5G
Article | May 18, 2023
We are surrounded by acronyms and buzzwords in technology. SD-WAN is one that is often used in the industry nowadays.
Organizations embrace digital transformation to stay up with market developments, consumer needs, and competitiveness. Traditional network designs weren't meant to manage digital transformation workloads and complexity. Business-critical services are commonly spread over numerous clouds, compromising network performance, particularly at branch sites.
Smart network operations teams opt for SD-WAN. SD-WAN reduces overhead and improves network performance. Routing and hardware expenses are saved through SD-WAN solutions while allowing multi-cloud access. SD-WAN also reduces overhead and supports new digital apps and services. This new technology streamlines WAN administration and operation and brings corporate advantages.
Business Challenges that SD-WAN Addresses
There has been a dramatic increase in the pressure on the network as a result of digitalization. Businesses must now rely on a stable and secure network, which conventional router-based network topologies are incapable of providing. An SD-WAN solution assists businesses in addressing use cases in order to expedite digital transformation efforts, lower cybersecurity risks, and increase revenue.
Eases connectivity with far-flung factories and offices.
Effectively deploys new sites and minimizes network equipment sprawl.
Enhances the speed of file transfer and backups to disaster recovery facilities.
Helps in moving applications to the cloud and protecting cloud app. data using Secure Access Service Edge (SASE).
Safeguards IoT devices using a zero-trust network
Helps in complying with the cybersecurity framework of the National Institute of Standards and Technology (NIST).
Ways SD-WAN Can Help Businesses Boost their Bottom Line
Boosts Security
Digital transformation is a double-edged sword. It can increase consumer satisfaction and market reach, but can pose security threats. According to the U.S. State of Cybercrime study, 41% of respondents stated more cybersecurity occurrences in 2017. The good news is that many SD-WAN solutions provide built-in security. Most SD-WAN systems only offer basic firewall and VPN functionalities, requiring IT teams to add security to elastic and dynamic SD-WAN connections after the fact. SD-WAN solutions with NGFW, IPS, encryption, AV, and sandboxing can avoid data loss, downtime, regulatory violations, and legal liability.
Enables Cloud Usage
Cloud services are rapidly being used by businesses. The great news is that SD-WAN enables direct cloud access at the remote branch, removing backhauling traffic – which routes all cloud and branch office traffic through the data center – allowing workers to directly access cloud applications irrespective of location without burdening the core network with additional traffic to manage and secure. Furthermore, SD-WAN enhances cloud application performance by prioritizing vital business apps and allowing branches to interact directly with the Internet.
Reduces Costs
As businesses deploy a growing number of cloud-based services, the volume of data traveling across a WAN rises dramatically, driving up operational expenses. SD-WAN, thankfully, can minimize this cost by utilizing low-cost local Internet connectivity, offering direct cloud access, and lowering traffic via the backbone WAN. According to an IDC poll (prediction), over a quarter of survey respondents anticipate SD-WAN cost reductions of up to 39%, with the other two-thirds anticipating more modest savings of 5–19%.
Improves performance
Data transfer over a network isn't created equal. Fortunately, SD-WAN can be set up to prioritize business-critical traffic and real-time services such as Voice over Internet Protocol (VoIP) and then successfully guide it over the most efficient path. IT teams can help decrease packet loss and latency concerns by supporting important applications over dependable, high-performance connections, increasing employee productivity and morale. This is business-impacting performance.
Closing Note
Indeed, SD-WAN evolved and flourished in the data center over the first few years of development. However, the time has arrived to take it seriously as a tool for managing your wide area network. There are currently several vendors on the market, as well as several mature solutions to choose from. More significantly, the business cases for SD-WAN are expanding on a daily basis.
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Wireless, 5G
Article | May 18, 2023
Network as a Service (NaaS) is gaining ground due to shrinking network boundaries and fast technological evolution in response to changing market demands. NaaS brings with it networks, operations, and business architecture that are more agile and based on open standards.
Rather than the conventional upfront cost, Network as a Service technology delivers networking gear, software, and operational and maintenance services as an operational expenditure. NaaS, like other cloud services, is maintained by the service provider and offered for a set cost.
Why Do Businesses Today, Need Network as a Service (NaaS)?
Businesses have recognized the advantages of the cloud in moving away from conventional on-premises networks. The corporate network boundary has practically vanished, and NaaS is becoming a popular technology.
Offers Flexibility to Businesses
Businesses can obtain a better return and save money by employing utility models instead of large expenditures on hardware and network equipment.
Time for Innovations
NaaS provides innovations by staying up to date with updated software versions via license upgrades and can fulfill corporate demands to introduce new goods and services more quickly.
Minimizes Operational Risk
NaaS will reduce operational risk associated with artificial intelligence (AI) and/or machine learning (ML); businesses will be able to implement the most recent product features and services.
Top 3 Benefits of Network as a Service (NaaS)
Access from Anywhere
Depending on how a cloud-based network is setup, users should be able to access it from anywhere and on any device without employing a VPN, though this creates the need for strict access control.
A user should ideally just need a connection to the internet and login details.
Bundled with Security
NaaS enables a single supplier to provide both networking and security services such as firewalls. As an outcome, the network and network security are more deeply integrated.
Cost-effective
Purchasing cloud services rather than developing one's own services generally leads to cost savings: cloud users do not have to purchase and maintain equipment, and the vendor already has the servers necessary to provide the service.
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Network Management, Network Security
Article | July 17, 2023
The Verizon 5G Business Internet rollout that started in parts of Chicago, Houston and Los Angeles continues this month in 21 new markets with more on the way, the company announced Thursday. Verizon Business is marketing fixed-wireless connectivity as an alternative to cable for enterprise and small to midsize customers. In a press release, Tami Erwin, CEO of Verizon Business, said, "As 5G Business Internet scales into new cities, businesses of all sizes can gain access to the superfast speeds, low latency and next-gen applications enabled by 5G Ultra Wideband, with no throttling or data limits."
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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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