Proactive Failure Detection: How to Prevent Network Downtime and Improve Reliability
For telecommunications companies, reliability is everything.
Users have come to expect uninterrupted service. That’s why operations organizations are measured on uptime.
When issues arise, timely restoration of service carries the utmost urgency. Having the processes and systems in place to carry this out represents a tremendous cost for the business.
It’s important because network downtime carries revenue-impacting risk to the brand.
Brand reputation risk aside, dropped calls and network failures for VIP subscribers can directly impact the workload and morale of an operations team, while faults in an emergency services system can be a matter of life and death.
Each outage isn’t just a technical setback—it’s a costly cycle of fixing problems that may have been prevented.
Today's mobile operators invest millions of dollars in observability systems to improve detection and remediate network issues. But no system has been able to analyze every packet of every activity flow for any select number of subscribers in near real time—until now.
The strongest competitive advantage a telco can establish is reliability. This requires proactive failure detection that allows the network to operate with minimized downtime and issues.
In this article, we’ll explore the challenges posed by modern network infrastructures and outline how advanced failure detection methods can mitigate issues before they lead to impactful disruptions.
The Importance of Proactive Failure Detection in 5G Networks
The rise of 5G networks has introduced exceedingly high levels of complexity into network infrastructures.
With massive data traffic and a multitude of connected devices, the possibility of network failures has increased. In such environments, eventual weak accuracy in failure detection can lead to serious issues.
This is particularly problematic in synchronous systems where timing and coordination are critical. Achieving eventual strong accuracy in detecting failures is crucial for maintaining service quality, especially for high-value VIP customers.
Business Implications of Effective Failure Detection
This is the area in which network downtimes can lead to far-reaching business implications, including diminished customer trust, lost revenue, and increased operational costs.
When a crash occurs, telecommunications businesses must invest significant resources in resolving the issue, often resulting in prolonged outages and a decline in customer satisfaction.
At the end of the last section, we mentioned the importance of maintaining service quality for VIP subscribers. These customers expect high levels of service and they’re paying for that expectation to be met. A lapse can lead to customer churn and damage to the brand’s reputation.
Proactive diagnostics and near real-time monitoring ensure that even during the initial period of a failure event, the system can quickly identify and resolve issues, maintaining the quality of experience and safeguarding customer loyalty.
Challenges in Identifying and Mitigating Network Failures
The bottom line is that without proactive failure detection, telecommunications firms risk under delivering and overspending:
- Under delivering on customer expectations; and
- Overspending on troubleshooting immediate issues they didn’t see coming.
In a highly competitive industry, this is no way to turn a profit. But of course, detecting potential failures before they escalate is easier said than done.
Here’s why it’s such a challenge:
Complexity of Modern Networks
We’ve already mentioned the complexity of 5G networks, but it’s worth emphasizing again because that complexity is the biggest challenge to network diagnostics. These networks operate across multiple layers, each with its own set of potential failure points.
The massive volume of data traffic, combined with the number of connected devices, makes near real-time monitoring and detection incredibly challenging. We already used the example of synchronous systems, but the importance of detection is the same for asynchronous systems, where processes do not necessarily occur at the same time—adding to the complexity and challenge.
Once again, an unreliable failure detector causes an increased risk of eventual weak accuracy in identifying issues, resulting in unnecessary network adjustments, affecting overall performance and reliability.
Scalability Issues
As networks scale to accommodate growth, this challenge only intensifies.
For telcos, growing means finding additional revenue streams which typically involves more strain on already complex networks. Existing failure detection methods that are reliant on manual work are not equipped to take on more, limiting the ability to grow business.
Larger networks mean more volumes of packets and data, more devices, and an increased number of potential points of failure. This scaling can strain the capabilities of existing monitoring tools.
Just as in the example we used at the beginning of this article, this is especially relevant to those operating under synchronous operating systems, where coordinated timing is crucial.
In these expanded environments, weak completeness—where not all failures are detected—can be a significant issue. Without advanced detection tools that offer eventual strong accuracy, network operators struggle to identify and address all potential failures, leading to unanticipated downtimes and service disruptions.
Multiple Stacks and Data Sources
It once again comes back to the complexity of modern networks.
They operate across multiple stacks and involve numerous data sources, each contributing to overall network performance. Correlating data across these various layers is essential for accurate failure detection, particularly in distributed systems where different parts of the network may operate independently and must solve consensus to ensure consistency.
In asynchronous systems, where processes are not synchronized, the risk of an unreliable failure detector causing issues increases. For example, if the maximum number of failures that a system can tolerate is exceeded, and consensus cannot be effectively solved, the system may fail to function correctly.
Addressing these challenges requires sophisticated tools, such as AI- and ML-informed systems, that are capable of integrating data from multiple sources and providing accurate, near real-time diagnostics, while also ensuring the network can reliably solve consensus even under challenging conditions.
The Role of Advanced Tools in Proactive Failure Detection
Overcoming these challenges efficiently requires advanced tools that detect failures and mitigate issues in near real-time.
By utilizing AI, ML, and near real-time diagnostics, network operators can not only anticipate and resolve issues swiftly, but also ensure seamless operations.
That’s an enormous competitive advantage in an industry where everyone is looking for the upper hand.
Artificial Intelligence and Machine Learning For Failure Detection
Advanced tools powered by AI and ML enable predictive analytics, allowing network operators to identify potential issues before they become critical.
In scenarios where eventual weak accuracy is a concern, AI-driven tools can improve detection precision by analyzing patterns in network data and predicting failures with higher reliability, reducing the chances of processes being eventually permanently suspected without just cause.
Advanced tools like convolutional neural networks (CNNs) and image recognition are now used for high-throughput classification of network data.
These tools can process vast amounts of data quickly, identifying patterns that might indicate an imminent failure. By achieving eventual strong accuracy, they ensure that even in complex, asynchronous systems, network failures are detected and addressed promptly.
Transform Your Network with B-Yond’s Failure Detection
In today’s connected world, network failures are not just minor inconveniences—they can lead to cascading effects that disrupt businesses, impact financial transactions, and even jeopardize critical infrastructure.
For instance, in 2023, a major European telecom operator experienced an outage that affected over 10 million subscribers, causing payment failures, disruptions in emergency services, and a 30% spike in customer churn.
Such incidents highlight the urgent need for more proactive network failure detection.
Traditional methods often fall short, as they rely on reactive approaches that miss critical packet-level failures and escalate into larger network-wide issues.
B-Yond’s Packet-Based Failure Detection offers a solution that prevents these failures before they have widespread impact.
By using advanced technology to segregate and analyze network issues, B-Yond helps operators diagnose problems up to 70% faster, reducing downtime and improving service reliability.
One of the key advantages of this system is its ability to prioritize VIP subscribers, ensuring that high-value customers continue to receive top-tier service.
Application for VIP Subscribers
Packet-based failure detection offers a powerful use case for ensuring exceptional service to VIP subscribers by enabling near real-time, proactive monitoring and issue resolution.
High-value users, such as enterprise clients or influential individuals, rely on uninterrupted, premium service, and any disruption can have significant impacts on their business or personal activities.
B-Yond’s solution utilizes deep learning models like CNNs to monitor and diagnose network flows specific to VIP users, offering immediate insights into potential failures.
Five-Step Process for Near Real-Time VIP Monitoring
This proactive approach involves a five-step process that helps operators resolve issues before they escalate, ensuring smooth service for their most important customers and securing customer loyalty.
Without preventative failure detection, interruptions can create a disappointing customer experience, especially for VIP subscribers, leading to lost revenue and increased operational costs.
That’s why AGILITY’s packet-based failure detection system was developed to prevent these types of failures before they occur.
Here’s how it works:
Step #1: Extracting PCAPs
Packet Capture (PCAP) extraction is the first step in packet-based failure detection.
PCAPs are collected from various probes across the network—Viavi, Spirent, and Polystar are major probe vendors that specialize in extracting PCAPs. Once stored, the PCAPs are analyzed to ensure optimal performance.
Step #2: Filtering VIPs
The captured packets are funneled into failure detection which can be deployed at the edge.
Packet based failure detection receives packets of focus on VIP subscribers, prioritizing the network traffic of these high-value users. This step ensures efficient use of resources by narrowing down the scope of monitoring.
Step #3: Failure Detection (Classification of Flows via Image Recognition)
Once the PCAPs have been filtered based on the highlighted VIP subscribers, failure detection analysis can occur, prioritizing the VIP subscribers.
By leveraging image recognition through CNNs, the system classifies network flows as either pass or fail in near real-time. This enables early identification of potential issues that could affect VIP users, ensuring proactive monitoring and timely intervention.
Step #4: Running Analysis
After a failure is detected, in-depth analysis is performed to interpret the data and diagnose the root cause of the problem, offering actionable insights for network operators.
This process involves interpreting ingestion of the failure into a network diagnostics tool like B-Yond’s AGILITY which provides rapid actionable insights from collection standards and OEM documentation.
Step #5: Determine Diagnosis
The final step involves pinpointing the underlying causes of network failures and enabling operators to implement targeted fixes.
This ensures minimal disruption and maintains a high-quality experience for VIP subscribers.
What Can B-Yond Do For Your Network?
By utilizing the five-step approach outlined above, telecom operators can proactively ensure the highest service standards for VIPs, protecting their reputation and reducing potential losses associated with network downtime.
The result is:
Improved Operational Efficiency:
B-Yond drastically reduces Mean-Time to Repair, ensuring network issues are resolved swiftly, cutting downtime significantly, and restoring services faster than ever.
Additional Cost Savings:
B-Yond drastically reduces overtime labor expenses by cutting the need for extended work hours. With streamlined troubleshooting capabilities, it also decreases dependence on outside contractors.
Proactive Network Management:
B-Yond’s issue detection, powered by self-learning algorithms, identifies and resolves potential problems before they can escalate, ensuring smoother network operations and fewer disruptions.
A Future-Proofed Network:
B-Yond is designed with the future in mind, supporting both 4G and 5G networks. As your company transitions to 5G, B-Yond helps navigate the complexities and unknowns of new technology, ensuring your infrastructure is scalable and ready for whatever comes next.
Don’t wait to experience the transformative power of B-Yond’s failure detection. Discover how this innovative solution can reduce operational costs, maintain profitability and ensure scalability for future revenue opportunities.
