Communication Service Providers and Network Operators struggle to keep up with the pace of change in their networks. Current operational models no longer work when the industry is moving to cloud-based architectures. AI-powered automation is required to ensure that the business can deliver the applications and services that the consumers expect. Continuously!

Benefits Of B-Yond AGILITY

Reduce time-to-market for new
services and features

Improve network resiliency

Eliminate IQ drain due to staff
attrition

Reduce Mean-Time-To-Restore
for user impacting issues

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Monetizing 5G SA = ML-Infused Automation + New Operational Methods

Recovering from the Covid-19 pandemic, the importance of seamless telecommunication services delivering high-speed, ultra-low latency, differentiated network services is more pronounced and significant than ever. As a result, a Telco paradigm shift towards 5G Standalone (5G SA) is imperative to deliver differentiated, highly valuable services, potentially rendering 4G LTE and 5G non-standalone (5G NSA) technologies futile.  

   

Beyond Telco, other Vertical Enterprises (aka Smart Enterprises), such as the health care and automotive industry, are leveraging the benefits of 5G SA. The diverse applicability of 5G SA is attributed to its distinctive dynamic individuality & its ability to deliver:   

  • Massive Machine-Type Communications 
  • Simplified Network Architecture  
  • Ultrareliable, low latency comms 
  • Network Slicing (hence new Monetizable Services) 
  • Network Cost Optimization  

  

ML-Infused Automation 

  

5G SA needs ML-infused automation of a disaggregated and reaggregated full stack solution. Though 5G Standalone technology promises numerous advantages, its induction has proven challenging. 5G SA is complex because it demands the disaggregation and reaggregation of the vertical (full) stack. The technology entails multiple and diverse horizontal layers provided by different vendors, hence disaggregation. Afterward, the reaggregation of the vertical (full) stack delivers a flexible comprehensive architecture that combines all the distinctive (and differentiated capabilities) chosen from the individual horizontal layers. This full stack architecture requires at least five different vendors (one from each horizontal row, see Figure 1).  

 

It is arduous to judiciously select five vendors horizontally only to reintegrate the chosen ones vertically for an optimized monetizable architecture. Achieving this requires a scientific selection process to incorporate the layers in a lab environment & verify their homogenized functionality. The scattered nature of these layers not being pre-integrated and validated necessitates ML-infused automated blueprint validation to deliver an optimal architectural implementation. This blueprint needs to be configurable and, more importantly, repeatable. Accordingly, an automated method for blueprint delivery is essential as 5G SA demands automated segregation and reintegration.  

  

Figure 1: Blueprints for Disaggregation and Reaggregation 

 

 
 
Ideally, 5G SA services need to be conceptualized and delivered in minutes or hours through self-service accounts that allow users to create services from a catalog. 5G SA’s capabilities, such as network slicing, support the creation of differentiated services that provide innovative monetizable value to consumers and Smart (vertical) Enterprises. For example, a sliced network can address the needs of crucial communications like e911 while another slice of the same network handles V2V or V2I & another handles IIoT (monitoring gas pipeline leaks). In this example, each slice requires ultra-low latency at the sub-milliseconds level to address new B2B2C models. These 5G SA network sliced latencies are lower than those typically used in 4G LTE (and even 5G NSA) networks. Therefore, it is essential to test end-to-end precision, speed, and latency to ensure that the quality grade is suitable for mission-critical M2M, health sector & Autonomous Driving (ADAS) tasks.  
 

End-to-End Validation Required for New Operational Methods 

 

5G SA capabilities, such as network slicing, uLLC (ultra-low latency), and eMBB (improved broadband services), facilitate many new differentiated services. However, developing new end-to-end services designed for rapid operationalization (in hours, days, or seconds) is immensely difficult. Presently, simple, preliminary network services take months, quarters, or up to a year to enable! Nonetheless, rapid rollouts for new premium differentiated services are crucial for the operation and monetization of 5G SA and the realization of its full potential. 

 

New differentiated services as required by B2B customers and Smart Enterprises (aka vertical industries) need ubiquitous end-to-end network services (from app to UE to Tower to RAN to Core/IMS through S1 to internet/server) with value generation enabled for Private Wireless. Facilitating these universal services requires all network parts (from app/UE to Core to app/server/internet) to be provisioned using an amalgamation of service catalogs of sub-systems dynamically strung together and tested on an almost real-time basis.  

   

Additionally, operational needs require Zero Touch Service Provisioning (ZTSP) (not just ZTP as defined by TM Forum). Service creation needs to happen end-to-end horizontally (across the X-Axis) from App/UE to Core, along with configuring and setting up the vertically reaggregated (full) stack, (across the Y-Axis) to deliver the complete solution. Creating dynamic services using approaches such as BPM (Business Process Models) is paramount to competing in the modernization of telecommunications service delivery. 

 

In conclusion, a mix of ML-infused automation and ZTSP enabled SDx (Service Delivery Experience) is required to monetize and offer new services, at an exponentially accelerated pace, using new operational models that address the needs of B2B and B2B2C ecosystems. While there may appear to be a multitude of "buzz" solutions available from various vendors, one that delivers both (a) ML-infused automation and (b) ZTSP that enables (c) integration across a broad ecosystem is needed to monetize 5G SA's value.  

 

B-Yond's product AGILITY provides an ML-infused approach with ZTSP to realize the value of delivering SDx (Service Delivery Experience) and monetize 5G SA’s value. The new 5G standalone technology era brings advanced capabilities and challenges juxtaposed against its predecessors. Though the configuration of the 5G SA blueprints is rugged, the demand for this new wave of standalone technology is far too significant to ignore. The introduction of 5G SA blueprints will pioneer new and accelerated means of monetization across the telco industry. B-Yond aims to expand on the usage of 5G SA and is currently already using ML-infused automation to provide solutions such as Continuous Assurance (CA) via Anomaly Detection and Continuous Validation (CV) via Root Cause Analysis (RCA). RCA applies machine learning to automate test analyses & troubleshooting & predicts root cause for failures, decreasing the test life cycle and accelerating time-to-market. Anomaly Detection provides service assurance through continuous monitoring, prediction, & impact analysis, thus, accelerating the transformation to a low touch network with ML-based automation. 

 

B-Yond’s ML-infused automation solution using our product, AGILITY, enables these much-needed capabilities. Refer to our website or ask for a demo/presentation for more details. 

 

Website:  https://bit.ly/B-YOND
Contact Email: contact@b-yond.com 

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Reaching B-Yond: The Frontier of ModelOps for Telcos

Reaching B-Yond: The Frontier of ModelOps for Telcos 

B-Yond is in the vanguard of implementing carrier-grade MLOps for Telcos and Private Wireless. Though, you don't need to take our word for it. B-Yond is on the FutureNet World AIOps Award 2022 shortlist for The Best Operations Solution Incorporating AI. Read on to learn how B-Yond drives Telco and Smart Enterprises advancement to meet the expectations of Industry 4.0 and Private Wireless by pushing to further the capabilities of AI and ML. 

B-Yond's Pre-Trained Telco Optimized ML ServiceModels are used by all three US Tier-1 wireless operators and Tier-1 operators in EMEA in production at scale. Our MLOps approach exponentially accelerates network service transformation, reduces MTTR, increases ARPU by delivering seamless Service Delivery Experience (SDx). We use ML-Infused automation, observability & remediation solutions to replace the manual resolution of live production issues by automating E2E lab to live & live to lab implementations. 

Our Pre-Trained ServiceModels predict failures and enable pre-emptive closed loop network remedies that facilitate uninterrupted end-to-end network services. As a result, our product AGILITY allows customers to attain a 7-10x Return on Investment (ROI). In addition, the CA (Continuous Assurance) implementation of AGILITY prevents adverse customer impact & saves our customers millions of dollars by predicting issues up to 36 minutes ahead of failure (these results are based on the prediction timeframes recently recorded in customer's environment). 

When applied to mission-critical use cases such as Emergency Response networks, our products significantly increase reliability, reduce mean time-to-repair (MTTR), increase ROI, improve customer experiences & even save lives by predicting and pre-emptively remediating mission-critical (e911) network issues.  

Our product, AGILITY, provides Telcos with Continuous Assurance through Anomaly Detection (AD), Anomaly Prediction (AP) & Continuous Validation (CV) via Root Cause Analysis (RCA), Managed Labs. In addition, AGILITY applies Pre-Trained ML ServiceModels to infuse automated test analysis into production & identify root errors & root causes, thereby facilitating real-time remediation. AGILITY digs five levels deep towards identifying the root cause of problems (ask us to demonstrate it). 

 Conversely, competitors' rule-based (and even ML-based) root error solutions merely showcase root errors without unearthing root causes 

Through RCA, CV decreases test life cycles and accelerates time-to-market. Moreover, AD/AP accelerates the transformation to a low-touch, highly reliable network with ML-Infused Observability. AD provides service assurance via continuous monitoring, prediction, and impact analysis. Additionally, our ML-Infused Managed Labs improve time-to-market & quality through lab automation. Consequentially, Managed Labs reduce costs by making pre-production environments predictable, scalable, & zero-touch. 

 

Our implementations of MLOps use cases (using ModelOps) substantiate that AI and ML aren't just hype; they're a requirement for production at scale. In use cases with the top 3 Tier-1 wireless providers in the US and the top 2 Tier-1 Telcos in EMEA, B-Yond has produced the following results: 

Reduction of Network Lifecycle Costs 

AGILITY's models reduce network operations costs by up to 30 % by identifying highly impactful anomalies with results-driven prioritization and visualization. Further, our ML-Infused Root Cause Analysis models significantly decrease OpEx with an average test cost reduction of 75%. For a test suite of 25,000 tests, the cost savings alone amounted to $50M (per year across pre-production and production). 

 

Drive Revenue & Growth 

Through predictive procedures that anticipate, resolve, and prevent problems, AGILITY reduces revenue at risk and decreases customer complaints resulting from service issues by up to 20%. In addition, by optimizing lab launches and implementations, AGILITY's Managed Labs achieve up to a 33% reduction in lab lineup durations translating to a 10X return on ROI.  

 

Innovative Technological Implementation  

Our Root Cause Analysis significantly accelerates time-to-market for new features by delivering up to a 10X increase in test capacity, improving network quality, and hastening resolution times.  

 

Moreover, AGILITY enables self-healing networks by exposing remediation actions and integrating with existing orchestration and control systems resulting in up to a 4X increase in network autonomy. As a result, AD achieves up to a 25% decrease in mean-time-to-repair (MTTR), allowing engineers to focus on high-level troubleshooting and process optimization.  

 

Enhanced Customer Experience  

Our ML-powered closed loop in Anomaly Detection and Prediction improves Net Promoter (NPS) scores by decreasing network downtime & preventing customer-impacting anomalies through proactive redress that reduces user-facing incidents by up to 41%. 

 

B-Yond is catalyzing the telco paradigm shift away from expensive AI or MLCoEs that struggle to create repeatable production-worthy ML ServiceModels. Our clients reap unprecedented benefits using our ML-Infused NetDevOps tools and business chain solutions to achieve closed-loop automation and remediation. This shift in technological advancement is necessary for Telcos to address market demand and broaden their scope, transforming Telcos into TechCos to uplevel their ARPU and revenue potential. We're revolutionizing telco domain ML by using ModelOps to provide a robust, carrier-grade ML delivery pipeline.  

Are you ready to reach B-Yond barriers and brave the frontier of ModelOps for Telcos? If so, get in touch! 

www.b-yond.com 

contact@b-yond.com

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Telco AI Paradigm Shift: The Evolution of CI/CD & the Genesis of CV/CA

Traditional means of managing telecommunications networks are obsolete, and automation is at the forefront of development. The market-driven demand for automation presents a significant challenge to operators that requires a paradigm shift from reactive to proactive network monitoring, maintenance, and assurance.

 

Paradigm Shift Happens: Learn to "Operate Differently"

The increased capabilities of telco networks, driven by software-defined network disaggregation, necessitate improved network visualization, monitoring, and assurance. B-Yond provides the AI-Infused Network Services Automation solutions and products required to mediate this demand. B-Yond's automation solutions entice the telco industry to "Operate Differently" by providing the means to scale network optimization and monetization.

 

The Impetus: Market Demand and Industry Competition

Amidst the increasingly diversified telco provider landscape, leveraging the power of AI to achieve comprehensive automation is the mandate for attaining success. B-Yond's AI-infused NetDevOps tools and business chain solutions facilitate the genesis towards closed-loop automation. This shift is mandatory and inevitable due to technological advancement, market demand, and the broadened scope of network development and maintenance. Giants in the telecom industry have been actively pursuing automation transformation for years, with some claiming that their "network will be 55-75 percent virtualized by 2021" (TM Forum).

 

Catalyze Advancement: Data-driven Transformative Disruption

Modernizing telco networks entails fragmented vendor landscapes, network function disaggregation (VNF, CNF), software-defined networking (SDN), and the transition to hybrid telco cloud. As a result, the telecommunications industry is in a metamorphosis geared towards faster, automated, and continuously self-healing pre-production networks and production network services. This automation innovation will exponentially boost efficiency and ROI. B-Yond is on the frontier of the telecommunications remodel, working tirelessly to catalyze advancement in the industry by providing partners with the most cutting-edge AI-infused transformative network management approaches. Use case results substantiate that AI isn't just hype; it's a requirement.

 

The Way Forward: Continuous Assurance

Several operators have unsuccessfully tried leveraging Continuous Integration (CI) and Continuous Deployment (CD) from Staging to Preproduction to Production. These costly endeavors haven't delivered tangible results. The proven methodology for fast, optimized, and monetized network service deployments combines Continuous Validation (CV) and Continuous Assurance (CA).

 

Though traditional CI and CD toolchains are well known and revered, the narrative surrounding their autonomous efficacy is changing. Applying CI/CD to Telco workloads and network functions is challenging.

 

CI & CD are parts of the end-to-end process used to ensure that each new function or version journeys through multiple environments (recently, hybrid clouds) and undergoes testing for software consistency to deliver network services quickly and effectively. AI-Infused Continuous Validation (CV) enhances CI/CD by guaranteeing that the network function operates as a self-contained, seamless software solution which supports an end-to-end service.

 

AI-Infused CV comprises three phases of validation:

  • - Interface Level Validation – validating interop and standards compliance metrics
  • - Sub-System Level Validation – validating compliance to telco service levels
  • - End to End (E2E) Service-Level Validation – validating Service Delivery Experience (SDx)

 Each of these validation phases must operate, both in part and jointly, through a variety of scenarios.

 

In large part, many operators are already attempting to manage the need for CV, albeit unsuccessfully. The challenge is two-fold. First and most impactful, a significantly higher degree of volume and scale is required and expected across all telcom networks. Secondly, increases in individual functions are accompanied by a multitude of testing scenarios. Increased testing detects higher volumes of errors requiring analysis, causing further production delays. Accordingly, this increase in testing volume requires supplementary automation and precise fault and error isolation. B-Yond mitigates this issue by introducing AI-Infused Continuous Validation to automatically identify testing fallouts and pinpoint root errors. As a result, the automation cycle can finally scale to meet the much-needed demand.

 

In pursuit of fully automated, self-healing networks, CI/CD & AI-Infused CV must be engaged throughout pre-production and production networks. B-Yond deploys its AI-Infused Continuous Assurance (CA) chain to monitor all components of the network and its services in search of any anomalies that may occur, either by passive consumption of data or via active injection of simulated service consumption. Once an anomaly is detected, the next step is locating the root error to initiate the remediation process.

 

In our experience, taking the end-to-end Continuous Assurance in-house (versus outsourced to OEM's), automating it (via AI/ML), and treating the production network as an extension of the lab (versus having two extremely separate and unequal networks) allows telcos to reap unprecedented benefits. Inspired by operational expense reduction, customer satisfaction, high retention rates, user adoption improvements, as well as increased ROI, our clients share this mantra with us, “Automation Intelligence”.

 

B-Yond's AI-infused end-to-end life cycle network and service automation product, named AGILITY, answers CSP needs for all the previously mentioned network components that must become fully automated to remain relevant and competitive. Communication Service Providers can deploy cloud-native AGILITY to experience its value in pre-production and production environments. AGILITY's root error analysis, anomaly detection, and remediation capabilities enable optimized customer service delivery experiences (SDx). As a critical component of post-production deployment and subscriber data capture, AGILITY completes the end-to-end process.

We've borne witness to the successful genesis of AI Infused CV/CA; the benefits include:

  • - reducing test and validation life cycles from hours to minutes
  • - increasing service validation capacity 10X,
  • - reducing service delivery costs by 50% (on average), &
  • - detecting anomalies and root errors in real-time

The need for telcos to adopt AI Infused Continuous Validation & Assurance solutions to remain lucrative and competitive is undisputable. The call to action is clear; it's time to "Operate Differently".

 

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How AI Can Only Work When Humans Collaborate

Machines are more intelligent than humans; is this accurate? It depends. Researchers are currently developing algorithms and machine learning (ML) models within the narrow artificial intelligence framework (NAI), which outperforms humans in most specific cases (technically called targets).  

NAI candidate is a collection of ML models created and trained on historical behavioral data to learn patterns and causalities aiming to be used to solve a specific business problem. When trained and implemented in the automated pipeline, those models predict patterns and potentially impact decision-making become an AI process.  

 

The learning 

Events and objects are seen by babies and processed to create history and connectivity in their minds. Our memory stores events that allow the learning of logical connectivity between neurons. For machines, historical data replaces events and objects, which presents a substantial value of the machines given the current processing power that it can use to read and analyze years of data. On the other hand, humans need much more time to pass through the same events and objects. Moreover, a machine learning model can remember historical data patterns and make informed decisions based on what it has learned, which helps cover the needed touch points related to business processes for robust statistical learning. 

 

Challenges & Developments 

Data availability is one of the biggest challenges for ML model training. Good data governance initiatives for CSPs have a strategic added value for the adoption of AI initiatives. 

With a valid dataset, Data Scientists, with the help of subject matter experts (SMEs) in the industry specified, would develop and implement the appropriate model to solve the business problem in place, which will be later integrated by system developers for business decision making. 

How can we distinguish the "appropriate model" and its characteristics? Start by defining a model; it is a mix of mathematical and statistical formulas defined by types and parameters. Data scientists define types, while parameters are learned from the data. A model becomes appropriate when solving a business problem using the right data and is implemented, given its complexity and availability.  

 

Most of the used models fall under one of the following three types: supervised, unsupervised, and reinforcement learning. All types are designed to predict unseen events using seen/captured data. Thus, the second challenge of data scientists is to decide which model to use for each business problem. 

Without zooming in into statistical and mathematical assumptions, supervised models learn and memorize the historical patterns and co-occurrence of the outcome (also called a label) and derive the optimal statistical function that links the outcome to patterns. Unsupervised is the approach of learning data variations and classifying patterns into a measurable space. For example, to develop a similar root cause of network failures, an unsupervised approach would help. In contrast, a supervised approach is a way to learn the behavior of an outcome through patterns and variations of data captured, such as predicting the type of a network failure event based on what happened before that event. When it comes to reinforcement learning, which is an innovative approach in the data science space and heavily used in certain industries, including telecom, it consists of integrating a human in model tuning and adaptation following some changes in the data flow coming to the model. Theoretically, the human is called an "agent" with a specific gain and loss utility function and trying to direct the learning towards maximizing profit.  

 

Optimal way 

The optimal model is the one with higher statistical accuracy (defined as "the appropriate model"), and minimizes training efforts and implementation/integration time with the environment generating the data. This tradeoff discussion is highly essential and should be a joint discussion between a data scientist and SMEs from the early stages of project design and roadmap. We have seen a high percentage of failed AI initiatives within organizations due to the lack of communication from the start. Data scientists tend to use the challenging mathematical and statistical approach, while CSPs are looking for a final output with clear objectives to solve a specific business problem. 
 

B-Yond 

A practical example is B-Yond's Agility products, which are based on advanced supervised learning on top of a reinforcement approach by integrating an SME (agent) in the loop for continuous feeding of relevant information from the network to the model maximize the accuracy of detecting the root cause. B-Yond's configurable black-boxes of pre-trained models are also already optimized on both dimensions: fully automated with highly intelligent models but keeping the necessary model configurations parameters available for SMEs to control models' operational aspects. 

In the next series of blogs, we will go more into technical details of Agility product configurable black-boxes-AI models. We will also discuss Anomaly detection pre-trained robots that sit in CSPs environments 24/7 learning from data that report any issue and sometimes fix it automatically.  

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