AI/ML
Enable-6G: Yet another 6G R&D effort spearheaded by Telefónica de España
Telefónica de España has initiated yet another 6G R&D project, named Enable-6G, that aims to tackle the user privacy protection and energy-efficiency challenges associated with future generation wireless networks. In a statement, the Spanish telco announced the launch of the Enable-6G project, which is funded by the European Union’s economic recovery plan NextGenerationEU as well as Spain’s Ministry of Economic Affairs and Digital Transformation.
The initiative is led by the IMDEA Networks Institute (an innovation and development centre in Spain) and includes involvement from tech giant NEC and BluSpecs (a Spanish digital transformation consultant). It is designed to address “the challenges that will be faced by future 6G networks, such as increased connectivity, higher performance demands, and advanced object and environment detection and communication,” the company noted.
One of the main objectives is to ensure advanced privacy protections are built into the architecture, as precise mapping and sensing, data privacy and security have become major concerns, and has also become a major benefit for new use cases. Another strategic objective is the design and implementation of software-defined networks that can operationalise optimized edge-to-cloud processing to facilitate time-critical and geo-distributed network orchestration (e.g., via the application of control-task algorithms). The ENABLE-6G project represents a major step forward in the new technologies into 6G to improve wireless communications, provide environmental sensing and significantly reduce the energy footprint per device to avoid a large overall increase in network power consumption. We are excited about the potential impact of this project and look forward to collaborating with our partners to bring it to fruition.
Telefónica is one of the leading private R&D centers in Spain, aiming to explore and develop new technologies and solutions that can improve the company’s existing products and services, as well as identify and create new business opportunities in the telecommunications and technology sectors. One of the big companies joining this project is NEC Corporation, with a great capacity has a strong commitment to research and development and invests heavily in new technologies and innovative solutions. ENABLE-6G counts on the excellent IMDEA Networks scientists, one of the best innovation and development centres in Spain, with a variety of experts from all over the world. Finally, this project will count on the consultancy of BluSpecs, facilitating the digital transformation of private and public organisations through the application of knowledge, data, and methodologies in the field of strategy, implementation of new technologies and innovation.
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Opinion: The rush to 6G R & D is incomprehensible to this author, as there are still so many holes in 5G specifications and standards. Moreover, 5G Advanced specs (3GPP Release 18) have not been completed. Hence, there is no ITU-R standards work even started for that. There isn’t even an ITU-R recommendation that specifies 6G functionality or features!
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The development of the G project “has become crucial”, according to Telefónica, as it has become evident that 6G networks need to be “more adaptable and intelligent” so that they can give rise to a future vision that tackles “greater levels of complexity, contextualisation, and data traffic” – all the while consuming less energy, and providing enhanced security and privacy measures so that anyone developing future technology is given the level of trust required for the “widespread implementation of next-generation devices and nodes”.
A main objective for the Enable-6G project is to ensure “advanced privacy protections are built into the architecture,” given that precise mapping and sensing, as well as data privacy and security, are major concerns but also provide a great opportunity for new service development.
The initiative will also focus on designing and implementing software-defined networks (SDN) that can operationalise optimised edge-to-cloud processing, with the end goal being to support time-critical and geo-distributed network orchestration.
Enable-6G will look to provide “environmental sensing” which, according to Telefónica, will significantly reduce the energy footprint per device and prevent a large increase in overall network power usage.
While 5G networks and services are still being deployed and developed, many players in the industry are already exploring the potential of wireless 6G.
As well as Enable-6G, Telefónica is also active in another European 6G project, called Hexa-X-II, which involves participation from Orange and Telecom Italia, as well as vendors Nokia and Ericsson.
Also in Europe, German operator Deutsche Telekom is leading a consortium of 22 partners as part of the 6G-TakeOff research project within the broader 6G industrial projects funded by the German Federal Ministry of Education and Research (BMBF) – see Deutsche Telekom, Nokia take lead roles in European 6G projects. Ericsson is launching a €5.7m research and innovation consortium in Europe, called Deterministic6G, with which Orange is also involved, as well as several other members – see News brief: 6G R&D gathers pace in Europe.
Meanwhile, China plans to launch 6G by 2025 – way in advance of any standards which imply no interoperability! India has their Bharat 6G vision document with plans to launch a 6G research and development testbed.
In the UK, the government has invested £110m in 5G, 6G and telecom security research and development initiatives, in collaboration with BT, Cellnex, Virgin Media O2, Ericsson, Mavenir, Nokia, Parallel Wireless and Samsung, among others – see UK government pumps £110m into 5G, 6G R&D.
More recently, the UK Department for Science, Innovation and Technology (DSIT) announced that it plans to invest up to £100m into “a new long-term national mission to ensure that the UK is at the forefront of both adopting and developing 6G – the future of digital connectivity.”
Elsewhere, Japanese telco NTT Docomo is also taking strides towards shaping the future of 6G, including issuing advice in the form of whitepaper reports in partnership with its South Korean peer SK Telecom (SKT).
While in India, Prime Minister Narendra Modi has recently set out a vision, dubbed Bharat 6G, that aims to put India on the global map of leaders in the 6G era – see India eyes global leadership role in 6G.
North American is also involved into the 6G R&D sector. US industry group The Next G Alliance has been active in depicting a 6G vision for North America, drawing up a roadmap of necessary steps to secure the region’s leadership in wireless technology from the next decade onwards.
References:
Enable-6G launched to unlock the potential of Future 6G Networks
https://www.telecomtv.com/content/6g/telef-nica-joins-europe-s-latest-6g-r-d-effort-47305/
China to introduce early 6G applications by 2025- way in advance of 3GPP specs & ITU-R standards
India unveils Bharat 6G vision document, launches 6G research and development testbed
NTT DOCOMO & SK Telecom Release White Papers on Energy Efficient 5G Mobile Networks and 6G Requirements
Juniper Research: 5G to Account for 80% of Operator Revenue by 2027; 6G Requires Innovative Technologies
China’s MIIT to prioritize 6G project, accelerate 5G and gigabit optical network deployments in 2023
China Mobile unveils 6G architecture with a digital twin network (DTN) concept
Summary of ITU-R Workshop on “IMT for 2030 and beyond” (aka “6G”)
Arista Networks unveils cloud-delivered, AI-driven network identity service
At the RSA Conference today, Arista Networks announced a cloud-delivered, AI-driven network identity service for enterprise security and IT operations. Based on Arista’s flagship CloudVisionⓇ platform, Arista Guardian for Network Identity (CV AGNI™) expands Arista’s zero trust networking approach to enterprise security. CV AGNI helps to secure IT operations with simplified deployment and cloud scale for all enterprise network users, their associated endpoints, and Internet of Things (IoT) devices.
“Proliferation of IoT devices in the healthcare network creates a huge management and security challenge for our IT and security operations. The ease of securely onboarding devices on the network by CV AGNI and its integration with Medigate by Claroty for device profiling greatly simplifies this problem for a healthcare network,” said Aaron Miri, CIO of Baptist Healthcare.
AI-Driven Network Identity brings Simplicity and Security at Scale
While enterprise networks have seen massive transformation in recent years with the adoption of cloud and the acceleration of a post-pandemic, perimeter-less enterprise, Network Access Control (NAC) solutions have changed little for decades. Traditional NAC solutions continue to suffer from the complexity of on-premises deployment and administration and have been unable to adapt to the explosion of SaaS-based identity stores, users, devices and their associated profiles across the enterprise.
CloudVision AGNI takes a novel approach to enterprise network identity management. Built on a modern, cloud-native microservices architecture, the CV AGNI solution leverages AI/ML to greatly simplify the secure onboarding and troubleshooting for users and devices and the management of ever-expanding security policies.
CV AGNI is based on Arista’s foundational NetDL architecture and leverages AVA™ (Autonomous Virtual Assist) for a conversational interface that removes the complexity inherent in managing network identity from a traditional legacy NAC solution. AVA codifies real-world network and security operations expertise and leverages supervised and unsupervised ML models into an ‘Ask AVA’ service, a chat-like interface for configuring, troubleshooting and analyzing enterprise security policies and device onboarding. CV AGNI also adds user context into Arista’s network data lake (NetDL), greatly simplifying the integration of device and user information across Arista’s products and third-party systems.
CloudVision AGNI delivers key attributes from client to cloud across the cognitive enterprise:
- Simplicity: CV AGNI is a cloud service that eliminates the complexity of planning and scaling the compute resources for an on-premises solution. Administrative actions take a fraction of the time compared to a traditional NAC solution. It also natively integrates with industry-leading identity stores.
- Security: CV AGNI leapfrogs legacy NAC solutions by redefining and greatly simplifying how enterprise networks can be secured and segmented by leveraging user and device context in the security policies.
- Scale: With a modern microservices-based architecture, the CV AGNI solution scales elastically with the growing needs of any enterprise.
CloudVision Delivers Network Identity as-a-Service
Based on the CloudVision platform, CV AGNI delivers network identity as a service to any standards-based wired or wireless network.
CloudVision AGNI’s key features include the following:
- User self-service onboarding for wireless with per-user unique pre-shared keys (UPSK) and 802.1X digital certificates.
- Certificate management with a cloud-native PKI infrastructure
- Enterprise-wide visibility of all connected devices. Devices are discovered, profiled and classified into groups for single-pane-of-glass control.
- Security policy enforcement that goes beyond the traditional inter-group macro-segmentation and includes intra-group micro-segmentation capabilities when combined with Arista networking platforms through VLANs, ACLs, Unique-PSK and Arista MSS-Group techniques.
- AI-driven network policy enforcement based on AVA for behavioral anomalies. When a threat is detected by Arista NDR, it will work with CV AGNI to quarantine the device or reduce its level of access.
Tailored for Multi-vendor Integration
CloudVision AGNI leverages cognitive context from third-party systems, including solutions for mobile device management, endpoint protection, and security information and event management. This greatly simplifies the identification and onboarding process and application of segmentation policies. Examples include:
- Endpoint Management: Medigate by Claroty, CrowdStrike XDR, Palo Alto Cortex XDR
- Identity Management: Okta, Google Workspace, Microsoft Azure, Ping Identity and OneLogin.
- MDM: Microsoft Intune, JAMF
- SIEM: Splunk
- Networking devices: Multi-vendor interoperability in addition to Arista platforms
Availability
CV AGNI is integrated into Arista CloudVision to provide a complete identity solution. CV AGNI is in trials now with general availability in Q2 2023.
Visit us at booth #1443 at RSA. Learn more about AI-driven network identity at Arista’s webinar on May 18, register here. For more insight on this announcement, read Jayshree Ullal’s blog here.
About Arista
Arista Networks is an industry leader in data-driven, client to cloud networking for large data center, campus and routing environments. Arista’s award-winning platforms deliver availability, agility, automation, analytics and security through an advanced network operating stack. For more information, visit www.arista.com.
Competing Product:
SailPoint’s AI driven Identity Security Platform
References:
https://www.arista.com/en/company/news/press-release/17244-pr-20230424
https://www.sailpoint.com/platform/?campaignid=11773644133
Arista’s WAN Routing System targets routing use cases such as SD-WANs
AT&T realizes huge value from AI; will use full suite of NVIDIA AI offerings
Executive Summary:
AT&T Corp. and NVIDIA today announced a collaboration in which AT&T will continue to transform its operations and enhance sustainability by using NVIDIA-powered AI for processing data, optimizing service-fleet routing and building digital avatars for employee support and training.
AT&T is the first telecommunications provider to explore the use of a full suite of NVIDIA AI offerings. This includes enhancing its data processing using the NVIDIA AI Enterprise software suite, which includes the NVIDIA RAPIDS Accelerator for Apache Spark; enabling real-time vehicle routing and optimization with NVIDIA cuOpt; adopting digital avatars with NVIDIA Omniverse Avatar Cloud Engine and NVIDIA Tokkio; and utilizing conversational AI with NVIDIA Riva.
“We strive each day to deliver the most efficient global network, as we drive towards net zero emissions in our operations,” said Andy Markus, chief data officer at AT&T. “Working with NVIDIA to drive AI solutions across our business will help enhance experiences for both our employees and customers.” He said it’s AT&T’s goal to make AI part of the fabric of the company, to have “all parts of the business leveraging AI and creating AI” rather than limit its use to creation of AI by its specialist data scientists.
“Industries are embracing a new era in which chatbots, recommendation engines and accelerated libraries for data optimization help produce AI-driven innovations,” said Manuvir Das, vice president of Enterprise Computing at NVIDIA. “Our work with AT&T will help the company better mine its data to drive new services and solutions for the AI-powered telco.”
The Data Dilemma:
AT&T, which has pledged to be carbon neutral by 2035, has instituted broad initiatives to make its operations more efficient. A major challenge is optimizing energy consumption while providing network infrastructure that delivers data at high speeds. AT&T processes more than 590 petabytes of data on average a day. That is the equivalent of about 6.5 million 4K movies or more than 8x the content housed in the U.S. Library of Congress if all its collections were digitized.
Telecoms aiming to reduce energy consumption face challenges across their operations. Within networks, the radio access network (RAN) consumes 73% of energy, while core network services, data centers and operations use 13%, 9% and 5%, respectively, according to the GSMA, a mobile industry trade group.
AT&T first adopted NVIDIA RAPIDS Accelerator for Apache Spark to capitalize on energy-efficient GPUs across its AI and data science pipelines. This helped boost its operational efficiency across everything from training AI models and maintaining network quality and optimization, to reducing customer churn and improving fraud detection.
Of the data and AI pipelines targeted with Spark-RAPIDS, AT&T saves about half of its cloud computing spend and sees faster performance, while enabling reductions in its carbon footprint.
Enhanced Field Dispatch Services:
AT&T, which operates one of the largest field dispatch teams to service its customers, is currently testing NVIDIA cuOpt software to enhance its field dispatch capabilities to handle more complex technician routing and optimization challenges. AT&T has a fleet of roughly 30,000 vehicles with over 700 million options in how they can be dispatched and routed. The operator would run dispatch optimization algorithms overnight to get plans for the next day, but it took too long and couldn’t account for the realities that would crop up the next morning: Workers calling in sick, vehicles breaking down, and so on.
“It wasn’t as good at noon as it was at 8 in the morning,” Markus said. Using Nvidia GPUs and software, he said, AT&T was able to speed up its processing 60x so that it could run the scenario in near-real-time, as often as it needed to and achieve more jobs in a day (as well as reduce its cloud-related costs by 40%).
Routing requires trillions of computations to factor in a variety of factors, from traffic and weather conditions to customer change of plans or a technician’s skill level, where a complicated job might then require an additional truck roll.
In early trials, cuOpt delivered solutions in 10 seconds, while the same computation on x86 CPUs took 1,000 seconds. The results yielded a 40% reduction in cloud costs and allowed technicians to complete more service calls each day. NVIDIA cuOpt allows AT&T to run nearly continuous dispatch optimization software by combining NVIDIA RAPIDS with local search heuristics algorithms and metaheuristics such as Tabu search.
Pleasing Customers, Speeding Network Design:
As part of its efforts to improve productivity for its more than 150,000 employees, AT&T is moving to adopt NVIDIA Omniverse ACE and NVIDIA Tokkio, cloud-native AI microservices, workflows and application frameworks for developers to easily build, customize and deploy interactive avatars that see, perceive, intelligently converse and provide recommendations to enhance the customer service experience.
For conversational AI, the carrier also uses the NVIDIA Riva software development kit and is examining other customer service and operations use cases for digital twins and generative AI.
AT&T also is taking advantage of fast 5G and its fiber network to deliver NVIDIA GeForce NOW™ cloud gaming at 120 frames per second on mobile and 240 FPS at home.
Markus added that AI-powered Nvidia tools are also helping AT&T to both serve its customers better through various channels, from sales recommendations to customer care; and that its internal processes are leveraging AI as well, to help employees be more efficient. The company is embracing Nvidia’s AI solutions as a foundation for development of interactive and intelligent customer service avatars.
In the past 12 months, AI has created more than $2.5 billion in value for AT&T. About half of that came via Marcus’ team, but the other half came from what he calls “citizen data scientists” across the company who have been able to leverage AI to solve problems in their respective areas, whether than was marketing, network operations, software development or finance.
“As we mobilize that citizen data-scientist across the company, we’re doing that via a self-service platform that we call AI-as-a-service, where we’re bringing a unified experience together. But behind the experience, we’re allowing those users to leverage AI in a curated way for their use case,” he explained. “So they bring their subject matter expertise to the problem that they’re trying to solve, and we … enable the technology [and processes for them to create] robust AI. But we also govern it with some guardrails, so the AI we’re creating is ethical and responsible.”
In AT&T’s automation development, 92% of its automation is created by employees via self-service to solve a problem. “The goal is that over time, we bake in incredible functionality like Nvidia, so that AI-as-a-service is delivering that self-service functionality so that we do most of our routine AI creation via the platform, where you don’t have to have a professional data scientist, a code warrior, to be your sherpa,” Markus concluded.
References:
https://nvidianews.nvidia.com/news/at-t-supercharges-operations-with-nvidia-ai
Nvidia Survey Reveals How Telcos Plan to Use AI; Quantifying ROI is a Challenge
A Nvidia sponsored survey of more than 400 telecommunications industry professionals from around the world found a cautious tone in how they plan to define and execute on their AI strategies. Virtually every telco is already engaged with AI in some way, although mostly at an early stage. NVIDIA’s first “State of AI in Telecommunications” survey consisted of questions covering a range of AI topics, infrastructure spending, top use cases, biggest challenges and deployment models. The survey was conducted over eight weeks between mid-November 2022 and mid-January 2023.
Amid skepticism about the money-making potential of 5G, telecoms see efficiencies driven by AI as the most likely path for returns on investment. 93% of those responding to questions about undertaking AI projects at their own companies appear to be substantially underinvesting in AI as a percentage of annual capital spending.
Some 50% of respondents reported spending less than $1 million last year on AI projects; a year earlier, 60% of respondents said they spent less than $1 million on AI. Just 3% of respondents spent over $50 million on AI in 2022.
The reasons cited for such cautious spending? Some 44% of respondents reported an inability to adequately quantify return on investment, which illustrates a mismatch between aspirations and the reality in introducing AI-driven solutions. 34% cited an insufficient number of data scientists as the second-biggest challenge.
The biggest telco objectives for AI are to: optimize operations (60%), lower costs (44%) and enhance customer engagement (35%). Respondents cited use cases ranging from cell site planning and truck-route optimization to recommendation engines.
Just over a third of respondents said they had been using AI for more than six months. 31% said they’re still weighing different options, 18% reported being still in a trial phase and only 5% said they had no AI plans at all. Most industry execs say they see AI technologies will positively impact their business – 65% agreed AI was important to their company’s success, and 59% said it would become a source of competitive advantage.
Operators are spending a fraction of their capex budgets on AI projects – last year half said they spent less than $1 million on AI. At the top end, 2% spent more than $50 million in 2021, with that number rising to 3% in 2022.
The latest AI Index compiled by Stanford University puts telcos at the forefront of AI deployment. Using its own data and that from a McKinsey study, it found that the highest level of AI adoption is in product or service development by hi-tech companies and telcos (45%), followed by AI in service operations (45%).
The biggest single application in any industry was natural language text understanding deployed by 34% of hi-tech and telco firms, with 28% implementing AI-based computer vision and 25% using virtual agents.
- Moving from proof of concept to production/scale 47%
- Economic uncertainty 46%
- Infrasctructure upgrades 46%
- Market differentiation 34%
- Change in priority of data science 20%
- 92% will either increase or maintain their AI spend in 2023.
References:
https://blogs.nvidia.com/blog/2023/02/21/telco-survey-ai/
https://www.nvidia.com/en-us/lp/industries/telecommunications/state-of-ai-in-telecom-survey-report/
https://aiindex.stanford.edu/wp-content/uploads/2022/03/2022-AI-Index-Report_Master.pdf
Allied Market Research: Global AI in telecom market forecast to reach $38.8 by 2031 with CAGR of 41.4% (from 2022 to 2031)
Global AI in Telecommunication Market at CAGR ~ 40% through 2026 – 2027
The case for and against AI in telecommunications; record quarter for AI venture funding and M&A deals
SK Telecom inspects cell towers for safety using drones and AI
Summary of ITU-R Workshop on “IMT for 2030 and beyond” (aka “6G”)
Ericsson & Mobily enhance network performance through Artificial Intelligence (AI)
Ericsson has developed an AI system for automated network management which has now been included in Saudi Arabia operator Mobily’s wireless network. The companies have successfully deployed the ‘Ericsson AI-based network solution’ into Mobily’s network in Saudi Arabia in order to enable some ‘enhanced and smart end-user experiences.’
This AI system will provide 5G network diagnostics, root cause analysis and recommendations for ‘superior user experiences.’ The network diagnostics capabilities within the cognitive software suite provides ‘proactive network optimization’, allowing the operator to identify and resolve network anomalies and providing reliable connectivity, we are told.
Ericsson’s AI-based network solution delivers comprehensive Machine Learning (ML) based 5G network diagnostics, root cause analysis and recommendations for superior user experiences. The smart, automated network diagnostics capabilities of Ericsson’s cognitive software suite results in proactive network optimization, supporting Mobily, the leading digital partner of the international technical conference LEAP 23, in identifying and resolving network anomalies and constantly providing reliable connectivity.
Ericsson is so excited by the product in fact that it says it ‘redefines the very nature of network operations,’ alongside the presence of Big Data and ‘ever-expanding and more accessible computing power.’
“From people in remote locations to large gatherings, individuals often expect uninterrupted and quality connectivity,” said Alaa Malki, Chief Technology Officer from Mobily. “Ericsson’s Artificial Intelligence (AI)-based solution enables our customers to enjoy superior and uninterrupted 5G connectivity to stay connected with loved ones or to document key moments anytime, anywhere. Our partnership with Ericsson has once more reinforced our commitment to Unlock Possibilities during times that matter most, and we look forward to carrying our mission forward. I want to thank Ericsson for its support which allowed us to use this data-driven concept to make all kinds of changes and optimizations within short timeframes.”
Ekow Nelson, Vice President at Ericsson Middle East and Africa said: “For numerous years, our partnership with Mobily has provided customers with assured and superior connectivity to stream live experiences and benefit from a multitude of services even in the most challenging environments. Our success relied on Ericsson’s Artificial Intelligence-based network solution built with Machine Learning models that learn from the live network using the multiple sources of data to deliver near real-time improvements, thus avoiding interruptions during critical and peak times.”
How AI interacts and disrupts different industries looks likely to be an increasingly prominent issue in the years to come, for all sorts of reasons. In an interview with Telecoms.com recently, Beerud Sheth – CEO of conversational AI firm Gupshup said, “Like almost any industry, telcos will also have to figure out how they see this disruption… it creates opportunities and threats. And I think you have to lean into the opportunities, and maybe mitigate the threats a little bit. It changes a lot of things, it changes consumer expectations, it changes what people expect and what they want to do and can do, and they have to keep pace with all of it. So, there’s a lot of work for telco executives.”
References:
Ericsson, Mobily successfully enhance network performance through Artificial Intelligence
Ericsson warns profit margins at RAN business set to worsen
Using a distributed synchronized fabric for parallel computing workloads- Part II
by Run Almog Head of Product Strategy, Drivenets (edited by Alan J Weissberger)
Introduction:
In the previous part I article, we covered the different attributes of AI/HPC workloads and the impact this has on requirements from the network that serves these applications. This concluding part II article will focus on an open standard solution that addresses these needs and enables these mega sized applications to run larger workloads without compromising on network attributes. Various solutions are described and contrasted along with a perspective from silicon vendors.
Networking for HPC/AI:
A networking solution serving HPC/AI workloads will need to carry certain attributes. Starting with scale of the network which can reach thousands of high speed endpoints and having all these endpoints run the same application in a synchronized manner. This requires the network to run like a scheduled fabric that offers full bandwidth between any group of endpoints at any given time.
Distributed Disaggregated Chassis (DDC):
DDC is an architecture that was originally defined by AT&T and contributed to the Open Compute Project (OCP) as an open architecture in September 2019. DDC defines the components and internal connectivity of a network element that is purposed to serve as a carrier grade network router. As opposed to the monolithic chassis-based router, the DDC defines every component of the router as a standalone device.
- The line card of the chassis is defined as a distributed chassis packet-forwarder (DCP)
- The fabric card of the chassis is defined as a distributed chassis fabric (DCF)
- The routing stack of the chassis is defined as a distributed chassis controller (DCC)
- The management card of the chassis is defined as a distributed chassis manager (DCM)
- All devices are physically connected to the DCM via standard 10GbE interfaces to establish a control and a management plane.
- All DCP are connected to all DCF via 400G fabric interfaces in a Clos-3 topology to establish a scheduled and non-blocking data plane between all network ports in the DDC.
- DCP hosts both fabric ports for connecting to DCF and network ports for connecting to other network devices using standard Ethernet/IP protocols while DCF does not host any network ports.
- The DCC is in fact a server and is used to run the main base operating system (BaseOS) that defines the functionality of the DDC
Advantages of the DDC are the following:
- It’s capacity since there is no metal chassis enclosure that needs to hold all these components into a single machine. This allows building a wider Clos-3 topology that expands beyond the boundaries of a single rack making it possible for thousands of interfaces to coexist on the same network element (router).
- It is an open standard definition which makes it possible for multiple vendors to implement the components and as a result, making it easier for the operator (Telco) to establish a multi-source procurement methodology and stay in control of price and supply chain within his network as it evolves.
- It is a distributed array of components that each has an ability to exist as a standalone as well as act as part of the DDC. This gives a very high level of resiliency to services running over a DDC based router vs. services running over a chassis-based router.
AT&T announced they use DDC clusters to run their core MPLS in a DriveNets based implementation and as standalone edge and peering IP networks while other operators worldwide are also using DDC for such functionality.
Figure 1: High level connectivity structure of a DDC
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LC is defined as DCP above, Fabric module is defined as DCF above, RP is defined as DCC above, Ethernet SW is defined as DCM above
Source: OCP DDC specification
DDC is implementing a concept of disaggregation. The decoupling of the control plane from data plane enables the sourcing of the software and hardware from different vendors and assembling them back into a unified network element when deployed. This concept is rather new but still has had a lot of successful deployments prior to it being used as part of DDC.
Disaggregation in Data Centers:
The implementation of a detached data plane from the control plane had major adoption in data center networks in recent years. Sourcing the software (control plane) from one vendor while the hardware (data plane) is sourced from a different vendor mandate that the interfaces between the software and hardware be very precise and well defined. This has brought up a few components which were developed by certain vendors and contributed to the community to allow for the concept of disaggregation to go beyond the boundaries of implementation in specific customers networks.
Such components include open network install environment (ONIE) which enables mounting of the software image onto a platform (typically a single chip 1RU/2RU device) as well as the switch abstraction interface (SAI) which enable the software to directly access the application specific integrated circuit (ASIC) and operate directly onto the data plane at line rate speeds.
Two examples of implementing disaggregation networking in data centers are:
- Microsoft which developed their network operating system (NOS) software Sonic as one that runs on SAI and later contributed its source code to the networking community via OCP and he Linux foundation.
- Meta has defined devices called “wedge” who are purpose built to assume various NOS versions via standard interfaces.
These two examples of hyperscale companies are indicative to the required engineering effort to develop such interfaces and functions. The fact that such components have been made open is what enabled other smaller consumers to enjoy the benefits of disaggregation without the need to cater for large engineering groups.
The data center networking world today has a healthy ecosystem with hardware (ASIC and system) vendors as well as software (NOS and tools) which make a valid and widely used alternative to the traditional monolithic model of vertically integrated systems.
Reasons for deploying a disaggregated networking solution are a combination of two. First, is a clear financial advantage of buying white box equipment vs. the branded devices which carry a premium price. Second, is the flexibility which such solution enables, and this enables the customer to get better control over his network and how it’s run, as well as enable the network administrators a lot of room to innovate and adapt their network to their unique and changing needs.
The image below reflects a partial list of the potential vendors supplying components within the OCP networking community. The full OCP Membership directory is available at the OCP website.
Between DC and Telco Networking:
Data center networks are built to serve connectivity towards multiple servers which contain data or answer user queries. The size of data as well as number of queries towards it is a constantly growing function as humanity grows its consumption model of communication services. Traffic in and out of these servers is divided to north/south that indicates traffic coming in and goes out of the data center, and east/west that indicates traffic that runs inside the data center between different servers.
As a general pattern, the north/south traffic represent most of the traffic flows within the network while the east/west traffic represent the most bandwidth being consumed. This is not an accurate description of data center traffic, but it is accurate enough to explain the way data center networks are built and operated.
A data center switch connects to servers with a high-capacity link. This tier#1 switch is commonly known as a top of rack (ToR) switch and is a high capacity, non-blocking, low latency switch with some minimal routing capabilities.
- The ToR is then connected to a Tier#2 switch that enables it to connect to other ToR in the data center.
- The Tier#2 switches are connected to Tier#3 to further grow the connectivity.
- Traffic volumes are mainly east/west and best kept within the same Tier of the network to avoid scaling the routing tables.
- In theory, a Tier#4/5/6 of this network can exist, but this is not common.
- The higher Tier of the data center network is also connected to routers which interface the data center to the outside world (primarily the Internet) and these routers are a different design of a router than the tiers of switching devices mentioned earlier.
- These externally facing routers are commonly connected in a dual homed logic to create a level of redundancy for traffic to come in and out of the datacenter. Further functions on the ingress and egress of traffic towards data centers are also firewalled, load-balanced, address translated, etc. which are functions that are sometimes carried by the router and can also be carried by dedicated appliances.
As data centers density grew to allow better service level to consumers, the amount of traffic running between data center instances also grew and data center interconnect (DCI) traffic became predominant. A DCI router on the ingress/egress point of a data center instance is now a common practice and these devices typically connect over larger distance of fiber connectivity (tens to hundreds of Km) either towards other DCI routers or to Telco routers that is the infrastructure of the world wide web (AKA the Internet).
While data center network devices shine is their high capacity and low latency and are built from the ASIC level via the NOS they run to optimize on these attributes, they lack in their capacity for routing scale and distance between their neighboring routers. Telco routers however are built to host enough routes that “host” the Internet (a ballpark figure used in the industry is 1M routes according to CIDR) and a different structure of buffer (both size and allocation) to enable long haul connectivity. A telco router has a superset of capabilities vs. a data center switch and is priced differently due to the hardware it uses as well as the higher software complexity it requires which acts as a filter that narrows down the number of vendors that provide such solutions.
Attributes of an AI Cluster:
As described in a previous article HPC/AI workloads demand certain attributes from the network. Size, latency, lossless, high bandwidth and scale are all mandatory requirements and some solutions that are available are described in the next paragraphs.
Chassis Based Solutions:
This solution derives from Telco networking.
Chassis based routers are built as a black box with all its internal connectivity concealed from the user. It is often the case that the architecture used to implement the chassis is using line cards and fabric cards in a Clos-3 topology as described earlier to depict the structure of the DDC. As a result of this, the chassis behavior is predictable and reliable. It is in fact a lossless fabric wrapped in sheet metal with only its network interfaces facing the user. The caveat of a chassis in this case is its size. While a well-orchestrated fabric is a great fit for the network needs of AI workloads, it’s limited capacity of few hundred ports to connect to servers make this solution only fitting very small deployments.
In case chassis is used at a scale larger than the sum number of ports per single chassis, a Clos (this is in fact a non-balanced Clos-8 topology) of chassis is required and this breaks the fabric behavior of this model.
Standalone Ethernet Solutions:
This solution derives from data center networking.
As described previously in this paper, data center solutions are fast and can carry high bandwidth of traffic. They are however based on standalone single chip devices connected in a multi-tiered topology, typically a Clos-5 or Clos-7. as long as traffic is only running within the same device in this topology, behavior of traffic flows will be close to uniform. With the average number of interfaces per such device limited to the number of servers physically located in one rack, this single ToR device cannot satisfy the requirements of a large infrastructure. Expanding the network to higher tiers of the network also means that traffic patterns begin to alter, and application run-to-completion time is impacted. Furthermore, add-on mechanisms are mounted onto the network to turn the lossy network into a lossless one. Another attribute of the traffic pattern of AI workloads is the uniformity of the traffic flows from the perspective of the packet header. This means that the different packets of the same flow, will be identified by the data plane as the same traffic and be carried in the exact same path regardless of the network’s congestion situation, leaving parts of the Clos topology poorly utilized while other parts can be overloaded to a level of traffic loss.
Proprietary Locked Solutions:
Additional solutions in this field are implemented as a dedicated interconnect for a specific array of servers. This is more common in the scientific domain of heavy compute workloads, such as research labs, national institutes, and universities. As proprietary solutions, they force
the customer into one interconnect provider that serves the entire server array starting from the server itself and ending on all other servers in the array.
The nature of this industry is such where a one-time budget is allocated to build a “super-computer” which means that the resulting compute array is not expected to further grow but only be replaced or surmounted by a newer model. This makes the vendor-lock of choosing a proprietary interconnect solution more tolerable.
On the plus side of such solutions, they perform very well, and you can find examples on the top of the world’s strongest supercomputers list which use solutions from HPE (Slingshot), Intel (Omni-Path), Nvidia (InfiniBand) and more.
Perspective from Silicon Vendors:
DSF like solutions have been presented in the last OCP global summit back in October-2022 as part of the networking project discussions. Both Broadcom and Cisco (separately) have made claims of superior silicon implementation with improved power consumption or a superior implementation of a Virtual Output Queueing (VOQ) mechanism.
Conclusions:
There are differences between AI and HPC workloads and the required network for each.
While the HPC market finds proprietary implementations of interconnect solutions acceptable for building secluded supercomputers for specific uses, the AI market requires solutions that allow more flexibility in their deployment and vendor selection. This boils down to Ethernet based solutions of various types.
Chassis and standalone Ethernet based solutions provide reasonable solutions up to the scale of a single machine but fail to efficiently scale beyond a single interconnect machine and keep the required performance to satisfy the running workloads.
A distributed fabric solution presents a standard solution that matches the forecasted industry need both in terms of scale and in terms of performance. Different silicon implementations that can construct a DSF are available. They differ slightly but all show substantial benefits vs. chassis or standard ethernet solutions.
This paper does not cover the different silicon types implementing the DSF architecture but only the alignment of DSF attributes to the requirements from interconnect solutions built to run AI workloads and the advantages of DSF vs. other solutions which are predominant in this space.
–>Please post a comment in the box below this article if you have any questions or requests for clarification for what we’ve presented here and in part I.
References:
Using a distributed synchronized fabric for parallel computing workloads- Part I
Allied Market Research: Global AI in telecom market forecast to reach $38.8 by 2031 with CAGR of 41.4% (from 2022 to 2031)
Executive Summary:
Artificial Intelligence (AI) in telecom uses software and algorithms to estimate human perception in order to analyze big data such as data consumption, call record, and use of the application to improve the customer experience. Also, AI helps telecommunication operators to detect flaws in the network, network security, network optimization & offer virtual assistance. Moreover, AI enables the telecom industry to extract insights from their vast data sets and made it easier to manage the daily business and resolve issues more efficiently and also provide improved customer service and satisfaction.
The growing adoption of AI solutions in various telecom applications is driving market growth. The rising number of AI-enabled smartphones with a number of features such as image recognition, robust security, voice recognition and many as compared to traditional phones is boosting the growth of AI in the telecommunication market. Furthermore, to cater to complex processes or telecom services, AI provides a simpler and easier interface in telecommunication. In addition, growing Over-The-Top (OTT) services, such as video streaming, have transformed the dissemination and consumption of audio and video content. With more consumers turning to OTT services, consumer demand for bandwidth has grown considerably. Carrying such ever-growing traffic from OTT services leads to high operational Expenditure (OpEx) for the telecommunication industry. Hence, AI helps the telecom industry to reduce operational costs by minimizing the human intervention needed for network configuration and maintenance. However, the major restraint of the AI in telecommunication market is the incompatibility between telecommunication systems and AI technology. Contrarily, the increasing penetration of AI-enabled smartphones in the telecommunication industry, and the advent of 5G technology in smartphones are expected to provide major growth opportunities for the growth of the market. Since advancements such as 5G technology in mobile and the rising need to monitor content on the tale communication network to eliminate human error from telecommunication are driving the growth of the market. For an instance, the Chinese government trying to improve its network services and telecommunication services; hence China Telecom Corporation has started a new 5G base station in Lanzhou city. Therefore, these factors are expected to provide numerous opportunities for the expansion of the AI in telecommunication market during the forecast period.
Allied Market Research published a report, titled, “AI in Telecommunication Market by Component (Solution, Service), by Deployment Model (On-Premise, Cloud), by Technology (Machine Learning, Natural Language Processing (NLP), Data Analytics, Others), by Application (Customer Analytics, Network Security, Network Optimization, Self-Diagnostics, Virtual Assistance, Others): Global Opportunity Analysis and Industry Forecast, 2021-2031.”
According to the report, the global AI in telecommunication industry generated $1.2 billion in 2021, and is estimated to reach $38.8 by 2031, witnessing a CAGR of 41.4% from 2022 to 2031. The report offers a detailed analysis of changing market trends, top segments, key investment pockets, value chain, regional landscape, and competitive scenario.
Drivers, Restraints, and Opportunities:
Growing adoption of AI solutions in various telecom applications, the ability of AI to provide a simpler and easier interface in telecommunication and reduce the human intervention needed for network configuration and maintenance, and the growing demand for high bandwidth with more consumers turning to OTT services drive the growth of the global AI in telecommunication market. However, the incompatibility between telecommunication systems and AI technology hampers the global market growth. On the other hand, the increasing penetration of AI-enabled smartphones in the telecommunication industry, and the advent of 5G technology in smartphones likely to create potential opportunities for growth of the global market in the coming years.
Covid-19 Scenario:
- The global artificial intelligence in telecommunication market saw a stable growth during the COVID-19 pandemic, owing to the increasing digital penetration and rise in automation.
- Moreover, the pandemic led the telecommunications infrastructure to keep businesses, governments, and communities connected and operational. The social and financial disruption caused by the pandemic forced people to depend on technology such as AI for information and remote working.
- AI also helped the telecom industry to reinvent customer relationships by identifying personalized needs and engaging with customers through hyper-personalized one-to-one contacts. It also helped configure fixed-line and mobile-network bundles that combine VPN, teleconferencing, and productivity apps.
The solution segment to dominate in terms of revenue during the forecast period:
Based on component, the solution segment was the largest market in 2021, contributing to more than two-thirds of the global AI in telecommunication market, and is expected to maintain its leadership status during the forecast period. This is due to the adoption of solutions by various end users for the automated processes. On the other hand, the service segment is projected to witness the fastest CAGR of 44.9% from 2022 to 2031, due to surge in the adoption of managed and professional services.
The on-premise segment to garner the largest revenue during the forecast period:
Based on deployment model, the on-premise segment held the largest market share of nearly three-fifths of the global AI in telecommunication market in 2021 and is expected to maintain its dominance during the forecast period. This is because it provides added security of data. The cloud segment, however, is projected to witness the largest CAGR of 43.8% from 2022 to 2031, as cloud provides flexibility, scalability, complete visibility, and efficiency to all processes.
The machine learning segment to exhibit a progressive revenue growth during the forecast period:
Based on technology, the machine learning segment held the largest market share of more than two-fifths of the global AI in telecommunication market in 2021, and would maintain its dominance during the forecast period. This is because machine learning algorithms are designed to keep improving accuracy and efficiency. The data analytics segment, however, is projected to witness the largest CAGR of 46.1% from 2022 to 2031, as it helps telecom companies to increase profitability by optimizing network usage and services.
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Asia-Pacific to maintain its leadership in terms of revenue by 2031:
Based on region, North America was the largest market in 2021, capturing more than one-third of the global AI in telecommunication market. The growth in the region can be attributed to the infrastructure development and technology adoption in countries like the U.S. and Canada. However, the market in Asia-Pacific is expected to lead in terms of revenue and manifest the fastest CAGR of 45.7% during the forecast period, owing to the growing digital and economic transformation of the region.
Leading Market Players:
- Intel Corporation
- Nuance Communications, Inc.
- AT&T
- Infosys Limited
- ZTE Corporation
- IBM Corporation
- Google LLC
- Microsoft
- Salesforce, Inc.
- Cisco Systems, Inc.
The report analyzes these key players of the global AI in telecommunication market. These players have adopted various strategies such as expansion, new product launches, partnerships, and others to increase their market penetration and strengthen their position in the industry. The report is helpful in determining the business performance, operating segments, product portfolio, and developments by every market player.
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https://www.alliedmarketresearch.com/request-sample/9717
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References:
https://www.alliedmarketresearch.com/ai-in-telecommunication-market-A09352
Global AI in Telecommunication Market at CAGR ~ 40% through 2026 – 2027
The case for and against AI in telecommunications; record quarter for AI venture funding and M&A deals
Emerging AI Trends In The Telecom Industry
The case for and against AI in telecommunications; record quarter for AI venture funding and M&A deals
Many pundits believe that telcos will need AI driven solutions. Some of the benefits: enable telcos to configure new offers and products in hours and days, fail fast/ learn fast when 5G applications don’t gain market traction, service customers more effectively and radically simplify their operations.
An AI-powered “decisioning engine” might help telcos take the correct action during every interaction in real time with customers, suppliers, and partners.
Proponents say that with AI-driven capabilities in place, telcos can:
Grow revenue through upsell and cross-sell of services: Telecom Providers (aka telcos or network operators) can increase average revenue per user (ARPU) by anticipating customer needs using real-time context, so they can make the right offer on the right channel when it is needed.
Accelerate subscriber growth: Net subscriber additions are critical to success. Key telecom industry partners can build customer interest in preferred channels, guide prospects to find the right bundle, and delight them with a flawless omni-channel experience.
Proactive digital customer service: By combining AI-driven decisioning with end-to-end automation, telcos can deliver proactive, personalized service across channels. This might give customers and agents a guided, intuitive experience that delivers the best outcomes for everyone seamlessly.
Resolve billing enquiries: To avoid costly calls to service centers and keep customers happy, telcos need to stay one step ahead. AI driven capabilities such as real-time monitoring and pattern detection can enable them to sense a potential billing issue, then send a proactive notification to the customer.
Guided service setup: In order to make a great first impression and reduce calls to the service center, AI can drive a self-serve guided setup for services like internet connectivity to make customers’ experience easy and frictionless. Step-by step visual instructions can help to get set up successfully, and troubleshooting tips allow customers to easily navigate challenges along the way.
Intelligent automation: To increase network capacity, efficiently deploy new 5G and fiber networks, or simplify order fulfillment, telecoms providers can use AI in combination with robotics and end-to-end automation to streamline and digitize complex operations, keeping margins high and bringing value to customers fast. With intelligent automation and robotics, telecoms can:
Orchestrate, automate, and deliver customer orders: With a better connection between front and back offices, partners, and customers across all channels, telcos can optimize operations, reduce costs and boost customer satisfaction.
Build and deploy new networks faster: Telecoms providers can accelerate fiber and 5G mobile network rollout with intelligent automation. Case management, robotics, and low-code development capabilities can help them build out critical infrastructure more efficiently and faster at lower cost.
Automatically resolve network outages and events: Telcos can provide end-to-end visibility of complex processes and analyze live data related to business rules, costs, and other criteria. The most effective delivery methods, equipment, vendors, or contractors can be selected to address and resolve problems.
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However, the AI cheerleaders never talk about the shortcomings of cyclically ultra hyped AI technology. We call attention to the cover story on this month’s IEEE Spectrum (the flagship publication of IEEE). “Why is AI so Dumb?” Here’s an excerpt:
AI has suffered numerous, sometimes deadly, failures. And the increasing ubiquity of AI means that failures can affect not just individuals but millions of people. Increasingly, the AI community is cataloging these failures with an eye toward monitoring the risks they may pose.
“There tends to be very little information for users to understand how these systems work and what it means to them,” says Charlie Pownall, founder of the AI, Algorithmic and Automation Incident & Controversy Repository.
“I think this directly impacts trust and confidence in these systems. There are lots of possible reasons why organizations are reluctant to get into the nitty-gritty of what exactly happened in an AI incident or controversy, not the least being potential legal exposure, but if looked at through the lens of trustworthiness, it’s in their best interest to do so.”
Part of the problem is that the neural network technology that drives many AI systems can break down in ways that remain a mystery to researchers.
“It’s unpredictable which problems artificial intelligence will be good at, because we don’t understand intelligence itself very well,” says computer scientist Dan Hendrycks at the University of California, Berkeley.
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CB Insights: What you need to know about AI venture in Q3-2021:
- New record: $17.9B in global funding for AI startups across 841 deals in Q3-2021. This marks an 8% increase in funding and 43% increase in deals QoQ.
- At $50B, 2021 YTD funding has already surpassed 2020 levels by 55%. 75% Growth in megarounds YTD.
- The number of $100M+ mega-rounds has reached a record-high 138 in 2021 YTD.
- There were 45+ mega-deals in each of the first 3 quarters in 2021 — the highest quarterly numbers ever.
- 100+ AI acquisitions. Quarterly M&A deals have surpassed 100 for 2 consecutive quarters, putting total M&A exits at a record 253 in 2021 YTD.
- Annual IPOs and SPACs are also up this year. In Q3-2021, there were 3 SPACs and 8 IPOs.
- The largest M&A deal of Q3-2021 was PayPal’s acquisition of buy now, pay later startup Paidy for $2.7B — 370% bigger than the next largest deal. Paidy uses machine learning to determine consumer creditworthiness and underwrite transactions instantly.
- 43% QoQ increase in median US deal size. In Q3-2021, global markets saw strong QoQ growth in the median size of funding rounds: 43% in the US, 64% in Asia, and 67% in Europe.
- Across regions, median deal size was $7M, while average deal size reached a record $33M.
References:
https://telecoms.com/opinion/how-is-ai-reshaping-telecoms/
https://spectrum.ieee.org/files/11920/10_Spectrum_2021.pdf
https://www.cbinsights.com/research/report/ai-trends-q3-2021/
Global AI in Telecommunication Market at CAGR ~ 40% through 2026 – 2027
The Global AI in Telecommunication Market [1.] is estimated to be $1.2 Billion (B) in 2021 and is expected to reach $6.3B by 2026, growing at a CAGR of 38%, according to a report by Research and Markets.
For comparison, Valuates says the global AI In Telecommunication market size is projected to reach $14.99B by 2027, from $11.89B in 2020, at a CAGR of 42.6% during 2021-2027.
Note 1. Artificial Intelligence in Telecom includes handling large volumes of data using machine learning and analytics, automating detection and correction of failures in transmission, automating customer care services, and complementing Internet of Things(IoT), e-mail, voice call, and database storage services.
Key factors of AI in telecom include the deployment of 5G mobile networks, growing demand for effective and efficient network management solutions have been driving AI in telecommunications market growth. Increasing AI-embedded smartphones and the growing adoption of AI solutions in various telecom applications are likely to further drive market growth.
Market Drivers:
- Increasing Adoption of AI for Various Applications in the Telecommunication Industry
- AI Can Be the Key to Self-Driving Telecommunication Networks
- Increased Need for Monitoring the Content Spread on Telecommunication Networks
- Growing Demand for Effective and Efficient Network Management Solutions
Telecom vendors commonly use AI for customer service applications, such as chatbots and virtual assistants, to address many support requests for installation, maintenance, and troubleshooting. To improve customer experience, telecom operators are adopting AI.
Other common uses of AI in Telecom include:
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Predictive maintenance
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Network optimization
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Fraud detection and prevention
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Robotic process automation (RPA)
Opportunities include:
- Cloud-Based AI Offerings in the Telecommunication Industry
- Utilization of AI-Enabled Smartphones
Conversely, incompatibility between telecommunication systems and AI technology, which leads to integration complexity in these solutions, is the major constraint for market growth. Also, the lack of skilled expertise and privacy & identity concerns of individuals are some other factors hindering the market growth.
References:
https://techblog.comsoc.org/2021/08/26/emerging-ai-trends-in-the-telecom-industry/
Emerging AI Trends In The Telecom Industry
by Harikrishna Kundariya, CEO at eSparkbiz Technologies
Introduction:
Artificial Intelligence (AI) is a technology that has the potential to shape our future. Today, almost all business verticals are utilizing AI in one way or another. AI is a large field, and there are many things yet to be researched, but it’s definitely been ground-breaking for many industries. Daily new research findings are emerging. Most of these have shown how AI can help businesses improve operations and be more productive.
AI is a black box for some, whereas it is a portal to unlocking great potential for others. Most businesses have started adopting AI as much as they can. It is predicted that by the end of 2023, companies will spend $10.83 billion on AI and automation.
Considering AI’s involvement in every business sector, the telecom industry isn’t far behind. Telecom companies are doing their individual research on AI to improve their business models. Using AI, it is easier for telecom companies to make accurate decisions. Moreover, with the right predictions from AI systems, they can get an insight into their decisions before they implement them in real life. Using AI’s predictive capabilities, telecom companies can get an edge over their competition.
To sustain the competition, businesses try to adhere to market standards and trends. Trends justify the changes that are widespread and followed by everyone to gain some benefits.
Here are some trends that are up and coming in the telecom industry.
Improve telecom network maintenance:
Telecom network maintenance is essential. When a network goes down, it is not only the users who suffer, but the telecom company also suffers a more significant loss. Loss of network shows the company’s insincerity towards its services and lack of care for its customers. The business also suffers monetary losses due to network breakdown. If there is some significant fault, the company has to get it rectified quickly, and this is costly too.
Hence AI is being used to overcome this problem. With AI, telecom companies can quickly identify the point of failure. Most of the time in network maintenance is spent behind finding the first point where maintenance is needed. With the availability of AI, it has become easy. Moreover, telecom companies are also leveraging IoT, which is a great technology.
Companies are looking forward to developing context-aware AI systems. Such AI systems are brilliant and can identify their state quickly. These systems follow the observe-orient-decide-act model to make decisions.
Using AI, downtime can be minimized. Moreover, the maintenance work can be carried out quickly by benefitting from context-aware systems and IoT processes.
Many companies are carrying out network maintenance with the help of drones. Comarch is one such company that creates solutions for telecom network maintenance with the help of AI-enabled drones.
Optimize network performance:
Network performance is vital if you want to be in the market. No user prefers a slow network. If your telephone towers are weak, you’ll face difficulty in adding new customers as well as maintaining the current ones.
There are many solutions for optimizing network performance. With the advent of AI, telecom service providers are using AI to optimize their networks.
One of the most common ways in network optimization is to predict network traffic and usage based on past conditions. AI can find out trends based on past data. These trends can then be used to create strategies to serve customers in a better way.
Telecom service providers create intelligent AI and ML systems that can accurately predict network traffic for any region. The results generated from AI systems are pretty accurate, and companies use those to optimize network performance. Usage data for any area is freely available with the service providers, so they can use this data to benefit.
Network performance can be optimized by increasing a tower’s capacity and range during certain peak hours when the area has high usage. Also, it can be decreased at a later stage to accommodate lower traffic levels.
Using AI, network performance can be controlled just like a remote-controlled device. The service providers are loving this benefit; hence AI is being used extensively. Many companies like AT&T and other telecom leaders are using self-organizing network technologies. These technologies have AI at their base and can work effectively under heavy traffic conditions.
Taking network performance a step ahead, Intel and Capgemini have tied up hands to develop a one-of-a-kind solution. These companies are already working on increasing the 5G spectrum’s capacity. Their project macaroni aims to boost a customer’s network experience by using real-time predictive analytics. Using this AI solution, every cell phone tower can handle more traffic than before, ultimately resulting in better network performance under a heavy customer base.
Improve network security/authentication:
Security is a big concern in the telecom industry. Tower Hijacking, wiretapping, and call forwarding pose a severe risk to the telecom business. To secure the user’s data from theft and cyberattacks, telecom service providers are using new and unique techniques. Many of these techniques include AI at their base.
AI can be used to authenticate users and also provide security to towers. When users sign up for a new connection, the chances of fraud are highest. They can use fake addresses, proofs, images, and any other thing. Identifying these fake things manually is nearly impossible. Hence, telecom companies are using AI to authenticate new users.
AI systems are being trained to spot fake documents. There are specific characteristics of fake documents that are well known. AI systems are trained to identify such characteristics on documents. When they reach a certain confidence level, they are used in everyday authentication work to ensure that no imposter is served.
Towers can be secured by using preventive AI technologies. These models are trained to look for defects in the towers every now and then. Sometimes the systems try to attack the towers to test the security procedure’s working. Using AI, it is easy for telecom companies to find towers in need of security. Such towers can be found by constantly monitoring and reporting if even a slight change is found in the tower’s characteristics.
End-user data protection is important because today, hackers are more active than ever before. Moreover, hackers are targeting places like telephone company’s databases where they can get a lot of personally identifiable data easily.
Many telecom service providers in the US are already using Cujo.ai’s network security solutions. Companies like Verizon, AT&T, and Charter communications rely on AI services from cujo.ai to secure their networks.
Cujo.ai has a unique offering named Sentry that can process large datasets in seconds. This AI system is well developed and it can make its own decisions regarding whether there is a security issue or not. Moreover, these systems are trained heavily with real-world data, so they can easily detect and take actions on unauthorized actions over a telecom network.
When AI is leveraged, the need for better standards increases. Hence, many telecom service providers use end-to-end encryption and other newly created security protocols and encryption standards. With suitable security systems, the data is fully secure and free from any interference.
Conclusions:
There are many trends that are seen within the telecom industry, and AI constitutes the majority of them. The telecom industry is being modernized at a large scale, and so they are trying to include AI as much as possible in their business models. Above, you’ve seen the three major trends seen in the telecom industry. These are the ones that are now becoming benchmarks for the telecom industry.
References:
https://www.esparkinfo.com/our-team.html
https://techblog.comsoc.org/2019/06/10/cisco-announces-ai-ml-and-security-software/
https://techblog.comsoc.org/2017/04/17/verizon-china-telecom-huawei-et-al-form-etsi-ai-group/
About Harikrishna Kundariya:
Mr. Harikrishna Kundariya is a serial entrepreneur leading eSparkBiz since 2010. Under his leadership the company has built its reputation as an excelling offshore development company. He values building relationships with clients rather than just focusing on the business at hand.