5G
Article | May 18, 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.
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [{
"@type": "Question",
"name": "How Can ML Help in Making Networking Smarter?",
"acceptedAnswer": {
"@type": "Answer",
"text": "ML can streamline the network by automation, threat detection, and improving its performance."
}
},{
"@type": "Question",
"name": "How Complex Is Integrating ML into Networking?",
"acceptedAnswer": {
"@type": "Answer",
"text": "The complexity depends on the type of network you are integrating it into."
}
},{
"@type": "Question",
"name": "What to Keep in Mind Before Using Ml in Networking?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Consider investment costs, data availability, feasibility, and scalability."
}
}]
}
Read More
Network Management, Network Security
Article | July 17, 2023
The next-generation of wireless technologies – known as 5G – is here. Not only is it expected to offer network speeds that are up to 100 times faster than 4G LTE and reduce latency to nearly zero, it will allow networks to handle 100 times the number of connected devices, revolutionizing business and consumer connectivity and enabling the “Internet of Things.” Leading policymakers – federal regulators and legislators – are making it a top priority to ensure that the wireless industry has the tools it needs to maintain U.S. leadership in commercial 5G deployments. This blog provides monthly updates on FCC actions and Congressional efforts to win the race to 5G.
Read More
Enterprise Mobility
Article | June 15, 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.
Read More
Data Center Networking
Article | July 5, 2023
Your patients have grown to trust your expertise and recommendations in matters regarding their healthcare. As the sector transitions into a more digital playing field, uninterrupted network connectivity is more than just a bonus; it’s a necessity.
While there are many different challenges to completely integrating your practice into the digital world, internet outages are the costliest. Downtime can be caused by various factors, which can compromise patient safety, the faith your team instills in you, and your practice’s reputation and revenue. However, investing in the means to maintain a resilient network lets you maximize your network uptime to optimize resources.
We'll look at four different strategies and their benefits for your infrastructure so you can focus on what you do best: providing healthcare excellence to your patients.
Strengthening Network Infrastructure
The traditional way of doing things may be great for your remedies and techniques. Still, with a growing number of patients and their contextually relevant demands, your network needs to be able to accommodate many different booking requests, increase user activity on your server, and store sensitive patient information.
High-speed internet connections enhance your network performance and let you, your team, and your patients make the most of your uninterrupted uptime. Fiber-optic networks, when combined with load balancing and proper segmentation, can diffuse and direct network traffic efficiency and prevent congestion, which prevents downtime due to overload.
Implementing Network Monitoring and Management Tools
Much like your patients visit your practice to ensure everything is all right with the current state of their health, your network must also receive the same treatment. Identifying and pre-emptively resolving potential issues and vulnerabilities will prevent much more destructive or expensive problems from occurring.
Use real-time tools to monitor your bandwidth usage and gain visibility of potential bottlenecks. Tools that offer risk monitoring deliver alerts about critical events that pose a threat to your business continuity. Your IT team will be better equipped to troubleshoot issues promptly and optimize performance.
Conducting Regular Network Assessments and Audits
Once you have the proper monitoring tools to manage your network topology better, proactive troubleshooting is a great way to spot-check whether your current solution is working as it should. A network audit is much like proactive troubleshooting; you are looking to see if anything could harm the overall system and catch it before it can develop.
When auditing a network, the primary focus should be security measures. If patient and confidential data is not secure, the smooth operations of your business are the least of your worries. When conducting an audit, consulting with a network service provider will help identify issues with your protocols, data encryption, and firewall configuration.
Establishing Redundancy and Disaster Recovery Plans
Backing up private and confidential data is crucial to ensuring that sensitive information is not lost or exposed. Minimizing network downtime can often be achieved by having backup systems that will keep running in the event of an attack or outage. For example, a dedicated Cloud Access Network, power supplies, and switches will go a long way.
When creating an internet contingency plan, outline steps and protocols with your team that you will take in the event of a complete failure, including things such as brand reputation management, customer service, and data loss prevention.
Looking Forward
As the lines between in-person and digital are blurred, navigating the complexities of implementing a robust network is paramount to your business.
Strengthening your infrastructure, integrating redundant systems, and conducting regular audits and assessments with the proper monitoring and management tools will help you maximize uptime usage and minimize network downtime.
Although overwhelming, working with a reputable network service provider can help you embrace your network topology to remain competitive.
Read More