5G
Article | May 18, 2023
Although the cause is as yet unknown, this breach is likely to have the same culprit as most large scale data breaches that have occurred in recent memory, through a simple misconfiguration of a server or shared repository. As the sheer size, scale, and footprint of global technology vendors like Microsoft, Facebook, Google, and so on continues to grow, so too does the opportunity for simple errors to make their way into some infrastructure configurations that can then be exploited.
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Network Infrastructure, Network Management
Article | July 27, 2023
In an increasingly digital world where every pillar of information is now online, lightning-fast connectivity, rock-solid reliability, and impenetrable security are transforming into essentials within the network industry. 5G transforms the connected ecosystem and pushes the boundaries of connectivity to lay the foundation of a faster, more secure, and sustainable future.
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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 | May 25, 2022
Advancements inconnectivity have fueled the rapid progress in digitalization. From 1G in the 1980s to 4G in the last decade, wireless connectivity has constantly contributed to the transformation of businesses and the global economy. Today's 5G and Wi-Fi 6 technologies provide exciting features that are critical to increasing corporate productivity and improving people's digital experiences.
When we refer to the 5G and Wi-Fi 6 revolutions, we're not talking about undisturbed movie streaming or faster downloads; we're talking about making sci-fi movie fiction a reality. Things you used to only see in movies, like robots doing chores, autonomous vehicles, smart cities, virtual reality gaming experiences, remote surgeries, telemedicine, automated assembly line production, augmented reality marketing strategies, and the way you shop, travel, work, and get medical consultations, will undergo a transformation beyond imagination, and 5G will make it happen.
5G VS Wi-Fi 6
5G and Wi-Fi 6 Carving the Future of Businesses Together
When it comes to addressing particular needs, both 5G and Wi-Fi 6 are competitive depending on the industry vertical business environment, operation, devices, and applications. While Wi-Fi will be the dominant technology for indoor operations, as well as non-critical applications and the usage of unlicensed spectrum, 5G cellular networks will be used for outdoor coverage, mission-critical applications, highly guarded settings, and the anticipation of various QoS features.
According to a survey conducted by Deloitte, the priorities of companies were 5G and Wi-Fi 6, the importance of which isonly anticipated to grow in the future years.
Adoption of Wi-Fi 6 and 5G is regarded as a strategic requirement, leading businesses into a new era of wireless connectivity. With the convergence of Wi-Fi and 5G, organizations can do business everywhere while being highly productive and providing the greatest user experience.
Businesses will attain the following primary goals by transitioning to this enhanced wireless 5G technology:
increased effectiveness
enhanced security
Taking advantage of the benefits of these two forces,
such as big data analytics, AI, and edge computing.
The overall objective of leveraging this deadly mix is to unlock the possibilities of other emerging technologies such as IoT, cloud, Edge computing, big data analytics, VR, AR, robots, and others. Together, 5G and Wi-Fi 6 operate as a revolutionary multiplier.
Closing Note
5G and Wi-Fi 6 are two separate technologies that can work in tandem. They share the following characteristics: low latency, faster data rates, increased capacity, and excellent performance. Even though 5G and Wi-Fi 6 complement each other's capabilities, the environment, sensitivity of the application, and business use cases will determine which is the best match.
A holistic approach of Wi-Fi 6 and 5G is the optimum method for developing a smart city that is entirely networked or offering powerful Internet connectivity for families and businesses. Both technologies are critical in today's world, and every breakthrough in connection, whether it's 5G or Wi-Fi 6, contributes to our society's overall growth and innovation.
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