Unified Communications, Network Security
Article | July 10, 2023
One year ago, Tableau Foundation launched our Racial Justice Data Initiative out of a core belief that data can and should play a transformative role in the fight for racial equity and justice in the United States. We have forged new partnerships and deepened existing relationships with organizations committed to using data to raise awareness of inequities, point to solutions, and advocate for progress.
Just today, we announced two new partnerships that will deepen our commitment to advancing racial equity in the Southern US with—Southern Economic Advancement Project (SEAP) and Fair Count. We believe that data can be a powerful resource in both identifying the experiences and challenges of people whose voices we may not normally hear and help create the necessary programs, policies, and resources needed to ensure these same people are not left behind
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Wireless, 5G
Article | May 18, 2023
In today’s shop-from-anywhere world, the model for success continues to change. Tried and true techniques are becoming obsolete as consumer expectations evolve, demand volatility rises, and supply chain disruptions become more frequent. Retailers are also dealing with online shopping surges that add new complexities to existing data strategies due to an influx of raw, unprepped, and largely underutilized data.
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Enterprise Mobility, Mobile Infrastructure
Article | June 16, 2023
The cloud, robotics, automation, and digital technologies are indispensablefor efficient, adaptable, and dynamic business operations. Artificial intelligence and 5G have evolved to become two of the most revolutionary technologies of the decade. While 5G and AI are capable ofindependently revolutionizing industries and facilitating future experiences, combining the two will be ground-breaking.
The combination of AI with 5G mobile technology has the potential to transform business and society, paving the wayfor new products and services that were previouslyunimaginable.So, let’s check out how AI and 5G can revamp and upgrade businesses.
AI with 5G: Making Network and Devices Better
Using AI on 5G networks and devices will enhance wireless communication and battery, and most importantly, improve the user experience. With the help of machine learning, you can now focus on major wireless issues that are tough to tackle with traditional methods.
The wireless industry has been talking about the ways in which AI can improve 5G networks.AI will significantly impactthe fundamental aspects of 5G network management, including efficiency, deployment, service quality, and security.
One of the less-discussed aspects is how on-device AI will enhance the 5G end-to-end system. Radio frequency awareness (RFA) is at the center of 5G improvements and AI's involvement in the process.Instead of a hand-crafted algorithm, machine learning can decipher the device's RF signals. Improved radio awareness increases device experience, system performance, and radio security.
Embracing 5G for Future Telecom & Business Operations
The fifth generation of mobile technology comes with many use cases that are enough to completely transform almost every industry. As the world gets ready for a substantial transformation, it's important to know what they are and how they can help your business. Presently, 5G is driving three significant global trends.
5G technology will alter connected devices by driving consumer adoption, making them smarter, and making large-scale device integration easier.
Cloud and edge computing depend on accessibility, and 5G will make cloud and edge computing more powerful and accessible than ever before.
As 5G allows algorithms to be much more efficient at collecting and analyzing data at scale, AI becomes more accessible and fundamental for businesses powered by 5G. This can be considered a scientific and ethical endeavor.
PartingThoughts
Like any new technology, there is indeed a lot of hype around 5G's debut. 5G and AI are two synergistic, necessary components driving future advancements. Those whocombine these technologies will have a competitive edge and the opportunity to build future forward brands.Businesses that adopt 5G will not only witness revenue gain but will also emerge as an influential player in the future.
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Network Management
Article | November 22, 2021
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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