Skip to main content

APM Tools and High-Availability Clusters: A Powerful Combination for Network Resiliency

Cassius Rhue
SIOS Technology

Network resilience, defined as the ability of a network to maintain connectivity and functional continuity in the event of disruption, is an operational imperative for technology dependent enterprises. Recent analysis by Siemens found that an hour of downtime can run into the millions, disrupting production, violating service level agreements (SLAs), preventing transactions, and running up large bills for staff overtime and outside consultants to restore service, run post-mortem analyses, and pay steep fines.

For some industries, like financial services, the effects of poor network resilience can be contagious. Global economies depend on financial services organizations with reliable, efficient IT infrastructure to facilitate trillions of dollars of commercial transactions each year, so the perception of network fragility can upset entire markets. That's why banking regulators like the Basel Committee and the US Federal Reserve require high standards for achieving network resilience. Likewise, because of their critical role in public safety, organizations operating in industries like healthcare, critical infrastructure, and telecommunications all have mandates to adopt practices designed to achieve high levels of network resilience.

Resilient Organizations Are Smart Organizations

IT infrastructure (on-premises, cloud, or hybrid) is becoming larger and more complex. IT management tools need data to drive better decision making and more process automation to complement manual intervention by IT staff. That is why smart organizations invest in the systems and strategies needed to make their IT infrastructure more resilient in the event of disruption, and why many are turning to application performance monitoring (APM) in conjunction with high availability (HA) clusters.

APM tools are well-positioned as a means of feeding better data into the platforms enterprises use to monitor and manage IT infrastructure. Data provided by APM provides a more precise understanding of system health, enabling IT management to establish more precise parameters for making decisions with the confidence of good, timely data. High availability clusters are either hardware (SAN-based clusters) or software (SANless clusters) that support seamless failover of services to backup resources in the event of an incident.

A Powerful Combination

The combination of APM and HA makes it easier for enterprises to improve network resiliency by supporting and injecting better decision making and the use of automation to enable seamless failover, predictive analytics, self-healing, and other capabilities consistent with maximizing network performance, uptime, and operational resilience. When used in a multi-cloud environment, services can failover to the organization's secondary cloud provider, which is a major advantage when an outage affects a cloud services provider. And in a multi-cloud environment resilience is further boosted by distributing workloads between clouds and eliminating a single source of failure.

As some enterprises evolve toward autonomous IT, data provided by APM provides a more precise understanding of system health, enabling IT management to establish more precise parameters for making decisions with confidence. This can help avoid an unnecessary dilemma in cases when the consequences of intervening to shut down one system, even if it is to switch to a backup system, could cost thousands of dollars.

Data-Based Decision Making

Consider a situation where the person responsible for a critical decision to failover to avoid a possible incident calculates that it may cost the organization more than $50,000 to manually intervene, even if the cost of waiting for an actual, catastrophic crash might be considerably higher. In that case, the decision maker may feel it would be better to blame something else rather than be questioned for making a gut decision or a good-faith judgment call. Better data means those involved have a clearer understanding of the situation and if they have to manually intervene, they can do so with hard evidence to justify their decision.

Here's where the one-two punch of APM tools and HA clusters helps by making it easier to maintain service continuity even when poor system performance, an incident, or a disaster threatens to disrupt operations. By giving IT managers a clear understanding of the health of the network and its components, operators can see exactly what's happening and take measures in advance of an incident or crisis to avert downtime. When failover is required, the reasoning is supported by data within the context of parameters established dictated by the organization's risk tolerance. Gray areas are eliminated.

Consider the Advantages

When integrated with an enterprise's APM tools, HA clusters provide network resilience by ensuring failover of mission-critical services and application is automatic and seamless, minimizing delays and errors that can occur during manual intervention and ensuring operations continue until the incident is resolved. Today, more organizations are opting for SANless clusters because they function the same as traditional SAN clusters but at a lower cost and without taxing network resources like SAN-based hardware. SANless clusters have the flexibility to work in on-premises, cloud, or hybrid infrastructure, and enable node configurations that support geographically distributed data centers, which is important for disaster planning.

Whether your organization operates in an industry where network resilience is mandated, or if you are looking for a way to differentiate by improving reliability, consider the advantages of teaming your APM solution with high availability clusters. Together they offer a smart, simple, and cost-effective way to keep pace with expectations for network resiliency.

Cassius Rhue is VP of Customer Experience at SIOS Technology

Hot Topics

The Latest

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

APM Tools and High-Availability Clusters: A Powerful Combination for Network Resiliency

Cassius Rhue
SIOS Technology

Network resilience, defined as the ability of a network to maintain connectivity and functional continuity in the event of disruption, is an operational imperative for technology dependent enterprises. Recent analysis by Siemens found that an hour of downtime can run into the millions, disrupting production, violating service level agreements (SLAs), preventing transactions, and running up large bills for staff overtime and outside consultants to restore service, run post-mortem analyses, and pay steep fines.

For some industries, like financial services, the effects of poor network resilience can be contagious. Global economies depend on financial services organizations with reliable, efficient IT infrastructure to facilitate trillions of dollars of commercial transactions each year, so the perception of network fragility can upset entire markets. That's why banking regulators like the Basel Committee and the US Federal Reserve require high standards for achieving network resilience. Likewise, because of their critical role in public safety, organizations operating in industries like healthcare, critical infrastructure, and telecommunications all have mandates to adopt practices designed to achieve high levels of network resilience.

Resilient Organizations Are Smart Organizations

IT infrastructure (on-premises, cloud, or hybrid) is becoming larger and more complex. IT management tools need data to drive better decision making and more process automation to complement manual intervention by IT staff. That is why smart organizations invest in the systems and strategies needed to make their IT infrastructure more resilient in the event of disruption, and why many are turning to application performance monitoring (APM) in conjunction with high availability (HA) clusters.

APM tools are well-positioned as a means of feeding better data into the platforms enterprises use to monitor and manage IT infrastructure. Data provided by APM provides a more precise understanding of system health, enabling IT management to establish more precise parameters for making decisions with the confidence of good, timely data. High availability clusters are either hardware (SAN-based clusters) or software (SANless clusters) that support seamless failover of services to backup resources in the event of an incident.

A Powerful Combination

The combination of APM and HA makes it easier for enterprises to improve network resiliency by supporting and injecting better decision making and the use of automation to enable seamless failover, predictive analytics, self-healing, and other capabilities consistent with maximizing network performance, uptime, and operational resilience. When used in a multi-cloud environment, services can failover to the organization's secondary cloud provider, which is a major advantage when an outage affects a cloud services provider. And in a multi-cloud environment resilience is further boosted by distributing workloads between clouds and eliminating a single source of failure.

As some enterprises evolve toward autonomous IT, data provided by APM provides a more precise understanding of system health, enabling IT management to establish more precise parameters for making decisions with confidence. This can help avoid an unnecessary dilemma in cases when the consequences of intervening to shut down one system, even if it is to switch to a backup system, could cost thousands of dollars.

Data-Based Decision Making

Consider a situation where the person responsible for a critical decision to failover to avoid a possible incident calculates that it may cost the organization more than $50,000 to manually intervene, even if the cost of waiting for an actual, catastrophic crash might be considerably higher. In that case, the decision maker may feel it would be better to blame something else rather than be questioned for making a gut decision or a good-faith judgment call. Better data means those involved have a clearer understanding of the situation and if they have to manually intervene, they can do so with hard evidence to justify their decision.

Here's where the one-two punch of APM tools and HA clusters helps by making it easier to maintain service continuity even when poor system performance, an incident, or a disaster threatens to disrupt operations. By giving IT managers a clear understanding of the health of the network and its components, operators can see exactly what's happening and take measures in advance of an incident or crisis to avert downtime. When failover is required, the reasoning is supported by data within the context of parameters established dictated by the organization's risk tolerance. Gray areas are eliminated.

Consider the Advantages

When integrated with an enterprise's APM tools, HA clusters provide network resilience by ensuring failover of mission-critical services and application is automatic and seamless, minimizing delays and errors that can occur during manual intervention and ensuring operations continue until the incident is resolved. Today, more organizations are opting for SANless clusters because they function the same as traditional SAN clusters but at a lower cost and without taxing network resources like SAN-based hardware. SANless clusters have the flexibility to work in on-premises, cloud, or hybrid infrastructure, and enable node configurations that support geographically distributed data centers, which is important for disaster planning.

Whether your organization operates in an industry where network resilience is mandated, or if you are looking for a way to differentiate by improving reliability, consider the advantages of teaming your APM solution with high availability clusters. Together they offer a smart, simple, and cost-effective way to keep pace with expectations for network resiliency.

Cassius Rhue is VP of Customer Experience at SIOS Technology

Hot Topics

The Latest

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...