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LiveAction Introduces LiveNX Insight

LiveAction announced LiveNX Insight, which extends LiveAction’s patented LiveNX network performance and analytics platform by applying deep learning to help prevent network problems before they occur while enhancing enterprise security.

LiveNX Insight uses machine learning and predictive models to improve network performance management in three ways.

First, LiveNX Insight applies deep learning to uncorrelated network metadata in real time. LiveNX Insight continuously learns and applies knowledge based on trends and incorporates the expertise of thousands of network engineers and IT operations professionals. This can include dynamic baselining that enables knowing which voice traffic to prioritize, when to throttle bandwidth, what is normal, and if a user’s access should be blocked.

Second, it automates recurring routine networking tasks. For example, fault monitoring the status of bandwidth thresholds with respect to Quality of Service (QoS). This boosts IT operational productivity through automation while predictively sharing insight that could affect customer, employee or partners’ experiences.

Third, LiveNX Insight enables network operations teams to optimize digital experiences for people and IoT-enabled assets. With LiveNX Insight, network behavior becomes operational intelligence based on activity by users, devices, locations, and applications. Further, network policies can be dynamically adjusted to ensure people and devices have high performing connected experiences.

“As businesses become more digitized, networks are emerging as an ultimate source of operational insight,” said Shamus McGillicuddy, Senior Analyst, Enterprise Management Associates. “Yet accessing that insight is getting complicated as more applications, devices, and users engage on the network. Network visualization and analytics are two ways to address this issue with machine learning emerging as the much needed third leg driving the network of the future.”

“With machine learning, LiveNX Insight simplifies day-to-day troubleshooting for network operations and advances intelligent automation for self-driving networks,” said Darren Kimura, Executive Chairman, LiveAction. “LiveNX Insight further advances our strength in network performance management and analytics, which is critical for our customers as they strive to stay ahead of digital transformation initiatives.”

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LiveAction Introduces LiveNX Insight

LiveAction announced LiveNX Insight, which extends LiveAction’s patented LiveNX network performance and analytics platform by applying deep learning to help prevent network problems before they occur while enhancing enterprise security.

LiveNX Insight uses machine learning and predictive models to improve network performance management in three ways.

First, LiveNX Insight applies deep learning to uncorrelated network metadata in real time. LiveNX Insight continuously learns and applies knowledge based on trends and incorporates the expertise of thousands of network engineers and IT operations professionals. This can include dynamic baselining that enables knowing which voice traffic to prioritize, when to throttle bandwidth, what is normal, and if a user’s access should be blocked.

Second, it automates recurring routine networking tasks. For example, fault monitoring the status of bandwidth thresholds with respect to Quality of Service (QoS). This boosts IT operational productivity through automation while predictively sharing insight that could affect customer, employee or partners’ experiences.

Third, LiveNX Insight enables network operations teams to optimize digital experiences for people and IoT-enabled assets. With LiveNX Insight, network behavior becomes operational intelligence based on activity by users, devices, locations, and applications. Further, network policies can be dynamically adjusted to ensure people and devices have high performing connected experiences.

“As businesses become more digitized, networks are emerging as an ultimate source of operational insight,” said Shamus McGillicuddy, Senior Analyst, Enterprise Management Associates. “Yet accessing that insight is getting complicated as more applications, devices, and users engage on the network. Network visualization and analytics are two ways to address this issue with machine learning emerging as the much needed third leg driving the network of the future.”

“With machine learning, LiveNX Insight simplifies day-to-day troubleshooting for network operations and advances intelligent automation for self-driving networks,” said Darren Kimura, Executive Chairman, LiveAction. “LiveNX Insight further advances our strength in network performance management and analytics, which is critical for our customers as they strive to stay ahead of digital transformation initiatives.”

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For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...