Machine learning meets 5G wireless

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

If you are clued into IT, then most likely, you are aware of the latest trending technology, edge computing data centers. Edge Computing ensures exceptional speed, with firm privacy and security compared to the conventional cloud methods, thus making edge data centers an imperative option for everyone. The world is undoubtedly moving faster, thereby perpetually pushing the power of next-generation innovation. Edge computing data center has emerged as a substitute to cloud computing, that keeps the data processing power at the “edge” of the network. But, it also comes with a set of challenges to the network. Edge computing devices that have processing functions are expensive and to operate the older version, additional equipment is required, which incurs extra expenditure. Despite the challenges, edge computing has turned out to be the biggest technology investment. So, let’s break it down here with comprehensive details to understand how this latest trending technology is all set to shape the future of the data center. A Brief on Edge Computing The word edge refers to the literal geographic distribution that brings computation and data storage nearer to the data sources. It improves the response duration and saves bandwidth as it runs fewer processes in the cloud and shifts those processes to local destinations such as on a user’s computer, an edge server, or an IoT for that matter. In a nutshell, edge computing is a topology that enables data to be analyzed, processed, and transferred at the edge of a network, It helps diminish the long-distance communication that takes place between a client and server. A significant advantage of using edge computing lies in its high speed and better reliability. In addition, it offers improved security by distributing processing, storage, and applications across wide-ranging devices and data centers. What’s more, it paves the way for a budget-friendly route to scalability as well as versatility, enabling organizations to expand their computing capabilities through an amalgamation of IoT devices and edge computing data centers. Edge Data Centers and Their Usage! There isn’t any specific explanation that would describe the idea of an edge data center, considering it isn’t one consistent style of the facility. It instead consists of smaller facilities that serve both edge computing and larger-scale cloud services. Since they are located closer to the population, they could easily extend the edge of the network to deliver cloud computing resources and cached content to end-users. Typically, they connect to a larger central data center or multiple computer data centers seamlessly. Latency has forever been a matter of concern for cloud data center managers. In recent times, it has emerged as a key obstacle due to big data, the Internet of Things, cloud and streaming services, and other technology trends. Moreover, in today’s time and age, end-users and devices demand access to applications and services anytime and anywhere, which leaves no room for latency. Consequently, companies across the spectrum are establishing edge data centers to ensure cost-effective and high-functionality ways to provide customers with content and performance. A great way to learn more about the data center would be to understand its usage. The following are some of the services that primarily rely on edge computing: Internet of Things Internet of Things tools essentially require low latency and reliable connections to the data center to function with high intensity. IoT devices add up a vast number of edge computing utilities; thus using edge computing makes it simple and effective. Streaming Content Streaming content is one of the most consumed form of infotainment. Users today want their video to get started with a single click that edge facilities help achieve. Drones While Drones are increasingly getting popular, their features are also massively advancing. For example, with edge computing, drones could be controlled even from far-flung locations without any hitch. Artificial Intelligence AI is one of the most thriving technologies that have taken over the world with its magnificent scalability, To make AI advantageous to the system, it should be able to access data, process it, and communicate with the end-users effectively and quickly which an edge data center allows. Virtual Reality Virtual Reality needs to get updates as quickly as possible to create an immersive world for the users. Though primarily associated with gaming, VR has also gained recognition for different paradigms such as communication, education, and several other significant uses. Edge Computing and Data Centers – The Future! A dedicated 5G Provider Edge Computing is underway, building mammoth telecommunications capabilities into data center growth trends. These facilities could change the dynamics of 5G providers for enterprise brands and emerge as the dedicated 5G providers for organizations. Support sustainable business goals Edge data centers are being looked to as a periphery that can help build more efficient solutions to enable the sector’s sustainability. Edge computing is specifically designed to keep applications and data closer to devices and their users. Therefore, there is little doubt over the impact that edge computing will have on sustainable business goals. Making way for Robot Security Guards Evolution in AI and IoT has drastically changed the human staffing needs inside the data centers and made way for Robots. Currently, Robots have been deployed in some of the hyper-scale data centers for specific tasks. Whether it is the automated inspection, faulty disc locating, or disc charging, with Robots at the helm of affairs, everything can be completed seamlessly. Many data center and robotics professionals are predicting that the next couple of years will be big leaps when it comes to placing more robotics in the data center environment. Bill Kleyman - now Switch EVP of digital solutions - wrote in 2013. How Does One Choose a Location For a Data Center? Data centers are a critical part of any business enterprise operations. Hence, decisions regarding its locations cannot be relegated to an arbitrary choice. In the past, companies used to set up their edge data centers closer to their offices to maintain the proximity. However, that is swiftly changing now as the equipment administration and monitoring can be achieved remotely. With the data center industry transforming, performance is no longer the sole consideration. To create a defining success of the data centers, companies are now looking for different sites for their data centers, primarily focusing on factors like economic, political, social, and geographical. The current scenario highlights the significance of considering Energy efficiency, business continuity plan, and resource optimization. With so much at stake, the edge data centers should be effortlessly accessible. Conclusion Edge computing and data center growth has garnered a lot of interest among the users over the past few years. It will continue to thrive for many more years to come as it meets the eye of the global tech demands and the current and future needs of the users worldwide. Frequently Asked Questions What are the benefits of edge computing? One of the top benefits of edge computing is its quick response time and low latency period across all devices. It also simplifies the bandwidth and creates less risk in corporate security. What are the drawbacks of edge computing? A significant drawback of edge computing is the need of a huge storage capacity. The security challenge is also relatively high due to the massive amount of data stored in it. Moreover, the expensive cost factor is also a disadvantage of it. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are the benefits of edge computing?", "acceptedAnswer": { "@type": "Answer", "text": "One of the top benefits of edge computing is its quick response time and low latency period across all devices. It also simplifies the bandwidth and creates less risk in corporate." } },{ "@type": "Question", "name": "What are the drawbacks of edge computing?", "acceptedAnswer": { "@type": "Answer", "text": "A significant drawback of edge computing is the need of a huge storage capacity. The security challenge is also relatively high due to the massive amount of data stored in it. Moreover, the expensive cost factor is also a disadvantage of it." } }] }

Wireless, 5G

Article | May 18, 2023

Latency – the time it takes for devices to communicate with each other or with the server that’s imparting information – was already pretty low with 4G, but 5G will basically make it disappear. This development is great news for new tech forays into remote real-time gaming and self-driving cars, as the communication needs to be instantaneous for hiccup-free gameplay and to guarantee the safety of passengers. lthough there has been much media coverage regarding 5G’s health-related dangers and conspiracy-driven connection to the coronavirus, many people are still in the dark about what the 5G network can bring to the everyday internet user.

Article | May 20, 2021

The much-anticipated 5G Standalone has arrived. T-Mobile is the first to launch it in the USA, covering 250 million people across 7,500 cities and towns, including rural areas. China Mobile is the only other service provider to launch it in Hong Kong. Overall, 58 operators are currently investing (November 2020) in 5G SA, including those who have launched. 5G SA makes a break from 5G non-standalone by integrating the evolved packet core or the signaling brain of the 5G network, which controls the network's devices. It prepares the groundwork for new services unique to this generation of networks, such as network slicing to customize enterprise services across multiple networks.