Your GPUs Are Idle. It’s Probably Not the GPUs’ Fault.

Telnet Networks blog header — Your GPUs Are Idle: validating AI data center performance with Keysight KAI solutions

Canada is building AI data centres at unprecedented scale. Here’s how to make sure the fabric underneath them actually performs — before the capital is committed.

In May 2026, the Government of Canada and TELUS announced work to advance a sovereign AI factory cluster in British Columbia. This build is designed to scale beyond 60,000 GPUs and 150 megawatts by 2032, drawing on an initial 85 MW of clean power from BC Hydro. Two months earlier, Bell committed $1.7 billion over two years to a 300 MW facility outside Regina, leasing capacity to Cerebras and CoreWeave.

Behind those projects sits the Canadian Sovereign AI Compute Strategy, a $2 billion federal commitment, and the AI Sovereign Compute Infrastructure Program, which allocates roughly $890 million over seven fiscal years beginning in 2026–27.

This is the largest concentrated infrastructure build Canadian networking teams have seen in a generation. And it is happening on a compressed schedule, with hardware that behaves nothing like the enterprise networks most of us have spent our careers operating.

If you are standing up GPU capacity, whether at a service provider, a bank, a research institution, or an enterprise bringing models in-house, this post is about the failure mode that quietly erodes the return on all of it, and the practical sequence for catching it before you have poured concrete.

The gap between what you bought and what you get

Start with a number that should be uncomfortable for anyone who has signed a GPU purchase order.

Model FLOPs Utilization (MFU) measures how much of a cluster’s theoretical peak compute a training run actually converts into useful work. It is the honest measure of whether you are getting what you paid for. The published figures from teams with world-class infrastructure engineering are humbling:

Training runReported MFU
GPT-321.3%
Megatron-Turing NLG 530B30.2%
Gopher32.5%
PaLM 540B46.2% (57.8% including rematerialization)
Megatron-LM on H100 clustersup to ~47%

These are among the most carefully optimized training runs ever executed. They still leave half or more of the theoretical compute on the table.

An important caveat, because it matters for diagnosis: the MFU gap is not all network. Memory-bound operations, pipeline bubbles, kernel inefficiency, and suboptimal parallelism strategy all contribute. Anyone telling you the fabric explains the entire gap is selling something.

But here is what separates the network portion from the rest: it gets worse as you scale, and it is the part you commit to irreversibly when you buy hardware. You can refactor a kernel next quarter. You cannot re-cable a spine layer that was undersized for the traffic pattern you actually run.

Why AI fabrics fail differently

Traditional enterprise networks optimize for north-south traffic and client-server patterns. Loss is tolerable, retransmission is routine, and a few milliseconds of jitter is invisible to the user.

AI training fabrics operate under different physics. Three properties drive almost every problem worth knowing about.

Collective operations create hard synchronization dependencies. When a distributed training job runs an AllReduce, every participating GPU must complete its exchange before the job advances. The job proceeds at the pace of the slowest worker. One congested rack does not slow that rack by 10% — it stalls the entire cluster. Localized congestion produces cluster-wide idle time, which is why the symptom you see (idle accelerators) is so far removed from the cause.

Elephant flows collide at predictable choke points. AI training generates a small number of very large, long-lived flows rather than many small ones. When these converge on the same uplink, you get buffer exhaustion and drops. Meta’s engineering team documented this directly in their production RoCE deployment: fragmented job placement causing congestion on particular switch uplinks degraded training performance by more than 30%. That is a real production number from a team with world-class network engineering, not a vendor projection.

Loss recovery in RDMA is brutally inefficient. When a packet drops in an RDMA transfer, the protocol generally cannot recover it selectively. It waits for a timeout and retransmits the entire operation, or every packet following the loss. A single dropped packet can force retransmission of a very large transfer. Researchers have injected controlled loss rates as low as 0.1% into GPU training setups precisely because the effects compound so sharply at scale.

In an AI fabric, congestion is not a bandwidth problem. It is a synchronization problem.

None of these behaviours appear in a link-level throughput test. They emerge from the interaction of topology, congestion-control tuning, buffer strategy, and the specific communication pattern of your workload: at scale, under load, and over hours.

Which is why most teams discover them after the hardware is racked.

The validation gap

Here is the position most infrastructure teams find themselves in.

You can validate components. You can confirm a switch forwards at line rate, an optical transceiver meets spec, or a NIC negotiates correctly. All necessary, but none of it tells you whether a 256-node training job will hit congestion collapse at the spine under a real AllReduce pattern.

The conventional options are all unsatisfying:

  • Test with synthetic traffic. Generic load generators produce traffic that does not resemble collective communication. You validate the fabric against a workload you will never run.
  • Test with the real cluster. This requires owning the GPUs first, meaning the capital is committed and the topology decisions are locked.
  • Find out in production. The most common approach, and by a wide margin the most expensive.

What is missing is a way to observe system-level behaviour before the system physically exists.

A practical validation roadmap

The gap between “we know AI fabrics are different” and “we have proven ours works” is where most programs stall. Here is the sequence we recommend, and where each step fits in an existing network performance testing practice.

Step 1: Emulate the workload, not just the traffic. This is the foundational step, and the one most teams skip. Keysight, whose network test lineage runs back through Ixia, has extended that portfolio into AI infrastructure with KAI Data Center Builder. Rather than generating synthetic load, it emulates the actual collective operations training jobs run: AllReduce, AllGather, ReduceScatter, AlltoAll, Broadcast and Gather, across ring, tree, halving-doubling and PXN algorithms. Critically, it works with the collective communication libraries teams actually deploy, such as NCCL on NVIDIA, RCCL on AMD, or MSCCL on Microsoft, driving RDMA and RoCEv2 traffic across 400G, 800G and 1.6T fabrics.

The practical consequence is that you can model a topology before purchasing it: compare spine-leaf variations, oversubscription ratios, and switch vendors against the same repeatable workload, and see how job completion time responds.

Step 2: Look at the mechanisms, not just the throughput number. An aggregate bandwidth figure will not tell you why a job stalled. What you need is visibility into PFC pause behaviour, ECN and DCQCN response, retransmission counts, and ECMP hashing efficiency: the specific mechanisms that turn a healthy fabric into an idle cluster. Emulation platforms expose these directly, with hardware-based congestion notification and per-queue-pair rate control, so a congestion event becomes reproducible rather than anecdotal.

Step 3: Test without tying up accelerators. This is usually the strongest part of the business case. Software-based agents run directly on standard servers and scale horizontally, which means validating your fabric does not require a lab full of scarce, expensive GPUs. For an organization competing for accelerator supply, freeing that hardware for actual training work is not a minor consideration.

Step 3: Test without tying up accelerators. This is usually the strongest part of the business case. Software-based agents run directly on standard servers and scale horizontally, which means validating your fabric does not require a lab full of scarce, expensive GPUs. For an organization competing for accelerator supply, freeing that hardware for actual training work is a major consideration.

Step 4: Make your telemetry trustworthy with precision time. This one is easy to overlook and painful to retrofit. Diagnosing a distributed training stall means correlating events across hundreds of nodes, switch counters, and GPU telemetry streams. If those sources disagree about what time it is, reconstructing the sequence becomes guesswork. At the microsecond scale where congestion events unfold, ordinary NTP drift is enough to make a timeline meaningless. Resilient, GNSS-backed network time from partners like Safran ensures every log, capture, and counter shares one accurate, traceable clock. It is the same foundational control we have written about in the context of Zero Trust architecture, and it matters here for exactly the same reason.

Step 5: Carry the visibility into production. Pre-deployment validation tells you the design is sound. It does not tell you what changed last Tuesday. Network TAPs and packet brokers give your monitoring stack a complete, reliable copy of what is actually traversing the fabric, and platforms like StableNet maintain the live inventory and configuration baseline you will need when someone asks why throughput dropped 12% after a firmware update. Validation is not a gate you pass once: every speed upgrade, node expansion, and serving-stack change shifts the performance profile.

The other half of the bill: inference

Training gets the attention. Inference gets the recurring invoice.

For organizations running models in production, cumulative inference spend eventually exceeds the one-time training investment. Industry estimates commonly place inference at the large majority of lifetime compute cost, and the mechanism is straightforward: per-token costs keep falling, but token volume grows far faster, so the total bill climbs even as unit economics improve.

Inference also fails in ways users feel immediately. Time to first token determines whether an assistant feels responsive or broken. Concurrency limits determine whether the system holds up at 9am Monday. And the bottleneck could be almost anywhere — GPU compute, HBM bandwidth, KV-cache capacity, the PCIe or RDMA interconnect, the model engine, the orchestrator, the load balancer, or the security gateway sitting in front of all of it.

Open-source benchmarks will not find it. They run idealized, uniform workloads. Real users send prompts of wildly varying shape, in bursts, with patterns specific to the industry and application — which is why a model serving legal research behaves nothing like one serving customer support.

Keysight’s KAI Inference Builder addresses this by emulating realistic inference clients at production scale, using prompt libraries modelled on actual user behaviour across verticals including finance, legal, healthcare and academia. It validates the full path through firewalls, load balancers, and smart NICs, across both east-west and north-south traffic. It also correlates client-side metrics with inference engine telemetry such as vLLM statistics and GPU telemetry such as DCGM data in a single time-synchronized view.

That correlation is the point. It turns “inference feels slow” into a specific, actionable finding: the exact concurrency level where KV-cache saturates and time-to-first-token begins degrading past your target. One of those statements you can engineer against.

Getting the foundation right

Canada is committing billions of dollars and years of construction to AI infrastructure. The organizations building it are working to schedules set by funding cycles and power availability, not by how long careful validation takes.

That pressure is precisely why the validation step gets compressed — and precisely why it should not be. Every published MFU figure tells the same story: the gap between theoretical and realized performance is large even for the best-resourced teams in the world. Some of that gap is unavoidable. The portion attributable to fabric design is not, but it is only addressable while the design is still on paper.

Emulation is how you check the assumptions before the capital is committed, and how you keep checking as you scale. Not because testing is inherently virtuous, but because the alternative is discovering your model was wrong after it has been converted into steel, concrete, and fibre.

A few questions worth putting to your team this quarter:

  1. What MFU are we actually achieving during training runs, and have we decomposed the gap between network, memory, and kernel efficiency?
  2. When we deploy new switches, NICs, or nodes, how do we validate performance before production?
  3. If a training job slowed 30% tomorrow, how long would it take us to identify the root cause?
  4. Do we know our inference concurrency ceiling: the measured number, not an estimate?
  5. Are our logs and telemetry synchronized closely enough to reconstruct a congestion event after the fact?

If any of those produce an uncomfortable pause, that is the conversation worth having.

That’s where we can help. Telnet Networks has spent more than 25 years helping Canadian service providers, financial institutions, government agencies, and enterprises build networks that perform under pressure — and our partnership with Keysight extends that work into AI infrastructure validation. Our engineering team can walk through your environment, map where your current testing practice stops short of AI workload behaviour, and arrange a technical session with Keysight’s AI specialists.e stops short of AI workload behaviour, and arrange a technical session with Keysight’s AI specialists.

Planning a GPU cluster deployment or expansion? Contact the Telnet Networks team for a no-obligation consultation.

Further reading: the Canadian Sovereign AI Compute Strategy and the Government of Canada–TELUS sovereign AI announcement; Meta’s engineering write-up on RoCE networks for distributed AI training at scale; the PaLM paper for MFU methodology and comparative figures; Juniper’s Networking the AI Data Center white paper; and Keysight’s white papers on Benchmarking Collective Operations and Improving Scalability in AI Data Center Clusters.

Beyond the “Perfect” Lab: Simulating Real-World Network Chaos Before Deployment

It is the classic IT paradox: your application performed flawlessly in the staging lab, but the moment it was deployed to the branch office or the factory floor, the trouble tickets started piling up.

The disconnect usually isn’t in the code or the hardware configuration; it is in the network conditions. In the pristine environment of a lab, latency is near-zero, packets never drop, and bandwidth is plentiful. In the real world, however, networks are hostile environments filled with jitter, congestion, and varying link qualities.

If you aren’t validating your technology against these imperfect conditions before deployment, you are effectively testing in production—a risk that modern enterprises can no longer afford.

What is Network Impairment Emulation?

Network impairment emulation is the practice of bridging the gap between “lab perfection” and “real-world chaos.”

Unlike a simulator, which uses mathematical models to predict behavior, an emulator is an active device placed “inline” between two points in your test network. It passes live traffic but acts as a programmable bottleneck, selectively injecting faults such as:

  • Packet Loss: Dropping 0.5% of packets to mimic a poor fiber splice or a congested switch.
  • Latency & Jitter: delaying packets to simulate satellite links or 5G backhaul instability.
  • Reordering & Corruption: Flipping bits or arriving out-of-sequence to test error-correction logic.

By introducing these specific, reproducible flaws, engineers can “break” the network in a controlled manner to ensure their applications recover gracefully.

The Two Approaches: Software vs. Hardware

Choosing the right tool often comes down to the Software vs. Hardware trade-off:

  • Software-Defined (e.g., Candela): These solutions prioritize flexibility and scale. They run on standard OS kernels, allowing for massive complexity—simulating thousands of clients or entire mesh topologies. The trade-off is often absolute precision at very high speeds (40G+), where OS overhead can introduce unintended jitter.
  • Hardware-Based (e.g., Aukua): These use FPGA (Field-Programmable Gate Array) silicon to process packets at wire speed. They offer nanosecond precision and zero “self-induced” latency. The trade-off is usually a fixed port count and higher cost per port.

At Telnet Networks, we rely on two primary partners to cover this spectrum: Candela Technologies and Aukua Systems.

Candela Technologies: Wi-Fi Mastery at Scale

Candela Technologies and their LANforge platform are the industry standard for environments that require scale and complexity, particularly in wireless networking. They don’t just emulate the wire; they emulate the clients on the wire.

Why Candela?

Candela shines when you need to answer the question: “What happens if 500 users try to stream video while roaming between Access Points?”

Industry-Specific Capabilities:

  • High-Density Wi-Fi Emulation: LANforge can simulate hundreds of virtual stations (vSTAs) using a single physical radio. This is critical for testing high-density deployments like stadiums or campuses without buying hundreds of physical phones and laptops.
  • TR-398 & RFC 2544 Testing: Candela offers automated scripts for standard performance benchmarking, including the Broadband Forum’s TR-398 standard for Wi-Fi performance.
  • “Home in a Box”: For ISP and consumer electronics testing, Candela provides RF enclosures combined with programmable attenuators. This setup allows you to physically isolate an Access Point and programmatically simulate a user walking away from the router—moving from strong signal to weak signal—to validate roaming handoffs and rate adaptation.

Aukua Systems: Extreme Precision for Ethernet Test & Validation

Aukua Systems focuses on the other end of the spectrum: precision, visibility, and speed. Their MGA and XGA platforms are engineered for environments where a microsecond of delay can mean system failure.

Why Aukua?

Aukua offers a unique “3-in-1” architecture. A single appliance can operate as a Traffic Generator, a Network Impairment Emulator, or an Inline Protocol Analyzer. This versatility is unmatched for troubleshooting: you can impair a link and immediately capture the traffic to see exactly how the protocol failed, all from one interface.

Notable Industry-Specific Capabilities:

  • Automotive Ethernet: As cars become data centers on wheels, testing the internal network is critical. Aukua supports specific automotive standards like 100/1000BASE-T1, allowing engineers to inject faults into ADAS (driver assistance) sensor networks to ensure safety systems don’t crash during a voltage spike or packet drop.
  • 5G O-RAN & Fronthaul: In 5G Open RAN environments, timing is everything. Aukua provides nanosecond-accurate latency measurements and can impair specific protocol messages (like PTP synchronization packets) while letting user data pass through. This allows telecom engineers to stress-test the rigorous timing requirements of 5G fronthaul links.

Summary: Which Tool is Right for You?

If your testing involves complex topologies, Wi-Fi mesh networks, or scaling to thousands of clients, Candela Technologies offers the scriptable, software-defined flexibility you need.

If your focus is on high-speed Ethernet (up to 100G), Automotive compliance, or mission-critical 5G timing, Aukua Systems delivers the hardware-based precision required to validate those strict standards.

Don’t leave your deployment to chance. Whether you need to simulate a noisy Wi-Fi environment or a jittery trans-Atlantic fiber link, Telnet Networks can help you architect the right test solution.

Contact our team today to discuss your lab requirements and see a demo of these platforms in action.

StableNet Telco: The Unified OSS Platform Built for Modern Service Providers

Why Service Providers Across Canada Are Choosing Infosim StableNet for Automation, Visibility, and Operational Efficiency

For Canadian service providers, the pressure has never been greater. Networks are more complex, services are more dynamic, and customer expectations are relentlessly high. Traditional, siloed OSS tools are no longer enough—operators need automation, end-to-end visibility, and seamless integration across their network and service layers.

Infosim® StableNet® Telco, represented in Canada by Telnet Networks, delivers exactly that: a unified, highly automated OSS platform that simplifies operations, reduces costs, and accelerates service delivery. Backed by nearly two decades of engineering excellence, StableNet is used globally by Tier-1/2 carriers, ISPs, MSOs, utilities, and critical-infrastructure operators who demand reliability, scalability, and ease of use.

Why StableNet Telco Matters Right Now

Most telco environments are burdened by fragmented management tools—separate systems for fault, performance, provisioning, configuration, and inventory. This creates operational blind spots, inconsistent data, higher costs, and slower response during outages.

StableNet solves this with a 4-in-1 OSS automation platform, unifying:

All delivered through a single platform, single UI, and single data model.

This means:

  • One tool to operate
  • One database to maintain
  • One licensing model
  • One source of truth for all network assets and services

What Makes StableNet Telco Stand Out?

1. A True Unified OSS—Not a Bundle

StableNet isn’t a collection of stitched-together modules. It’s an integrated architecture built from the ground up using:

  • Service-Oriented Architecture (SOA)
  • A fully unified data model
  • Vendor-agnostic device interaction layers
  • Integrated automation workflows

This eliminates tool silos and enables automation impossible with legacy NMS or multi-vendor OSS stacks.

2. Automated Discovery & Accurate Inventory

StableNet performs deep, multi-layer discovery across physical, virtual, and cloud infrastructures, creating:

  • A real-time, auto-maintained inventory
  • Unified service and device topology maps
  • Automatic detection of configuration drift or unauthorized changes

Accurate inventory is the foundation for everything—from RCA to provisioning to compliance—and StableNet delivers it with exceptional precision.

3. Intelligent Fault Management & Real-Time RCA

One of StableNet’s most valuable features is its automated root-cause analysis (RCA). Instead of overwhelming NOC operators with thousands of alarms, StableNet:

  • Automatically correlates 90%+ of alarms
  • Identifies the actual root cause
  • Enriches alerts with service and customer impact
  • Requires no custom correlation rules
  • Adapts dynamically as the network evolves

This dramatically reduces MTTR and allows operators to prioritize service-impacting issues.

4. Deep Performance Management & SLM

StableNet collects and normalizes performance data from sources such as SNMP, NetFlow, IP-SLA, CDRs, REST APIs, SQL, and more. Operators can actively simulate VoIP or video traffic to measure MOS, R-Factor, and other KPIs.

Highlights include:

  • Configurable KPIs
  • Advanced Service Level Management (SLM)
  • Historical and real-time trending
  • Automated PDF, Excel, and HTML report delivery
  • Capacity and growth planning insights

5. NCCM: Carrier-Grade Configuration & Change Automation

StableNet’s NCCM module provides telcos with robust configuration governance, including:

  • Real-time configuration backup
  • Full version history and rollback
  • Template- and script-based automation
  • Policy-based compliance enforcement
  • Security vulnerability notifications
  • End-of-Life/End-of-Service alerts

This minimizes configuration-related outages, enforces corporate standards, and supports compliance frameworks such as SOX, PCI, and ITIL.

6. Designed for Carrier-Grade Scale

StableNet supports:

  • 60+ vendor families
  • 800+ device models
  • Hybrid, cloud, and virtualized topologies
  • Multi-tenant service provider environments

It is built for national carriers, regional ISPs, utilities, and operators with rapidly growing infrastructure footprints.

7. Built on TM Forum Standards (eTOM, SID, Frameworx)

StableNet aligns with core TM Forum frameworks, enabling seamless integration with OSS/BSS environments and ensuring future-proof architectural compliance.

8. IoT, 5G, and Emerging Technology Ready

StableNet is already deployed across:

  • 4G/5G networks
  • LoRaWAN
  • Sigfox
  • ARM/Intel IoT architectures

The platform provides “any-to-any” visibility across legacy and modern IoT networks—ideal for utilities, smart-city rollouts, and next-gen broadband providers.

Where StableNet Creates the Most Value

Operational Savings (OPEX)

  • Unified toolset reduces workflow complexity
  • Automated RCA reduces MTTR
  • Automated configuration lowers human error
  • Improved network availability reduces SLA credits
  • Lower audit and compliance overhead

Capital Savings (CAPEX)

  • Consolidation of legacy EMS/NMS tools
  • Predictable, device-based licensing
  • Reduced infrastructure footprint
  • Long-term platform stability

Most operators realize ROI in 6–12 months.

Easy to Deploy, Easy to Use

StableNet is known for its practical, low-risk implementation model. Operators can start small—with NCCM, fault, or inventory—and expand to full OSS automation when ready.

Benefits include:

  • Fast implementation cycles
  • Modular adoption paths
  • Integration with existing NOC toolchains
  • Intuitive, operator-friendly user interface
  • Simplified, transparent licensing

StableNet + Telnet Networks: Delivering OSS Excellence in Canada

As the Canadian distributor and professional services partner for Infosim®, Telnet Networks provides:

  • Local technical expertise
  • Design and deployment services
  • Integration with existing OSS/BSS systems
  • Custom KPI and dashboard development
  • Ongoing support and managed services

Our team helps Canadian service providers of all sizes modernize their operational ecosystems with a proven, strategic, and scalable OSS foundation.

Ready to See StableNet in Action?

Telnet Networks offers:

  • Live demos
  • Architecture reviews
  • Proof of Concept deployments
  • ROI assessments and toolset consolidation guidance

Contact us to begin your OSS modernization journey.

Network Visibility: Security Applications of Network TAPs, Brokers and Bypass Switches

Security starts with awareness, but what happens when critical traffic slips through unnoticed? For security teams and network administrators alike, network visibility isn’t just a luxury—it’s a necessity. As threats become more sophisticated, ensuring complete, real-time access to network traffic is the first step in defending against malicious activity. This is where technologies like Network TAPs, Network Packet Brokers, and Bypass Switches come into play.

What is Network Visibility?

Network visibility refers to the ability to monitor all traffic flowing across a network—north-south (between users and data centers) and east-west (between internal systems, users and endpoints). Without it, blind spots emerge, leaving room for attackers to move undetected.

Visibility tools like Network TAPs (Test Access Points), Network Packet Brokers (NPBs), and Bypass Switches are the foundation for building a resilient, secure, and high-performance network. Each plays a unique role in feeding security appliances the data they need to function effectively.

Network TAPs: Your First Line of Insight

Network TAPs (Test Access Points) are dedicated hardware devices designed to deliver a real-time, unfiltered copy of network traffic. Placed in-line between network segments, TAPs allow all data to flow through uninterrupted while simultaneously duplicating that traffic for monitoring and security tools. Unlike other methods that may filter or miss packets under load, TAPs provide a complete and accurate view of every packet traversing the network—ensuring your tools receive 100% of the data, with zero interference, loss, or blind spots.

Security Use Cases:

Intrusion Detection Systems (IDS) rely on clean, complete traffic to detect anomalies.

Forensics and packet capture solutions use TAPs to store traffic for analysis after an incident.

Decryption appliances can tap into SSL/TLS sessions for deep inspection.

Network TAPs are available from vendors like Garland Technology, Cubro, Profitap and Keysight.

Network Packet Brokers: Smart Traffic Management

Gaining visibility is just the first step—managing that traffic effectively is where the real challenge begins. This is where Network Packet Brokers (NPBs) come into play. These smart, purpose-built devices aggregate traffic from multiple sources, then filter, de-duplicate, and reformat it before sending it to your security and monitoring tools. 

By delivering only the relevant data in the optimal format, NPBs reduce tool overload, eliminate unnecessary noise, and ensure that each system receives precisely what it needs to operate at peak efficiency.

Security Use Cases:

Traffic filtering: Send only relevant data to specific security appliances to reduce overload. 

Load balancing: Distribute traffic across multiple tools for redundancy and scalability. 

Packet deduplication and header stripping: Eliminate noise and unnecessary metadata that can bog down inspection.

Bypass Switches: High Availability for In-line Security

Bypass Switches, unlike TAPs and Network Packet Brokers, are purpose-built for in-line security tools—such as firewalls, intrusion prevention systems (IPS), and secure web gateways—that actively inspect and control live traffic. Because these tools sit directly in the path of network data, any failure or maintenance downtime can disrupt the flow of traffic and impact availability. Bypass switches solve this challenge by intelligently redirecting traffic around the in-line device if it becomes unresponsive or needs to be taken offline. This ensures continuous uptime, minimizes risk, and allows security teams to maintain and upgrade in-line defenses without interrupting business operations.

Security Use Cases:

Fail-safe failover: If an in-line appliance fails or is taken down for maintenance, bypass switches keep traffic flowing uninterrupted.

Heartbeat monitoring: Ensure that in-line tools are healthy and responsive.

Scheduled updates and maintenance windows: Perform patching or upgrades without interrupting traffic.

The Power of an Integrated Visibility Fabric

Individually, TAPs, Brokers, and Bypass Switches solve specific problems. Together, they form a visibility fabric—a unified, scalable approach to traffic monitoring that supports both performance and security initiatives.

If you’re struggling with visibility gaps or underperforming security tools, it’s time to rethink your monitoring strategy. Contact the Telnet Networks sales team to learn how we can help you deploy the right mix of Network TAPs, Network Packet Brokers, and Bypass Switches  from market leading and innovative partners like Garland Technology, Cubro, Profitap and Keysight to secure your infrastructure from the ground up.

Troubleshooting Cheat Sheet: Layers 1-3

Any time you encounter a user complaint, whether regarding slow Internet access, application errors, or other issues that impact productivity, it is important to begin with a thorough understanding of the user’s experience.

Not sure where to begin?  User complaints usually fall into three categories: slow network, inability to access network resources, and application-specific issues.

Based upon the complaint being presented you need to understand the symptoms and then isolate the issue to the correct layer of the Open Systems Interconnection (OSI) model.

The following Troubleshooting Cheat Sheet shows the questions to ask with a typical slow network complaint.

What to Ask What it Means
What type of application is being used? Is it web-based? Is it commercial, or a homegrown application? Determines whether the person is accessing local or external resources.
How long does it take the user to copy a file from the desktop to the mapped network drive and back? Verifies they can send data across the network to a server, and allows you to evaluate the speed and response of the DNS server.
How long does it take to ping the server of interest? Validates they can ping the server and obtain the response time.
If the time is slow for a local server, how many hops are needed to reach the server? Confirms the number of hops taking place. Look at switch and server port connections, speed to the client, and any errors.

Quick OSI Layer Review

With these questions answered, working through the OSI model is a straightforward process. When dealing with the different layers, understanding how each layer delivers data and functions will impact how you would troubleshoot each layer.

Physical Layer

  • If it can blind or shock you, think Physical Layer
  • Defines physical characteristics of cables and connectors
  • Provides the interface between network and network devices
  • Describes the electrical, light, or radio data stream signaling

Data Link Layer

  • Converts signals into bits which become the packet data that everyone wants
  • Performs error detection and correction of the data streams
  • Manages flow and link control between the physical signaling and network
  • Constructs and synchronizes data frame packets

Network Layer

  • Controls logical addressing, routing, and packet generation
  • Carries out congestion control and error handling
  • Performs route monitoring and message forwarding

Assessing the Physical Layer

Generally speaking, Physical Layer symptoms can be classified into two groups of outage and performance issues. In most cases, investigating outage issues is the easiest place to begin, as it’s a matter of confirming the link light is out or that a box is not functioning. Additionally, validating equipment failure is a matter of replacing the cable or switch and confirming everything works.

Physical Layer issues are overlooked by people pinging or looking at NetFlow for the problem, when in reality it’s a Layer 1 issue caused by a cable, jack, or connector.

The next step in investigating Physical Layer issues is delving into performance problems. It’s not just dealing with more complex issues, but also having the correct tools to diagnose degraded performance. Essential tools in your tool box for testing physical issues are a cable tester for cabling problems, and a network analyzer or SNMP poller for other problems.

Assessing Physical Performance Errors

In diagnosing performance issues from a network analyzer, you’ll notice that there are patterns common with these errors, which are usually indicative of what’s causing the Physical Layer problem. These can be divided into intelligent and non-intelligent errors.

Intelligent Errors: An intelligent host is smashing into your network signal and corrupting the data.

Example: Overloaded WiFi network or a busy channel.

Non-Intelligent Errors: An outside entity causing noise that interferes with the signal or flow of data across the network.

Example: A microwave interfering with a WiFi signal.

Climbing Further up the Stack

Confirming performance problems, taking a systematic approach to troubleshooting, and understanding how communication occurs across the layers of the OSI model are key to slashing troubleshooting times and improving resolution accuracy.

Year-End Network Monitoring Assessment

Planning for the Future

As we approach the New Year, many organizations’ data centers and network configurations are in lockdown mode. Whether this is due to assuming a defensive posture against the onslaught of holiday ecommerce traffic, or an accommodation to vacationing staff, the situation provides network managers an opportunity to perform a year-end network monitoring assessment

Establish Future Goals, Identify Current Weaknesses and Make Sure Core Tasks and Goals Are Achieved

Q. How many locations will you need to monitor in the New Year?

If there are new server clusters or even new data centers in the works, be sure to plan accordingly, and ensure that your network monitoring tools will have visibility into those areas.  Network Taps can be used to incorporate more points of visibility for your existing monitoring tools within your growing network. Advanced appliances such as Network Packet Brokers (NPBs) can perform more sophisticated switching and filtering to optimize visibility within that network sprawl.

Q. What traffic will you be responsible for monitoring?

If you are providing network support, you need to understand immediately the nature, volume and security of the traffic flowing over your network. Is your organization planning to implement new applications or services on the network? Even the introduction or expansion of virtualization will require a monitoring plan that incorporates Virtual Taps. Additionally using advanced features on a packet broker like load balancing can extend the useful life of existing tools by sharing current traffic across a pool of devices.

Q. What new threats will the network face, and what preventative measures will you add?

The growing phenomena of advanced persistent threats (APTs) and directed attacks against network vulnerabilities demand a stronger response from security personnel. Up to 75 percent of devices within an organization’s network can contain a known security vulnerability. Many organizations deploy a defense-in-depth strategy with overlapping security tools to provide more robust security coverage. Be sure to schedule software updates for all of your network security tools, and make sure those security tools have total visibility of the traffic they are monitoring.

Q. What is your replacement plan for older equipment?

Take inventory of network equipment that have reached end-of-life, end-of-sale or end-of-support.. Budgeting for, and planning ahead for the obsolescence or re-tasking of these devices should be included in your plan for the coming year.

Q. What are your redundancy and failover plans?

One option for extending the useful life of your legacy monitoring tools is to utilize them as redundant tools in case of failover. Utilizing a bypass switch or high-availability modes in NPBs can make use of these tools in the event a primary device is put in maintenance mode, taken offline, or experiences a hardware failure. Consider assessing your older equipment on the basis of discarding the equipment entirely OR re-purposing it as a hot-standby.

Q. Have you included hardware/software maintenance in your annual budget?

Most hardware vendors offer annual maintenance and service plans for their devices. Renewing and maintaining these plans is critical to ensuring that you have access to the latest software updates. Additionally, should any of your devices experience hardware failure, advance replacement plans can get replacement equipment into your network as soon as possible.

Managing Your Application Performance

apmcomponents1 end user monitoring

Are you are planning to implement new IT projects such as data center consolidation, server virtualization, cloud computing, or perhaps adding new applications on the network?  Do you understand exactly how these upgrades will affect the existing applications and the user experience?  What strategy are you going to use to ensure application performance is not compromised?  It is imperative that you understand how each of the components can affect overall application performance.

A Strategy you can use to Manage Application Performance (APM)

Most companies have some sort of visibility of their network systems via the network elements, but a healthy infrastructure does not necessarily mean that your applications are running efficiently because they do nothing to monitor the actual transaction.  There are many different components to APM, so what capabilities do you need to accurately monitor, measure, and troubleshoot?

6 Components of Application Monitoring

Monitoring the End-user Experience is quite different from Infrastructure Monitoring. We actually measure the end-user experience so we see what they see.

apmcomponents1 end user monitoring 
  • Measures response time and availability from the end user’s perspective
  • Aligns performance management with the needs of business users

 

Application Mapping is a separate dimension to APM this takes the different bits about applications and maps them together so you can trace an application and its dependencies/relationships across the network.

apmcomponents2 Application Mapping 
  • Discover application components and their relationships
  • Fundamental for managing an application

 

Transaction Following/Tracing is a critical component is being able to trace transactions on the network through multiple servers that all communicate to make up an application.

apmcomponents3 Transaction Following Tracing 
  •  Follow transactions through application tiers and components
  • Trace performance of each transaction at the code level
  • Holistic view of transactions from end-to-end

 

Deep Application Component Monitoring –  deep inside the system that runs the code allows you to get to the fine detail, and why there may be application issues.

apmcomponents4 Deep Application Component Monitoring 
  • Collect fine-grained metrics from application internals, including code-level performance
  • Produce detail needed for real-time analytics and true root cause analysis
  • Complemented by broad infrastructure performance information

 

Network-Aware APM – is another form of data collection because the network is where applications travel, and the network team usually gets called to solve problems first.

apmcomponents5 Network Aware APM 
  • The network is the backplane of modern applications
  • Understand impact of network performance on application behaviour
  • Provides tracing and visibility for ALL applications

 

Analytics – There is a tremendous amount of information as you collect data across 100’s of applications and perhaps millions of transactions.  You need to collect and store that data efficiently to be able to trend, analyze, and solve problems with it

apmcomponents6 Analytics
  • Store and index large amounts of performance data
  • Automatically extract anomalous behaviour, correlate information, identify the root cause of problems, and predict events and performance trends
  • Reveals valuable information to alert staff and resolve problems faster

 

With these 6 elements covered you will have complete visibility and will be able to effectively manage application performance and minimize user impact going forward.

Learn More Here

Measuring IPTV Quality

If you’re implementing IPTV, it’s important to know what metrics to monitor to ensure the transmission of high quality video. Managing performance entails more than tracking response time. Let’s look at the key metrics for managing IPTV in the following table:

Performance Area Metric Description
IPTV Service Metrics QoE Video quality of experience measured via Media Delivery Index (MDI), most often displayed as two numbers separated by a colon: delay factor (DF) and the media loss rate (MLR)
Packet loss Defined as the number of lost or out-of-order packets per second. Since many receivers make no attempt to process out-of-order packets, both are treated as lost in the MLR calculation. The maximum acceptable value for MLR is zero, as any packet loss will impact video quality.
Jitter Measures the variability of delay in packet arrival times
Latency Time taken by transport network to deliver video packets to user
QoS Verify precedence settings are the same for all components of IPTV transmission
IPTV System Metrics CPU Amount of CPU available and used
Memory Device memory available and used
Buffer utilization Quantity used and available
Network Metrics CIR utilization User utilization relative to Committed Information Rate (CIR)
Queue drops Queue drops due to congestion

 

With a basic understanding of key metrics and the technical specifications from your IPTV solution, you can set thresholds and alarms on metrics to notify you of potential issues before they impact the user.

5 IPTV Monitoring Best Practices

Chances are you’ve seen Internet Protocol TV (IPTV) but didn’t know it. Different types of IPTV are popping up in our daily lives ranging from Video-On-Demand to being greeted by pre-recorded video messages at the gas pump or ATM. And many businesses are adopting IPTV to broadcast live or on-demand video content to employees, partners, customers, and investors.

But what does this mean for the network team? In this article we’ll outline IPTV basics and focus on primary management challenges and best practices.

IPTV Basics

In regards to your company, IPTV simply means it has systems in place that can send, receive, and display video streams encoded as IP packets. IPTV video signals can be sent either as a unicast or multicast transmission

  • Unicast: involves a single client and server in the process of sending and receiving video and communication transmissions. Video-on-Demand is a great example of this.
  • Multicast: the process of one party broadcasting the same video transmission to multiple destinations. An example would be a retail chain broadcasting the same video to kiosks in all their stores.

Monitoring Challenges & Best Practices

Implementing best practices can ensure IPTV runs smoothly on your network and performance issues are minimized. As IPTV is deployed, make sure your team is doing the following:

  • Get Visibility, Get Resolution: To ensure video quality, monitor at several points along the video delivery path: headend (point of origin), core, distribution, access, and user/receiver. Critical for capturing accurate metrics and isolating problem source.
  • Minimize Delay and Packet Loss: IPTV video quality can often be compromised by small variations in delay or any significant packet loss. Track, baseline, and alarm on IPTV metrics to proactively identify issues.
  • Avoid Bandwidth Surprises: Transporting video across IP infrastructure consumes considerable bandwidth. Monitor regular use to avoid exceeding thresholds and assist in capacity planning. Reduce the impact of outages by confirming backup network paths have required capacity to carry video.
  • Don’t Monitor in a Vacuum: Confirm your existing performance monitoring tools can track IPTV traffic and metrics alongside existing applications. Incomplete performance views will cause your team to waste time attempting to guess IPTV performance or the impact of other applications on IPTV transmissions.
  • Play Nice with the Video Group: On a converged network, troubleshooting any video issue will involve working with the video group. Attempt to establish processes for coordinating troubleshooting efforts, before problems occur.

This article serves as a starting point for understanding IPTV performance challenges and best practices to implement for ensuring success. For more in-depth information on the technologies, critical network preparations, and IPTV monitoring metrics, check out the following resources: