Cheetah Networks Inc. has been actively analyzing and monitoring, in real-time, the Quality of Experience (QoE) across a number of technologies at Area X.O. It has been an incredible opportunity to test and develop our solution within a diverse and rugged outdoor network environment.
Read more about our journey at Area X.O05-2021-Area-X.O-Success-Story-Cheetah-Networks-1-min
Source: Area X.O Link
Network operators rely on network management systems (NMS) and network element management systems (NEMS) to troubleshoot network issues, optimize performance, and plan ahead. These systems and tools provide data from the network on a per-element basis—for example, per host, router, terminal server, etc.—and sometimes by network area, such as edge, core, or cloud.
Each tool also provides some degree of analysis and, often, proactive alerts and alarms to facilitate proactive network management. By analyzing the feedback these tools provide, network administrators can promote change.
What these systems are unable to do is provide visualization of the end-user (person or machine) experience on the network.
Rather than reinventing the wheel with more monitoring and management software, Cheetah Networks focuses on the flow of traffic through the entire network. The PulseView™ Solution complements NMS and NEMS with a tool that provides immediate value to the entire business.
Rather than reinventing the wheel with more monitoring and management software, Cheetah Networks focuses on the flow of traffic through the entire network.”
PulseView™ delivers a vendor-agnostic, real-time network QoE score from the edge-to the clous, across all an operator’s public and private 4G, 5G, and Cat-M networks. ARTINA™ is the Artificial Intelligence (AI) engine behind PulseView that analyses and correlates at machine speed what people, machines, and applications are actually experiencing from the network.
With ARTINA’s actionable, real-time network analytics delivered via PulseView’s single-pane-of-glass, network operators can go beyond network troubleshooting and optimization based on historical information. Now, it is possible to:
Contact Cheetah Networks to learn the QoE score of your network.
For public and private network operators, the old adage, “you can’t measure what you can’t see”, has traditionally been addressed with vendor- and equipment-specific systems and tools. (more…)
Heterogenous is the new normal in today’s wireless networks. This is especially relevant in hybrid and private industrial networks, like those owned and operated by oil and gas, mining, and utility companies. These heterogenous networks feature common complexities: (more…)
The future looks bright for mining and that evolution and growth affects the challenges mining companies face as well as the solutions they are deploying. In this article, Cheetah Networks’ CRO, Louis Lambert, explores the top pressures on mining companies today and the implications of those forces on mining networks.
Read the full article here.
Every private and public network operator today needs the ability to visualize not just how the network is performing but also how that performance impacts the business, machines, and people connected to it. This view is known as Quality of Experience (QoE).
Network equipment and network implementations are not traditionally designed to provide end-to-end visibility. The more complex the end-to-end network, the more important it is to focus on QoE. In this article, we examine the needs and requirements of delivering a homogenous service experience over heterogenous network infrastructure.
Public and Private Networks: Dependent and Distinct
Private networks and clouds coexist and interconnect with the large-scale public networks that have underpinned telecommunication for the past century. Yet, each of these networks is designed and optimized for different services and to serve particular customers, subscribers, and users.
Private networks are built to meet needs that public networks cannot or do not currently provide for and generally need to deliver higher-grade connectivity, security and reliability than public networks. For this reason, the quality, reliability, and performance expected of private networks is often of higher grade than that of public networks.
Many private networks are more complex than public networks. Larger private operators—oil and gas, mining, and utility companies, for example—are multi-regional and international. They must integrate and manage networks across several regions and countries. Frequent merger and acquisition activity in these industries also increases the complexity and heterogeneity of networks with multiple vendor implementations.
Machine-to-machine (M2M) networking further increases the levels of complexity in and on the network and drives major innovation in private networks.
Public operators are also responding to these new opportunities by building out IoT networks to provide end-to-end, IoT-grade services and applications. Fleet management, smart cities, smart agriculture, and e-health are examples.
Private and public operators need a way to deliver homogenous service experience
over heterogenous infrastructure and QoS (metrics) tools.
In this context, one common challenge puts pressure on both public and private network operators: effectively managing the performance of end-to-end services and applications despite the complexity behind the connection point. Operators need a way to deliver homogenous service experience over heterogenous infrastructure and QoS (metrics) tools.
Changing the Game of Network Visualization for QoE
Network equipment and network implementations are not traditionally designed to provide end-to-end visibility. Rather, QoS tools and network element management systems (NEMS) monitor individual pieces of equipment and segments of a network. In the NOC, this means multiple systems and screens to monitor; it may also require dedicated individuals focusing on specific pieces of the network.
To deliver scalable QoE and meet the growing demand on IoT networks, the scope and capabilities of network visualization must be redefined.
The more complex the end-to-end network, the more important it is to focus on QoE. QoE visualization can best be monitored and managed on a single pane of glass. One centralized interface must illuminate the experience of machines and people on the network. It must provide homogenous visualization uninhibited by the network’s heterogenous underpinnings. This QoE view must also be available in real-time to the entire organization, including operations, IT, and business decision-makers.
The primary goal or measurement of QoE network visualization is to detect
anomalies before they impact the machines and users relying on the network.
The Role of ML and AI in Network Analytics and Automation
Providing end-to-end QoE in increasingly diverse network environments will require automation. Artificial intelligence (AI) and machine learning (ML) are needed to achieve end-to-end visualization with impact correlation of diverse networks and applications.
For public operators, scale is another concern. With IDC anticipating more than 40 billion connected IoT devices worldwide by 2027, it’s easy to imagine a large telco needing to support 1 billion such devices in short order. Scale at this magnitude will not be possible with current QoS tools, NOC staff, and process alone. Automation assisted by ML and AI is the only feasible means to maintain real-time visibility into an end-to-end network’s QoE with scale and at machine-speed.
For private operators, the criticality of network health and reliability has health, safety, and environmental (HSE) implications that public networks do not. Real-time HSE and business continuity notifications, as well as automated remediation actions, are needed to support and enable ongoing automation throughout these operations and business models.
Automation is also critical to provide the modeling capabilities all operators will depend on to anticipate future needs and how best to address them. This includes knowing where to invest both geographically and in terms of technology (LTE, 5G, SDN, MEC, edge compute, network slicing, etc.), as well as ensuring investments enable improved QoE.
Actionable, Real-Time, IOT Network Analytics
Understanding the goals and current challenges of both public and private network operators, Cheetah Networks PulseView™ is an AI- and ML-enabled QoE analytics solution built on five pillars:
For more information, please contact us or request a demonstration of the PulseView™ Solution.
Every public or private network operator needs to understand how its network is performing for its customers. Although operators configure QoS ( Quality of Service) parameters throughout their network infrastructures, the EXPERIENCE of the network is ultimately the customer’s and is referred to as Quality of Experience (QoE). (more…)
Cheetah Networks’ actionable, real-time, edge-to-cloud IoT network analytics provides enhanced customer experience for TELUS’ IoT solutions.
The Cheetah Networks team recently completed the first phase of an innovative collaboration with TELUS to improve the Quality of Experience (QoE) the national carrier’s customers (more…)