for change in the world. When it comes to carbon reduction, MongoDB is committed to creating sustainable and lean data architectures, both internally and for customers. Check out these three stories of companies that have taken incredible strides to curtail their carbon impact. Business leaders can all be inspired by and learn from each other to make changes and collectively benefit the Earth and future generations. Learn about this and more from MongoDB Solutions Architect, Snehal Bhatia’s, presentation on Designing a Sustainable Architecture from MongoDB World 2022 . .

August 24, 2022
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The Journey of MongoDB with COVESA in the Connected Vehicle Landscape

There’s a popular saying: “If you want to go fast, go alone; if you want to go far, go together.” I would argue The Connected Vehicle Systems Alliance (COVESA) in partnership with their extensive member network, turns this saying on its head. They have found a way to go fast, together and also go far, together. COVESA is an industry alliance focused on enabling the widespread adoption of connected vehicle systems. This group aims to accelerate the development of these technologies through collaboration and standardization. It's made up of various stakeholders in the automotive and technology sectors, including car manufacturers, suppliers, and tech companies. COVESA’s collaborative approach allows members to accelerate progress. Shared solutions eliminate the need for individual members to reinvent the wheel. This frees up their resources to tackle new challenges, as the community collectively builds, tests, and refines foundational components. As vehicles become more connected, the data they generate explodes in volume, variety, and velocity. Cars are no longer just a mode of transportation, but a platform for advanced technology and data-driven services. This is where MongoDB steps in. MongoDB and COVESA As the database trusted for mission-critical systems by enterprises such as Cathay Pacific , Volvo Connect , or Cox Automotive ; MongoDB has gained expertise in automotive, along with many other industries, building cross-industry knowledge in handling large-scale, diverse data sets. This in turn enables us to contribute significantly to vehicle applications and provide a unique view, especially in the data architecture discussions within COVESA. MongoDB solutions support these kinds of innovations, enabling automotive companies to leverage data for advanced features. One of the main features we provide is Atlas Device SDKs : a low-footprint, embedded database directly living on ECUs. It can synchronize data automatically with the cloud using Atlas Device Sync , our data transfer protocol that compresses the data handles conflict resolution, and only syncs delta changes, making it extremely efficient in terms of operations and maintenance. VSS: The backbone of connected vehicle data An important area of COVESA’s work is the Vehicle Signal Specification (VSS). VSS is a standardized framework used to describe data of a vehicle, such as speed, location, and diagnostic information. This standardization is essential for interoperability between different systems and components within a vehicle, as well as for external communication with other vehicles and infrastructure. VSS has been gaining more and more adoption, and it’s backed by ongoing contributions from BMW, Volvo Cars, Jaguar LR, Robert Bosch and Geotab, among others. MongoDB’s BSON and our Object-oriented Device SDKs uniquely position us to contribute to VSS implementation. The VSS data structured maps 1 to 1 to documents in MongoDB and objects in Atlas Device SDKs , which simplifies development, and speeds up applications by completely skipping any Relational Mapper layer. For every read or write, there is no need to transform the data between relational and VSS. Our insights into data structuring, querying, and management can help optimize the way data is stored and accessed in connected vehicles, making it more efficient and robust. Where MongoDB contributes MongoDB, within COVESA, finds its most meaningful contributions in areas where data complexities and community collaboration intersect. First, we can share insights into managing vast and varied data emerging from connected vehicles generating data on everything from engine performance to driver behavior. Second, we have an important role in supporting the standardization efforts, crucial for ensuring different systems within vehicles can communicate seamlessly. Our inputs can help ensure these standards are robust and practical, considering the real-world scenarios of data usage in vehicles. Some of our contributions include an Over the Air update architectural review presented at Troy COVESA’s AMM in October 2023; sharing insights about the Data Middleware PoC with BMW; and weekly contributions at the Data Expert Group. You can find some of our contributions on COVESA’s Wiki page . In essence, MongoDB's role in COVESA is about providing a unique perspective from the database management point of view, offering our understanding from different industries and use cases to support the developments towards more connected and intelligent vehicles. MongoDB, COVESA, and AWS together at CES2024 MongoDB’s most recent collaboration with COVESA was at the Consumer Electronics Show CES 2024 during which MongoDB’s Connected Vehicle solution was showcased. This solution leverages Atlas Device SDKs, such as the SDK for C++ , which enables local data storage, in-vehicle data synchronization, and also uni and bi-directional data transfer with the cloud. Below is a schematic illustrating the integration of MongoDB within the software-defined vehicle: Schema 1: End to end integration for the connected vehicle At CES 2024, MongoDB also teamed up with AWS for a compelling presentation, " AI-powered Connected Vehicles with MongoDB and AWS " led by Dr. Humza Akhtar and Mohan Yellapantula, Head of Automotive Solutions & Go To Market at AWS. The session delved into the intricacies of building connected vehicle user experiences using MongoDB Atlas. It showcased the combined strengths of MongoDB's expertise and AWS's generative AI tools, emphasizing how Atlas Vector Search unlocks the full lifecycle value of connected vehicle data. During the event, MongoDB also engaged in a conversation with The Six Five, exploring various aspects of mobility, self-driving vehicles (SDVs), and the MongoDB and AWS partnership. This discussion extended to merging IT and OT, GenAI, Atlas Edger Server, and Atlas Device SDK. Going forward At the end of the road, it’s all about enhancing the end-user experience and providing unique value propositions. Defect diagnosis based on the acoustics of the engine, improved crash assistance with mobile and vehicle telemetry data, just-in-time food ordering while on the road, in-vehicle payments, and much, much more. What all these experiences have in common is the combination of interconnected data from different systems. At MongoDB, we are laser-focused on empowering OEMs to create, transform, and disrupt the automotive industry by unleashing the power of software and data. We enable this by: Partnering with alliances such as COVESA to build a strong ecosystem or collaboration. Having one single API for In-vehicle Data Storage, Edge to Cloud Synchronization, Time Series storage, and more, improves the developer experience. Focusing on having a robust, scalable, and secure suite of services trusted by tens of thousands of customers in more than 100 countries. Together with COVESA’s vision for connected vehicles, we’re driving a future where this industry is safer, more efficient, and seamlessly integrated into the digital world. The journey is just beginning. To learn more about MongoDB-connected mobility solutions, visit the MongoDB for Manufacturing & Motion webpage . Achieving fast, reliable and compressed data exchange is one of the pillars of Software Defined Vehicles, learn how MongoDB Atlas and Edge Server can help in this short demo .

April 15, 2024