Enterprise Mobility
Article | June 15, 2023
Applications of AI/ML
Modern businesses are adopting Artificial Intelligence (AI) that encompasses disciplines like machine learning (ML), natural language processing (NLP), evolutionary computation, etc., to increase their productivity and management capabilities.
Companies like Qualcomm are using AI and machine learning to improve their customer experience.
“Across many industries, we are currently experiencing the creation of intelligent machines that is using AI to simulate smart behavior.”
-Dr. Vinesh Sukumar, Senior Director- Head of AI/ML Product Management at Qualcomm, ( in an interview with Media7)
The application of machine learning in networking is swiftly taking shape. However, as the problems in modern computer networks are getting tedious to handle, AI tools are being introduced to hard-carry their smooth functioning.
Let’s take a look at how network complexity impacts businesses:
Difference in Network Parameters
Different client devices like laptops, smartphones, CCTV cameras, etc., are connected to a single network. However, their requirements and parameters are different. Therefore, the IT team of the business needs to meet them without compromising the functionality and security of the network.
Users Prefer Wireless Networks
Wireless networks are more complex than wired ones. They perform dynamically depending on the number of users, applications, and other variables.
Impact of Cloud Computing
Most applications are now cloud-based, and such a network has multiple data entry points and requires more support.
User Experience
Deciphering root cause analysis, finding correlation and solutions becomes tedious without an AI/ML model. Complex patterns remain unanalyzed, and this creates a vacuum between the customer and the business.
What Does ML Bring to the Table?
Machine learning applications in networking correlate to solving four types of network problems: clustering, extraction, regression, and classification.
For classification and regression, ML clusters similar data and creates a gap between data groups. It then successfully maps a new set of data to a pre-set continuously valued output. As for extraction, it easily establishes a statistical relationship between the data it analyzes.
Machine learning applications in networking encompass the following:
Automation and Cognitive Computing
ML enables automation in data processing by eliminating the human error factor and constantly improving with time. It analyzes data, improves the productivity, security, and health of the network. Cognitive computing allows processing diverse data sets, detecting and finding root causes and common traits within the system.
Network Monitoring & Security
Network monitoring is used to solve problems in a large dataset by deciphering the hidden pattern in the data. It then predicts the outcome for clustered data, malware attacks, or impending network failure. It recognizes impending threats in time and sends out warnings. ML uses anomaly-based intrusion, misuse-based intrusion, or hybrid intrusion to prevent misuse, modification, unauthorized access, or malfunction.
Traffic Prediction, Classification, and Routing
Network traffic prediction is important to handle any mishaps proactively. Network analysis in machine learning is done by using Time Series Forecasting (TSF). By using a regression model solution, TSF finds a correlation between the traffic volume in the future and the traffic previously observed.
Traffic classification ensures Quality of Service (QoS), planning ahead for capacity, security, performance analysis, etc. It helps with proper resource utilization by pinpointing unnecessary traffic in a critical application.
Factors like cost-effectiveness, link utilization, operational capabilities, and policies are also considered by the ML model.
Congestion Control
ML models control the number of packets that enter a network to ensure that the network is stable, fairly utilize resources, and follow queue management employed for congestion control.
Efficiently Managing Resources
ML efficiently manages network resources like the CPU, frequency, switches, memory, routers, etc., by using analytical decision-making.
ML Learning Curve
ML models learn in the following ways:
Pitfalls
Like any other technology, machine learning application in networking comes with pitfalls and limitations. Here are a few:
Data Quality
The efficiency of an ML model is based on the quality, quantity, and diversity of data it processes so it can deduce patterns or identify root causes. Most ML models use simplistic synthetic data for training, validation, and performance. The same cannot be said about practical settings because the data comes from different applications and services and is more complex.
Feasibility
There are scalability and feasibility issues because each network and application is different. Moreover, there are no set standards for uniformity for implementation which makes it hard to set benchmarks or best practices. Control over autonomic networks is distributed and remains limited based on the vendor’s specific devices.
Predictive Analysis and Its Cost
Network analysis and machine learning prediction require additional accurate and effective monitoring investments. Moreover, fault management may have some potholes as there may be a scarcity of normal fault data.
High FPR (False Positive Rates)
Anomaly detection by ML in networking has not created enough buzz in the industry because it generates high FPRs during operations. Also, no detailed anomaly report is generated, so no anomaly history log can be maintained.
Striking a Balance
ML requires time to learn and mitigate issues. It is difficult to identify, in advance, how complex the ML’s approach will be. Striking a balance between the performance and computational cost is difficult. Deciphering comprehensive evaluation metrics is also a tedious task.
No Theoretical Model
There is no theoretical model, in turn, a unified theory, for ML in networking, so each network may have to be learned separately. The current machine learning applications in networking are made keeping in mind certain applications. Over time, more research to tailor ML for certain networks needs to be done. Cross-domain experts who understand both ML and networking are also rare.
Solutions
Software Defined Networking (SDN)
CISCO helped PwC Italy set up a secure network at their new twenty-eight-floor tower with the help of their SD-Access product. PwC wanted a secure, robust network with increased Wi-Fi and wired connectivity for their 3000 employees by streamlining network operations.
“We needed a robust and highly reliable wireless network infrastructure that’s as advanced as the tower itself.”
-Simone Demaria,Network Architect and Infrastructure Manager at PwC Italy
By applying Software Defined Network (SDN), IT personnel can remotely govern network policies in real-time through open interfaces, so traffic engineering is easily possible. SDN also contributes to network virtualization.
SDN supports the upcoming 5G ecosystem. When combined with NFV and VNF, SDN can revolutionize networking.
Going Beyond Traffic Volume & Prediction
To tackle the limitations that TSF-based traffic prediction models have, leveraging features beyond traffic prediction and concentrating on traffic interpolation and sampling could be viable. Research is ongoing on this possibility.
Summing It Up
As the influx of data keeps on increasing, the complexity of networks will increase in tandem. For successfully implementing ML for streamlining networking, the ML approaches we are aware of today need to be upgraded to accommodate multi-layer networks and multi-tenancy so autonomic networking can be a reality.
FAQs
How Can ML Help in Making Networking Smarter?
ML can streamline the network by automation, threat detection, and improving its performance.
How Complex Is Integrating ML into Networking?
The complexity depends on the type of network you are integrating it into.
What to Keep in Mind Before Using Ml in Networking?
Consider investment costs, data availability, feasibility, and scalability.
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5G
Article | September 28, 2023
Before the COVID-19 crisis, the biggest news in tech was the ongoing -- and controversial -- rollout of the 5G network. First, there was the ban on Chinese companies, prohibiting them from being involved with 5G infrastructure in the U.S., U.K. and Australia. Then articles started pointing out that the threat profile for 5G was an order of magnitude higher than that of existing telecom protocols. The coronavirus outbreak, though, has forced some analysts to reassess the value of 5G. While security concerns remain, the network has been invaluable in the fight against the pandemic.
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Unified Communications, Network Security
Article | July 10, 2023
Asia stands out as home to a handful of telcos busy building an international business out of selling their internally developed IT platforms. Leading the way are Jio in India, Japan’s Rakuten and Singapore operator Singtel.
Having built their own businesses, they are now selling their platforms to support new 5G business models for enterprises and other operators. In the case of Singtel, this means its 5G multi-access edge computing (MEC) services, based on Paragon, its orchestration platform for enterprise services.
Manoj Prasanna Kumar, Head of Enterprise Platforms at Singtel, who is responsible for the Paragon platform, discusses in this article the company’s enterprise service ambitions, how it’s partnering with global enterprise software vendors and the obstacles it still sees to 5G B2B service uptake.
Paragon, which falls under the telco’s DigitalInfraCo arm, aims to give enterprises “a single pane of glass that provides an end-to-end view and control of the network, the edge and the application ecosystem,” says Manoj. “It opens up the edge to the enterprise world, allowing them to deploy either their own applications or applications from Singtel's ecosystem.”
Launched last year, Paragon also lets telcos orchestrate end-to-end 5G enterprise networking services in combination with applications from software and cloud computing partners. Paragon’s application partners include Amazon Web Services, Intel, Microsoft and SAP, and the platform is available to every 5G enterprise user within the Singtel Group.
Singtel’s bet is that a growing number of enterprises will need a tightly intertwined combination of 5G connectivity and cloud computing on the edge to run specific vertical applications.
“Our strategy is to become a super aggregator of MEC,” says Manoj. “We focus on high throughput, low latency use cases, such as video analytics or streaming, mixed reality and virtual reality which pump data into the back-end applications and where the decision-making cannot afford even a few milliseconds of extra latency.”
In addition to Paragon, Singtel Group’s investments in 5G infrastructure and service delivery include a national 5G standalone (SA) network, covering more than 95% of Singapore, and international investment in data centers to support cloud computing on the network edge. Today, there are signs that its investments in 5G enterprise services are starting to bear fruit. In the second half of the 2022/23 financial year, which ended on 31 March, Singtel reported that higher demand for technology solutions and 5G services contributed to ICT revenue growth of 11%, with ICT revenues contributing 23% of Singtel Group’s overall enterprise revenue.
Singtel scored a notable win for the Enterprise 5G offering powered by Paragon platform last year when Silicon manufacturer Micron said it would deploy it and Singtel’s 5G campus network infrastructure to support its smart manufacturing operations. Micron is using Singtel’s solution to help manage and analyze its manufacturing processes for enhanced efficiency. Likewise, Singtel recently announced Hyundai as another customer for their Enterprise 5G offering powered by the Paragon platform to deliver digital twin for their electric vehicle manufacturing plant in Singapore for advanced manufacturing operations.
Nonetheless, Manoj recognizes that challenges remain when it comes to growing the 5G enterprise business. “5G and edge in Singapore have had quite a good start. But I would say we've got a long way to go,” he says.
Convincing customers
One of the biggest obstacles is generating customer demand. After all, just because enterprises are able to set 5G connectivity parameters on demand or use MEC for 5G applications at the click of a button doesn’t mean they see a reason to do so.
“Many customers don't have a lot of awareness of how edge computing can really transform their business and how a few milliseconds of latency can actually save money for them, make them more efficient, and reduce errors and so on,” says Manoj.
This reality has shaped Singtel’s sales process. “We spend quite a lot of time in raising awareness amongst customers,” he explains. “We never start with what 5G can do. Instead, we focus on understanding their challenges, their current processes, what gaps there are, and…start with applications that can help solve their problems.”
Another challenge is a lack of 5G-native devices. “This puts us in a very tough spot because when we go and connect devices to wi-fi hotspots, and then use 5G as backhaul, customers often ask ‘isn't this similar to wi fi? Why do I need 5G?’” He adds: “It will be a bit of a roadblock…for all telcos until the 5G-native device ecosystem matures.”
There is also a need for software applications that can perform optimally on 5G and the edge, and switch between network slices with different payloads. “There is a little bit of hand holding required when we bring in an ISV to qualify their application so that it can benefit from all the capabilities of 5G and the edge,” says Manoj.
And then there are the engineering challenges associated with orchestration. Paragon sets out to automate much of the orchestration and management capabilities that make it possible to request quality of service on demand for specific applications and use cases. But here again, success is dependent on close partnerships with third parties.
“Strategic partnerships with Ericsson on the network side and with Intel, Microsoft and AWS help us boost the infrastructure and the application side to stitch together the network and the infrastructure capabilities,” explains Manoj.
Choosing your vertical
Singtel is currently targeting three strategic verticals: manufacturing, public safety and urban planning. Its choice reflects the opportunities in both Singapore and the domestic markets of members of the Singtel Group.
“In Singapore, we are lucky because both enterprises and the government are very, very future-looking and invest quite a lot in adopting new technology,” says Manoj. In particular, “public sector customers are more motivated to explore something new because they carry the digital footprint of the country,” he says.
And because governments operate public safety and urban planning systems at a national level, the promises are on enough scale to spur third parties to invest in developing devices and software applications. Typical public safety use cases include video analytics, surveillance systems and robotics applications; urban planning covers systems such as traffic management.
Some of the enterprise applications Singtel sees gaining traction include immersive B2B2C content, such as delivering real-time analytics to gamers via a 360-degree video feed or mixed reality applications to train factory workers on how to troubleshoot to use complex equipment. “If they need an augmented overlay of information through the camera feeds then they need 5G and edge because a lag will make users nauseous,” explains Manoj. Other promising use cases include autonomous drones and robots.
Singtel has drawn on standard APIs, including TM Forum’s Open APIs, CAMARA APIs to build Paragon. Manoj encourages both technology standardization and collaboration with hyperscalers and software vendors to grow the enterprise market.
“Telcos should be embracing tech players as partners, seeing them as catalysts of more pull through on their services,” says Manoj. “When you partner with them, you expose your services on the hyperscale infrastructure, you naturally work with developers, which allows telcos to expand the services market.”
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Wan Technologies
Article | August 23, 2022
In the United States, private networks saw the sun for the first time in the early 1970s by AT&T. The networks were majorly operated over telecommunication networks. In the 1990s, with the evolution of Internet technology, a new type of network known as virtual private networks emerged. This type of network was built on public infrastructure, and the data was encrypted to protect it from eavesdroppers.
Nowadays, networks developed by businesses are called Enterprise Private Networks (EPN) when privacy is maintained via security processes and tunneling protocols such as Layer 2 Tunneling Protocol.
The objective of an EPN is to provide high-speed internet access and data sharing within an organization. Businesses can utilize Wi-Fi in their workplaces to share internet access and resources. This type of private network also employs routers, switches, fiber optics, virtual devices, and modems. Security is also a significant factor while developing an enterprise network. Different firewalls are set at access points to ensure safe data transfer between systems.
Enterprise private networks can be built in various ways, including:
Virtual private network (VPN)
Local area network (LAN)
Wide area network (WAN)
Cloud-based networks
Let’s dive deeper into EPN.
Enterprise Private Network: Reasons to Deploy It Today!
Giving its intended objective, enterprise private networks can be considered to provide a variety of conceivable benefits to an organization.
Enhancing Network Security
A company must adhere to strict procedures to safeguard its security. Networks are vulnerable to cyberattacks, and some business data contains sensitive information that might be lost or acquired by the wrong people. However, data circulation is critical to long-term business operations, which is why incorporating an enterprise private network is a wonderful way to keep security issues at bay even while allowing the organization to more easily manage its critical operations. This is a key reason why most businesses do not hesitate to use EPN, regardless of how difficult it is to set up or the upfront costs associated with it.
Economizing Measures
Keeping overheads to a minimum is critical for achieving a convincing ROI at the end of the day. Most businesses take stringent steps to ensure that they remain profitable. Cutting major expenditures is a helpful technique to do this, and the deployment of EPN is a perfect example. Because important business data can be exchanged over the network where key stakeholders can access it, the expense of physically transferring data and resources from one place to another is reduced. Moreover, a significant amount of valuable time is saved since any type of data and resources can be uploaded across the network in a matter of minutes.
Centralization
Another important element that is essential to an organization's success is business continuity. Different departments of an organization are interconnected to one another through an enterprise private network and can effortlessly share resources with one another. As earlier stated, it enables time efficiency and allows companies to keep progressing toward gradually achieving their day-to-day demands.
Enterprise Private Network: A Catalyst to 5G Digital Transformation
Businesses now depend on technology that has undergone significant transformation in recent years. Organizations are increasingly relying on feature-rich apps to operate their operations and drive innovation. Connectivity is at the heart of keeping everything operating smoothly and effectively, and 5G is expected to provide even more pace and potential. 5G is enticing because its infrastructure differs from prior generations of cellular networks. The 3G and 4G networks were designed with hardware-based network operations in mind. 5G, on the other hand, is 'cloud-native,' with network functions stored in software as a Virtual Network Function (VNF) or Cloud-native Network Function (CNF). 5G has the ability to drive digital transformation for companies and organizations by providing faster connection speeds, reduced latency, higher capacity, and better security. Organizations can obtain significant business advantages in automation, security, and safety when 5G is used in combination with a private cellular network.
The Importance of 5G on Private Networks
Speed
5G provides better bandwidth than 4G LTE networks, which is critical for data-intensive applications.
Latency
Robotics, manufacturing, remotely operated systems, and vehicle-to-vehicle communications all need low latency.
Network Slicing
The 5G network core offers network slicing, allowing network operators to virtualize network traffic, often in a cloud-based flexible environment.
Increased Connection Density
5G will ultimately support 100 times more connected devices per square kilometer, up to one million devices.
Multi-Access Edge Computing (MEC)
5G Multi-Access Edge Computing (MEC) moves computational power closer to the network edge, reducing the time required to send data to a centralized data center.
Wi-Fi 6
While 5G has considerable benefits over Wi-Fi, the new Wi-Fi 6 version also has greater capacity, reduced latency, and faster speeds than its predecessor, Wi-Fi 5.
Industries Leveraging EPN to Enhance Their Capabilities
With the expanding digital transformation, business interest in private networks is growing. Enterprises can deploy such networks to explore a broad range of wireless use cases and provide access to areas that are not covered by a public network. These networks can also be customized to meet the needs of certain industries and businesses. With the arrival of 5G, private networks enabled by the technology are positioned to stimulate innovation and allow next-generation enterprise transformation across a wide range of industries. Industries leveraging 5G-enabled enterprise private networks are:
Healthcare
Healthcare tops the list of rapidly growing industries, requiring private networks. The unprecedented burden caused on healthcare systems worldwide by the COVID-19 pandemic has driven the need for improved connectivity and modernization of infrastructure, prompting hospitals to establish private networks.
Manufacturing
The manufacturing industry is undergoing a significant digital transformation, which is enabling various new use cases like automated manufacturing. In the industrial arena, private 5G networks play a critical role in increasing the density and efficiency of automation technologies like collaborative mobile robots, automated guided vehicles, AR predictive maintenance, and virtual reality remote devices.
Smart Facilities
By reducing the reliance on third-party wireless service providers, private 5G networks enable these establishments to build and install the infrastructure most suited to their digital transformation roadmap.
Logistics
Another high-potential use case for private 5G networks is the logistics industry. With increasing global e-commerce adoption, the continuous movement of products through all logistical checkpoints—including warehouses, ports, and distribution centers—must be monitored and linked through a diverse variety of corporate mobility devices.
Mining
Another industry with significant potential for private 5G networks is mining. Mine operators want dependable wireless connections in order to leverage digital technologies, but they are often unable to introduce wireless communications to underground locations while still meeting the essential connectivity demands of machinery and mobile employees in open-pit locations. Private networks, which have fewer access points than Wi-Fi, can overcome these difficulties by providing a stable and widespread internet connection to machines, vehicles, and workers throughout a mine. This leads to improved safety, increased production, and a lower carbon footprint.
Some of the other industries are Oil and gas, Education, Ports, Smart Cities, etc.
Rising Demand for Enterprise Private Network (EPN)
As per research analyst Leo Gergs from ABI Research, there are a couple of factors that are causing the surge in demand for private networks for enterprises. These factors are:
Rise in demand for automation and enterprise digitalization in every sector of the market, including industrial manufacturing, logistics, oil and gas, etc., because of COVID-19.
The private 5G network has arrived, bringing with it irresistible features and use cases for businesses from all industries.
Private networks depend on technology from both public carrier networks and business IT, bringing together two disciplines that had previously evolved in quite distinct directions. Industry digitalization, the convergence of telecom and IT, edge migration of cloud apps, and increased spectrum availability are all combined to set the scenario for exploding demand for private 5G. A private 5G network is an enterprise-specific network that offers communication connections to people or items belonging to a single company as well as unique services required for the enterprise's operations. Enterprises across sectors are crunching the math on private 5G, from factories to farms to hospitals to hotels.
According to ABI Research, heavy industrial verticals will increase demand for private network installations. Industrial manufacturing and energy production (including mining, oil and gas, and logistics) will contribute $32.38 billion in private network revenues by 2030, accounting for half of the $64 billion in total private network revenues. The need for private 5G networks is increasing as 5G arrives, allowing compelling business use cases and favorable legislative developments on spectrum availability for corporations. TBR projected that the market for private 5G networks would reach $7.5 billion by 2025, rising from $200 million in 2020.
Carving the Future
With every new cutting-edge technology comes a leap of faith. Businesses and industries can expedite their digital journeys by using 5G private networks to offer secure connections while gathering and managing huge amounts of business-critical data. Private 5G is not simply a new paradigm for network operators; it's also an incredible opportunity for public and private organizations to unleash efficiency, exploit real-time data, and boost revenue.
FAQ
How Does Enterprise Private Network Work?
An enterprise private network is a business computer network that allows business organizations with several offices to securely connect to each other through a network. The primary purpose of an enterprise private network is to share system resources.
How to Set Up Your Private 5G Network?
To build a private 5G network, businesses need to:
The first step is to get the spectrum right-to-use.
Acquire 5G equipment such as base stations, mini-towers, and small cells from network equipment or infrastructure providers.
Integrate equipment with edge devices like smartphones, routers, sensors, etc.
What Is the Cost of Building a 5G Network?
A modest tower and 5G cell site will cost between $30,000 and $50,000. If the wireless network is to function during a power failure, the cell site will also need commercial power and batteries.
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