Impact of 5G on 4G Services for Operators and NEMs

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? 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Enterprise Mobility, Mobile Infrastructure

Article | June 16, 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.

Unified Communications, Network Security

Article | July 10, 2023

There is a fundamental shift in demand for core network services from enterprise customers, away from MPLS and private leased circuits to fixed broadband, Ethernet VPN, and DIA. Omdia forecasts that through 2026, the increase in revenue from fixed broadband, Ethernet VPN and DIA will be insufficient to make up for the decline in revenue from MPLS and private leased circuits.

Article | February 12, 2021

With the emergence of new technologies, the networking field is transforming rapidly. The epicenter of networking has shifted to clouds from datacenters. Similarly, the focus of networking has also moved towards mobile devices. In the upcoming years, tech trends will hugely impact the way a business operates and bring the rise of Industry 4.0. Top Networking Tech Trends 1. 5G and WI-FI 6 Undoubtedly, the deployment of next-generation wireless networking will be around the corner. In the arena of mobile devices, 5G is set to rewrite the new technological possibilities. It will uncover the true power of augmented reality and IoT. On the other hand, the next journey of the WI-FI Standard - WI-FI 6 or 802.11ax will become the step for a non-stop innovative world. It will add density, flexibility, scalability, and efficiency for increasing the internet speed of multiple connected devices. That will in return improve the working capabilities of businesses. 2. SD-WAN As the name suggested, SD-WAN is the software-defined approach for managing WANs. It can lower operating costs while amplifying the usage of resources in multiple deployments. It increases the security level for applications and enables admin to use bandwidth efficiently. It will become the standard format for wide area networks and will help in connecting public cloud resources and branch offices. 3. Secure Access Service Edge (SASE) SASE is a new networking technology that converges functions of different security and network solutions into one global cloud service. It is an architectural alteration of networking and security that supports IT to offer prompt, holistic, and versatile service to the digital business. It amplifies the security postures, improves access performance, and diminishes operational complexity. It helps organizations to develop new products faster and respond to business needs or changes. 4. IoT/Edge Networking In comparison to traditional cloud computing, edge computing is the idea to bring data and computers much closer to the end-users. It reduces the need for long-distance communication among client and server, and lessen the cost of bandwidth. It will remain to achieve drift in companies while they decentralize their networks. 5. Automation in Networking Network automation is the process that automates security and network to maximize the functionality and efficiency of the network. It will help IT companies to deploy applications faster. It is set to take the digital transformation to the next step by automation of network and security operations. It reduces the risk of downtime and failure of the network while making the management faster, simpler, and easier. Connecting to Future Networking Based on the trends that will reshape the networking world, we are going to see a significant change in the tech landscape. 2021 will be transformative for every person around the world. Several long-held concepts and infrastructure will be replaced by new ones making the network a vital asset to the business. Besides, the organizations are ready to take advantage of them in a way that was never imagined before. For any question or concern, have an IT consultation from the experienced.