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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

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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

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...