The age of intelligent Wi-Fi: automated to autonomous networking

Transcription

1 The age of intelligent Wi-Fi: automated to autonomous networking 1

2 Wireless networking has evolved to become the backbone for any enterprise infrastructure. With high volumes of data, users, devices and applications that run on enterprise access networks, the need to constantly monitor, assess and resolve network management issues has risen rapidly. Artificial Intelligence (AI) and Machine Learning (ML) are the two latest technologies that enable network managers to automate this process and empower enterprises to deliver a better WiFi experience. Intelligent Wi-Fi, a culmination of these two very powerful technologies, results in predictive network management, marking a paradigm shift in how enterprises want to deliver the best Wi-Fi experience to their users. 2

3 How big is the Wi-Fi network management problem? Wi-Fi is a critical resource for any enterprise. Users don t just expect a wireless experience, they demand it; and they expect it to be high quality, seamless, consistent and secure. Powered by digital technologies such as smart mobility, big data analytics and IoT, the hyper-connected enterprise thrives on the ability to process, analyze and respond to data faster and better. Any network downtime, therefore, means lost productivity and revenue. In fact, a survey conducted by EMA showed that networking teams spend nearly 36% of their total time on reactive troubleshooting instead of proactive problem solving. 3 According to a report by IHS, IT downtime (comprising server, application and networking downtime) costs North American enterprises nearly $700 billion a year.1 Almost 40% of this downtime results from network interruption, caused by equipment problems or failure. 2 A big problem for network managers is to keep an eye on these interruptions and troubleshoot them. Reduce firefights: move from reactive to proactive intelligence Enterprises that realize the real impact that artificial intelligence and machine learning can have on Wi-Fi networks are one step ahead. As network teams deal with the challenge of monitoring vast amounts of data flowing across networks, emerging network analytics models are simplifying operations, optimizing performance and improving how enterprises respond to network problems. The combined power of artificial intelligence and machine learning automates troubleshooting and responsiveness through a multipronged approach that allows the network to sense, comprehend, act and learn. 3

4 Sense data to provide deep visibility at the system and user levels Artificial intelligence-enabled Wi-Fi monitors data as it moves across the enterprise. It provides a granular view of Wi-Fi performance at each level of the network as well as the impact on the user, and it gives network teams more effective visibility and control over network management. In a dynamic environment, where parameters like RF interference, roaming, client steering and QoS are constantly changing, network teams must go through volumes of data to identify the real source of the problem. Also, these parameters must be constantly adjusted for normal network performance. Artificial intelligence provides this deep visibility of MAC frames and IP packets at any layer to provide a granular picture of the network. Act upon network issues to resolve them intuitively Intelligent Wi-Fi pinpoints exactly where the network problem is and enables a proactive response. With artificial intelligence, network teams no longer have to spend hours trying to troubleshoot connection problems arising from various sources. As wireless infrastructure becomes the de facto backbone for any digital enterprise, manual process and packet sniffers are not adequate for timely detection and remediation of tactical network problems. Enterprises that continue to rely on these legacy methods often find that troubleshooting is an expensive yet critical function they must have. It is also a time-consuming function that ignores the Wi-Fi experience at the user level while only focusing at the system level. Intelligent networks address these issues by analyzing RF packets from each user to identify the root cause of the problem and determine corrective actions such as quarantining suspicious data packets or auto-correcting issues. Comprehend how the network is performing and assess for actionable insights Once a comprehensive map of the network is available, the network is equipped with greater context awareness to predict network problems before they happen. Intelligent Wi-Fi analyzes network performance to create patterns, create co-relations, identify anomalies and determine root causes of problems. Enterprises are also able to identify how their users respond to the Wi-Fi experience, which gives them actionable insights that they can use to create more revenue opportunities. Learn from the output and continuously improve network optimization and user experience With machine learning, Wi-Fi learns from outcomes of predictive troubleshooting to continuously improve network performance and user experience. Machine learning algorithms can reconfigure resources based on new data and from previous insights the higher the volume of realistic data that is consumed and processed, the greater the learning. Machine learning enabled networks are, therefore, an ideal solution for highly sophisticated and large environments. 4

5 Buzz in network automation: artificial intelligence and machine learning are your virtual assistants In 2017, the enterprise WLAN market grew 5.7% to US $5.7 billion, with the US share at 37.6%, as per IDC. 4 In this rapidly growing market, networks have become business-critical to operations and continuity. In addition, they are also taking more complex and sophisticated forms. As Wi-Fi becomes the prominent access to connectivity in an enterprise environment, there is an exponential growth of access points being deployed. Non-enterprise/ BYOD, guest and IoT devices connecting to the network adversely affect the Wi-Fi service levels and initiate troubleshooting alerts and tickets. The advent of multiple device types and users further complicates the troubleshooting process and methodology, and significantly drives up time required to support, maintain and troubleshoot. Network management is, therefore, becoming challenging, costly and time-consuming. Cloud-managed W-Fi simplifies network management by performing non-core functions such as control and management in a hosted and managed cloud. It also enables enterprises and Managed Service Providers (MSPs) to quickly scale operations without impacting network performance. Intelligent Wi-Fi uses the combined power of cloud infrastructure and artificial intelligence to enhance overall network performance. It leverages big data analytics and enhanced situational awareness to analyze a massive volume of data from millions of wirelessenabled devices, including smartphones, drones, wearables, sensors and IoT devices, and provides solutions to create a comprehensive map of the network. The network, based on this map, can predict network faults and rectify them before the user reports an issue. Machine learning mechanisms within the network allow continuous self-improvement in network performance, enabling more intelligent decision-making. The result is an ultrareliable, low latency, intelligently managed Wi-Fi network that meets stringent quality-ofservice needs. 5

6 Turn CAPEX to OPEX Wi-Fi as a service enables enterprises to show Wi-Fi as an operating expense instead of having to spend upfront on hardware, software and perpetual licenses. The model allows managed service providers (MSPs) and enterprises to seamlessly provide highly optimized network infrastructure to their employees and clients with the least human intervention. By infusing artificial intelligence and machine learning, enterprises can avoid upfront equipment purchase costs, in addition to having network teams spend less time on management and troubleshooting. With the increased usage of data, devices, users and mission-critical applications in the enterprise segment, the concept of Wi-Fi as NaaS (Network as a Service) is becoming increasingly adopted especially in mid-sized enterprises with 500 or fewer employees. These enterprises would rather prefer to outsource their network management requirements to allow them to focus on their core business. The minimal requirement of in-house IT resources to manage networks and the sizeable reduction in deployment of costly equipment to enhance network performance has further driven the preferred OPEX subscription model. This has allowed enterprises to focus their CAPEX budgets on their core business. It is becoming increasingly harder and costlier to troubleshoot wireless networks, given the several mobile device types, applications and operating systems add to it the number of users and wireless-enabled connected devices. Wi-Fi as a service, coupled with AI/ ML capabilities, enables enterprises to provide highly reliable and optimized service to end users. Furthermore, the proactive and predictive monitoring allows the network to diagnose and remediate issues autonomously or send alerts for timely human intervention. This will additionally assist in reducing the OPEX pressure on enterprises towards troubleshooting costs and focus on scaling their business. In a survey conducted by IHS, it was found that enterprises reported an average of five downtime events in a month. 5 The costs associated with these downtimes ranged for $1 million per year for a mid-sized company to a whopping upwards of $60 million per year for one large enterprise in North America alone this coupled with employee productivity and revenue loss at 78% and 17%, respectively. 6 Additionally, network administrators are spending a substantial amount of time troubleshooting and resolving user reported Wi-Fi issues. Therefore, enterprises are proactively investing to upgrade their Wi- Fi networks to reduce downtime and see gains in productivity and revenue. To meet these enterprise goals, the Wi-Fi industry is rapidly moving to cloud-managed Wi-Fi. 6

7 Use network intelligence to deliver smart services operationalize, optimize, monetize The impact of IoT on human lives is mindboggling, given its potential to improve living conditions, create smarter cities, improve human health, control disease and make enterprises more diverse and profitable. As per Gartner, the total number of connected devices in use globally including such items as smart home appliances, wearables, smart metering systems and autonomous vehicles is projected to grow to 20.4 billion by 2020 from an estimated 11.2 billion in The enterprise share in the IoT installed base is expected to grow from 4.1 billion to 7.5 billion over the same period. 8 Artificial intelligence and machine learning enabled networks allow enterprises to create and deliver this IoT ecosystem where devices are always talking to each other and services can be delivered based on user patterns and location. Employees will get a super-fast, seamless wireless connection and be able to switch between devices to collaborate smoothly and securely. Similarly, retail customers will be guided to the products of their choice faster, driving sales. Machine learning will learn and re-learn customer usage patterns, providing not just actionable insights but mimicking human decisions for improved customer engagement. Other sectors such as health care and call centers stand to benefit too as intelligent networks churn vast volumes of data to ensure the network is always optimized for the best user experience. Is it the start of role reversal for the autonomous network? Artificial intelligence and machine learning allow network teams to focus on user performance rather than just network performance. With predictive capabilities, intelligent networks proactively identify performance issues and intuitively heal themselves through remediation measures. While the autonomous self-healing network is the dream of network engineers, automating is the first step toward achieving this goal. Intelligent Wi-Fi enables network teams to focus on how business productivity can improve with their given network infrastructure or how the insights they get from intelligent Wi-Fi can be leveraged to tap into new business opportunities. Enterprises that embrace this intelligent Wi-Fi will be pioneers in the world of self-healing, selfoptimizing and self-learning networks. Today enterprises have human teams that work to ensure network management is seamless and performance is at peak. With the autonomous self-healing network, the role will be reversed. Network management will be automated and the super-intelligent network will work to ensure human teams are at the top of their performance. Today enterprises have human teams that work to ensure network management is seamless and performance is at peak. With the autonomous self-healing network, the role will be reversed. Network management will be automated and the super-intelligent network will work to ensure human teams are at the top of their performance. 7

8 Why Samsung? Cloud Wi-Fi 1.0 efficiently resolved critical pain points of the SMB segment as it evolved. Deployment of multiple APs has allowed Wi-Fi as a primary connectivity anchor for enterprise employees, multiple devices and guests to gain ready access. Although Cloud Wi-Fi 1.0 has leveraged its technology to fulfill and support connectivity anchoring for large enterprises and their employees, Cloud Wi-Fi 2.0 brings more promise to the enterprise. Intelligent Wi-Fi networks can optimize network performance and path, enable autonomous predictive diagnosis and raise timely alerts and tickets for human intervention, if required. Overall, next generation Wi-Fi networks will drive network efficiency and support. This augmentation will further decrease incidence of holes and connectivity failures in the network and avoid productivity and revenue loss for enterprises. Autonomous remediation and intelligence related to issues will reduce costs associated with help desk and IT administrators intervention potentially saving enterprises millions of dollars. As a leader in consumer electronics and connectivity solutions, Samsung is paving the way for IoT adoption globally. The wireless network suite - 5G, unlicensed small cell and Wi-Fi - will be the backbone for IoT connectivity. Well aware of the challenges of managing at-scale wireless networks, Samsung is looking forward to introducing a next-gen cloud platform, which will usher the enterprise segment into the era of Intelligent Cloud Wi-Fi. This AI/ ML powered cloud will disrupt the existing market model of a largely CAPEX-oriented view to one that is aimed at lowering TCO, increasing ROI and making it possible to roll large-scale wireless networks. What if a network manager could spend time anticipating future network infrastructure potential instead of troubleshooting today s connectivity problems? 1 IHS Infonetics; The Cost of Server, Application, and Network Downtime: Annual North American Enterprise Survey and Calculator; January Ibid. 3 Enterprise Management Associates; Report Summary: Network Management Megatrends; April CNBC; Worldwide Enterprise WLAN Market Sees Steady Growth in Full Year and Q4 2017, According to IDC; March IHS Infonetics; The Cost of Server, Application, and Network Downtime: Annual North American Enterprise Survey and Calculator; January Ibid. 7 Gartner; Gartner Says 8.4 Billion Connected Things Will Be in Use in 2017, Up 31 Percent from 2016; February Ibid. 8

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