How Can Machine Learning Streamline Networking?

Apurva Pathak | November 22, 2021 | 37 views

How Can Machine Learning Streamline Networking

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.

Spotlight

IDDI Computers Sarl

Based in Geneva and Lausanne, Iddi Informatique Sàrl specializes in the management and maintenance of computer networks.

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DISH and T-Mobile Expand Network Services Partnership

DISH Network Corporation | June 27, 2022

DISH Network and T-Mobile signed an amendment to the 2020 Master Network Services Agreement (MNSA) that provides customers of DISH's retail wireless brands, including Boost Mobile, access to T-Mobile's nationwide 5G network. The amendment, which will become effective upon approval by the United States Department of Justice, incorporates financial and operational changes, including improved pricing and enhanced roaming solutions for DISH 5G customers in consideration of an annual minimum revenue commitment through the remaining term of the MNSA. "We are pleased to have reached new terms with T-Mobile that provide DISH with the ability to be more competitive and to meet our customers' evolving needs," said John Swieringa, president and COO, DISH Wireless. "DISH's 5G network now covers more than 20 percent of the U.S. population, and this amendment gives our customers enhanced access to nationwide coverage and in-market roaming while we continue to deploy our own 5G network." "T-Mobile is building a national, Ultra Capacity 5G network with unprecedented capacity, which has put us in a unique position to support partners like DISH, While DISH customers will benefit from our network, this deal also locks in a multi-billion dollar revenue commitment for our business. It's a win-win." Mike Katz, chief marketing officer, T-Mobile DISH is committed to providing competition in the wireless market as the nation's fourth facilities-based carrier and the company will continue to expand coverage of DISH's 5G network. About DISH Network: DISH Network Corporation is a connectivity company. Since 1980, it has served as a disruptive force, driving innovation and value on behalf of consumers. Through its subsidiaries, the company provides television entertainment and award-winning technology to millions of customers with its satellite DISH TV and streaming SLING TV services. In 2020, the company became a nationwide U.S. wireless carrier through the acquisition of Boost Mobile. DISH continues to innovate in wireless, building the nation's first virtualized, O-RAN 5G broadband network. DISH Network Corporation (NASDAQ: DISH) is a Fortune 200 company. About T-Mobile: T-Mobile U.S. Inc. is America's supercharged Un-carrier, delivering an advanced 5G LTE and transformative nationwide 5G network that will offer reliable connectivity for all. T-Mobile's customers benefit from its unmatched combination of value and quality, unwavering obsession with offering them the best possible service experience and undisputable drive for disruption that create competition and innovation in wireless and beyond. Based in Bellevue, Wash., T-Mobile provides services through its subsidiaries and operates its flagship brands, T-Mobile, Metro by T-Mobile and Sprint.

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