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Mirthe Luria
As we head into 2020, it is clear that CSPs are racing to deploy 5G networks. But the transition is complex and costly. (Read my most recent blog on 5G for greater detail on this topic.) In addition to bearing the high cost of acquiring spectrum, CSPs need to invest heavily in core and radio access networks, transmission, and infrastructure. 5G typically requires 5 to 10 times more cell tower infrastructure, and some industry analysts predict that network-related capital expenditures will increase significantly, roughly doubling the cost of ownership in some markets. How each CSP rolls out their 5G plan will be crucial.
The 5G future of operations will be automated and why CSPs need to plan for it now
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To achieve the vision of autonomous networks, CSPs need to adopt an intent-driven automation approach. CSPs must first define the business intent; this could be an enterprise wanting to automate their WAN or the CSP wanting to fully automate its mobile broadband operations. The business intent is then translated into service intent (encapsulating the service design and service orchestration processes) and network intent (network resource and service orchestration), supported by assurance systems to monitor the state of the network and services. Together, the service design and orchestration, network orchestration and automated assurance systems, supported by AI/ML capabilities, enable an intent-driven, fully closed-loop autonomous network. (Illustrated in Figure 1)
CSPs will need advanced AI/analytics capabilities in all networks at level 3 and above. Such capabilities can aid self-learning from fast-changing environments, provide accurate predictions of potential network problems and anticipate trends and ensure the continuous improvement and adaptation of the rules that govern autonomous operations. Our interviews revealed that advanced CSPs typically start with piecemeal, tactical implementations of AI/ML with various tools, methodologies and datasets that are specific to a network element, domain, use case or business problem. However, as they expand the number and scope of these individual implementations and their sub-components over time, they plan to join them together to create end-to-end operations with centralized data lakes and common tools, models, processes, and governance. This suggests that autonomous networks should be developed with a layered AI/ML and analytics approach that is aligned with the hierarchical architecture discussed in the previous section. The three layers in this approach are described below (Figure 3).
While service automation is already a theme within many telecommunications companies, they need to go further. Automated processes must be made to work seamlessly together. Service orchestration is the component that will help telecom service providers scale up and face their fast-moving future in the cloud.
As telecom businesses approach their new and more heavily orchestrated futures, they will need all of their technology investments to step up. This means having the right solutions for essential roles including integrated workflow for holistic management of the wide variety of business processes necessary to ensure the most efficient delivery of service.
5G delivers an entirely new class of powerful technological capabilities and innovative business models. However, these advances come at the price of exponentially greater complexity and scale and require a new approach to automation to capitalize on the potential of 5G and offer new services. The CSPs that take the time to reinvent their operations environment now will reap the benefits of streamlined networks and differentiated services in the future.
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To quicken the pace of internal operations, here, CSPs need to adopt an intelligent automation solution that has the capabilities to orchestrate services, manage customer data, and automate end-to-end cloud business processes. This will help cloud service providers to create on-demand products and services, deliver them to the customers quickly and manage their billing transparently from a single portal.
Imagine a visit to the doctor. You wait an hour for an examination at one location. Additional tests are needed at a different department. So, they give you a piece of paper to take to a different location, but the instructions were unclear. So, you return to the starting point to try again. Healthcare systems are modernizing to eliminate these layered inconveniences and improve their operations; with routed optical networking, communication service providers (CSPs) are optimizing in a similar way. The modernization of the Internet will require the most advanced silicon, optics, systems, and software to automate its operations.
Competitive positioning: Companies in the dynamic telecom market find it difficult to predict what types of use cases will be deployed on future networks or the types of devices that will be connected. Hyperautomation can bridge this gap through AI-enabled learning and data exploration for forecasting mobility and different usage patterns, anomaly detection, and IT data analysis. As such, CSPs can develop future-proof operations and be in control of their competitive advantage in the ever-evolving market.
An existing OCS may not be able to support everything a CSP would ultimately want to be able to achieve from its future charging system, but it can still enable substantial flexibility to charge in new ways. Especially within the confines of 4G-era product offerings and pricing models, existing OCS is sufficient to support many usage-based services and this capability can be extended into the 5G connectivity era. Eventually, CSPs will need to upgrade their OCS but this major transformation is not essential to enable a participation in new digital and usage-based models.
To be prepared and ultimately successful in 5G, CSPs should consider the optimal ways to maximize the opportunities that usage-based data will bring. These include the ability to comprehensively capture and rapidly process all the data needed, to support an extensive range of current and future business models, the ability to go beyond traditional CSP data sources to maximize the information available to support usage-based revenue management, and utilization of cloud-native software to take advantage of developer-friendly, ready-to-use infrastructure.
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