A Deep Dive into Network Troubleshooting

In today’s digital-first world, where speed and precision are paramount, even a slight dip in application performance can create significant disruptions. In enterprise environments, where operations depend on software performance and seamless communication, slow application speeds aren’t just an inconvenience; they can be a roadblock to productivity. When users begin to experience slow performance, whether in internal systems or client-facing applications, the issue must be resolved quickly to avoid cascading effects that compromise business operations. Troubleshooting such problems often requires a multi-pronged approach: the right combination of expertise, diagnostic tools, and a methodical investigation process.

In one particular case, slow TCP retransmissions were identified as the cause of application lag. This specific scenario opened a window into the depths of networking intricacies and led to an exciting, albeit challenging, investigation to pinpoint the root causes. This article will break down the systematic troubleshooting steps, infrastructure analysis, and strategic thinking that ultimately solved the issue of sluggish application performance.

The Search for Clarity: Identifying the Problem

The initial symptoms were clear: the application was sluggish, but it was only observed in remote branch locations. In particular, a wireless point-to-point bridge that connected one of the branches to the headquarters was experiencing issues, while users at the main headquarters did not report any significant degradation in performance. This immediate disparity raised critical questions about the source of the problem. Was it specific to certain locations, or was there a more universal issue affecting the network?

The application in question was a barcode inventory system that relied heavily on TCP communications, which created an immediate area of focus. Initially, the slowness was noted only on one protocol—the Telnet protocol pathway. On the other hand, the fat client protocol that communicated over TCP port 4003 appeared relatively unaffected, which quickly narrowed down the problem to specific communication channels.

At this point, two other environmental changes were discovered: the recent deployment of an Application Delivery Controller (ADC) and the introduction of high-bandwidth video traffic. These changes immediately jumped out as possible culprits, especially given that bypassing the ADC improved performance and the video traffic seemed likely to overload available bandwidth. This created a perfect storm of factors that could potentially strain the network and cause issues that were only noticeable in remote locations.

Infrastructure and Network Configuration Analysis

The next step in troubleshooting was understanding the full topology of the network and how it interacted with the application environment. The infrastructure, at its core, included a backend SQL server setup housed in the data center of the customer’s headquarters, alongside three front-end servers that supported application traffic. These servers were distributed behind two ADCs operating in high-availability mode, with the goal of load balancing and improving network performance.

The issue became more interesting when the remote branches, which were connected via a wireless point-to-point bridge, exhibited performance degradation. This bridge, although relatively high-performing, had a bandwidth limit of only 54 Mbps and a latency of around 2ms. While these numbers did not indicate severe bandwidth limitations or high latency, it was important to analyze the finer details of the connection. Interestingly, the connection showed 2% packet loss, which was a significant enough detail to suggest that network performance was being impacted.

Though the latency and bandwidth seemed sufficient for most types of communication, this subtle packet loss highlighted the possibility that network congestion or packet retransmissions were at play. This issue, though seemingly minor, could cause delays and performance bottlenecks, especially with protocols like Telnet, which rely heavily on stable and fast network connections. The investigation now needed to look deeper into the effects of packet loss and the potential role of the ADC and video traffic in exacerbating these issues.

The Hypotheses: Analyzing the Usual Suspects

Given the symptoms observed, the investigation quickly narrowed down the most likely sources of the performance issues: the Application Delivery Controller (ADC) and the newly added video traffic. Let’s break these down further:

1. Application Delivery Controller (ADC)
   The ADC was introduced as part of a system upgrade with the goal of improving the distribution of traffic across the various servers. While ADCs are often a vital component in optimizing traffic flow, they can also introduce overhead if misconfigured or overloaded. Since bypassing the ADC temporarily resolved the performance issues, it raised an immediate red flag. ADCs work by intercepting network traffic and deciding which server should handle the request. However, if the load balancing algorithm is inefficient or if the ADC is not scaled properly to handle the amount of traffic it’s receiving, it can create delays that ripple through the network. Additionally, any misconfiguration in routing rules or even a firmware bug could result in longer-than-expected transmission times and application lag.
   
   
2. Video Traffic
   Another significant factor that quickly emerged as a suspect was the recently introduced video traffic on the network. Video traffic, particularly high-bandwidth streams, can saturate the available bandwidth, especially if the network is not designed to handle bursty traffic. In enterprise networks, this is particularly problematic as video content requires consistent bandwidth to prevent buffering, stuttering, or delays. The addition of video streaming over the WAN links placed extra stress on the network, exacerbating any already existing limitations on bandwidth. This could also lead to congestion at certain points in the network, resulting in delayed packets or even packet loss, which is crucial when troubleshooting TCP performance. Given the application’s reliance on stable connections, any disruption in network stability caused by video traffic could have a direct impact on performance.
   

Deep Dive: Isolating the Network Components

To further refine the analysis, the investigation focused on each segment of the network infrastructure separately, isolating potential failure points to understand how each component could be contributing to the overall performance issues.

1. Wireless Link Performance
   Although the wireless point-to-point bridge initially seemed sufficient, it was essential to perform deeper diagnostics. Tools like Wireshark and packet analyzers were employed to assess packet loss, jitter, and retransmissions at the link level. While the connection was stable with relatively low latency, the packet loss of 2% was still enough to affect performance over time, particularly for TCP-based communications. Analyzing the error rates and retransmission counts revealed that the network was dropping packets intermittently, which in turn led to the application’s slower performance. These dropped packets forced the system to initiate retransmissions, causing delays in the application response times.
   
   
2. ADC Configuration and Load Balancing
   The next step was investigating the ADC. Analyzing traffic logs and performance metrics showed that the ADC was indeed introducing delays in routing packets between the frontend and backend servers. While the ADC was designed to balance traffic efficiently, it appeared that the algorithm wasn’t performing as expected under load. This was likely caused by either insufficient resources allocated to the ADCs or a configuration issue in how the traffic was distributed. In some cases, the ADC had to re-route traffic multiple times due to incorrectly balanced loads, which led to increased latency and poor performance for remote locations.
   
   
3. Impact of Video Traffic on WAN Links
   Given that the network was now handling high-bandwidth video traffic, the WAN links were under significant stress. A detailed bandwidth usage analysis indicated that during peak times, video traffic occupied a large portion of the available bandwidth. This led to congestion, especially in the uplinks between remote branches and the headquarters. The result was that critical application traffic, such as the barcode inventory system’s Telnet protocol, experienced delays as the network struggled to prioritize between video and application data. QoS (Quality of Service) policies were investigated to ensure that business-critical applications received higher priority over less time-sensitive traffic like video.    
4. The Final Solution: Addressing the Root Causes

After thorough analysis and investigation, several adjustments were made to resolve the performance issues:

1. Optimizing ADC Load Balancing
   The load balancing algorithm was fine-tuned, and additional resources were allocated to the ADCs to better handle the traffic distribution. This improved performance by reducing delays caused by inefficient routing.
   
   
2. Improving Network Performance
   The wireless link’s configuration was optimized to reduce packet loss, including increasing the link’s throughput by tweaking various transmission parameters. Furthermore, redundant wireless paths were set up to increase reliability.
   
   
3. Traffic Shaping and QoS Policies
   Quality of Service (QoS) policies were configured to prioritize application traffic, such as Telnet, over non-essential video traffic. This ensured that the network’s limited bandwidth was allocated effectively, reducing congestion and improving the overall user experience.
   

The investigation into the slow application performance was a complex, multifaceted problem that required a strategic, methodical approach to solve. By focusing on key areas of the network infrastructure—ADC configuration, wireless link performance, and bandwidth management—it was possible to pinpoint the root causes and implement solutions that restored optimal application performance. This case illustrates the importance of comprehensive network analysis and highlights how seemingly small factors, such as packet loss or misconfigured load balancing, can have a disproportionate effect on overall network performance. By employing the right tools and techniques, organizations can overcome application performance challenges and ensure that their systems run smoothly, even under complex and demanding conditions.

The Case Unfolds: Discovering the Network Topology

In the quest to resolve network inefficiencies and performance degradation, a meticulous analysis of the underlying network infrastructure is essential. Understanding how traffic flows, where potential bottlenecks lie, and the interplay of network layers is crucial in pinpointing the root causes of problems. The investigation in question demanded a thorough mapping of the network’s physical and logical structure to understand how data traverses from one location to another. Equipped with an arsenal of monitoring tools, including Cisco switches with capabilities like SPAN (Switched Port Analyzer), RSPAN (Remote SPAN), and a variety of advanced diagnostic tools, I began the journey of unraveling the complexities of this network environment. The primary aim was clear: to identify inefficiencies, minimize latency, and optimize the network’s overall functionality.

Layer 1 and Layer 2: Physical Network Setup

The first step in this extensive investigation involved a keen focus on the physical aspects of the network — specifically Layers 1 and 2 of the OSI (Open Systems Interconnection) model. These layers form the bedrock of the network infrastructure, and a detailed understanding of their structure is critical to comprehending how data is transmitted across the physical medium. By charting the physical network topology, we could visualize how devices, cables, and switches interact and communicate.

A key component of the HQ infrastructure was the ADC (Application Delivery Controller), which served as the central load balancer for traffic destined for the front-end application servers. This ADC was interconnected with three front-end application servers via a robust 1Gb connection, ensuring high-speed communication between the devices. Despite being located on different ESXi hosts, the application servers were situated within the same Layer 2 network, specifically VLAN 4 (with a subnet range of 172.16.4.X/24). The reason for placing the servers within the same Layer 2 domain was to ensure seamless data transfer and communication between the load balancer and the servers. With a shared VLAN, the ADC could quickly forward requests to the appropriate server without introducing unnecessary complexity or delays.

But it wasn’t just the switches and cables that were being analyzed; the network’s VLAN structure was crucial. Each VLAN, by definition, is a logical division of the Layer 2 broadcast domain that facilitates traffic segmentation within the physical network. By meticulously mapping out the different VLANs, I could ensure that traffic was being routed appropriately without crossing boundaries unnecessarily, which could introduce inefficiencies and potential security risks.

Layer 3: The Wide Area Network

Once the physical infrastructure was accounted for, the investigation transitioned into Layer 3, which focuses on routing and the overall path traffic takes across the network. This is where the WAN (Wide Area Network) came into play, as the remote branch and the HQ data center were connected via a point-to-point wireless bridge. The wireless bridge, while providing the necessary connection between the two locations, had its limitations, particularly in terms of bandwidth. Operating at a relatively slow 54Mbps, the wireless bridge introduced a potential bottleneck in the communication flow, especially when traffic loads increased.

Interestingly, the configuration of the network required the application servers’ default gateway to point to the ADC’s self-IP address. The ADC, in turn, acted as the intermediary that forwarded packets to the firewall for further routing. This design added a layer of complexity, as it meant traffic had to traverse the ADC before reaching the destination servers, potentially causing delays and affecting the overall performance of the network.

The wireless link between the branch and HQ was particularly concerning. Although the latency remained relatively low at 2ms, packet loss was another issue. The packet loss rate of 2% was consistently measured over several tests, which, although seemingly modest, could have significant repercussions, especially in real-time applications that require minimal latency and a high degree of data integrity. Video conferencing or VoIP calls, for example, could suffer noticeably under these conditions, as small packet losses would translate into choppy communication or even dropped calls.

Latency and Packet Loss Testing: Identifying Network Inefficiencies

To quantify the effects of the network’s potential vulnerabilities, a series of latency and packet loss tests was executed. The goal was not only to identify issues that could be affecting the performance of the network,  but also to understand how these issues compounded when the network was under load.

Latency, in the context of networking, refers to the delay between sending and receiving data packets. In this case, the wireless bridge presented a relatively low latency of 2ms. For most use cases, this amount of latency would be acceptable, especially for non-real-time applications. However, as data traffic increased, the likelihood of encountering performance degradation also grew. The low latency could become a non-factor if the network experienced substantial packet loss.

Packet loss, though seemingly a trivial issue when examined in isolation, has the potential to cause serious disruptions. A loss of just 2% of packets over a sustained period could lead to noticeable delays, especially in latency-sensitive applications such as Telnet or video streaming. Even small delays can compound over time, affecting user experience and overall productivity. For real-time applications, this becomes even more critical, as every lost packet equates to a drop in data quality. For instance, in video conferencing, this could result in pixelated video or disrupted audio, leading to frustration among participants.

The packet loss was found to be a consistent issue during periods of high utilization on the wireless link. Even though 2% packet loss may appear minimal in an idealized scenario, the cumulative effect over time would be substantial. As more data is transferred, more packets are lost, and the degradation in service quality becomes evident. This was a clear indication that the network was underperforming and needed further attention.

Wireless Bridge: The Critical Link

The wireless bridge, while providing the necessary connectivity between the branch office and the HQ data center, became the focal point of the network’s inefficiencies. Its relatively low bandwidth was a significant limitation, especially when considering the amount of traffic that needed to traverse the link.

Wireless bridges are often chosen for their convenience and cost-effectiveness, but they come with trade-offs in terms of bandwidth and reliability. The 54Mbps speed of the wireless bridge was simply not sufficient to support the data-heavy applications running across the network, especially as video traffic and remote collaboration tools began to take a more central role in daily operations. While it served its purpose, it became evident that the wireless bridge was a bottleneck that required attention.

Given the importance of real-time applications in modern business, this setup was no longer sustainable. The 2% packet loss, though seemingly minor, had an outsized effect on communication applications. Additionally, latency, while low in isolation, is omcompoundedith the packet loss, leading to a degraded user experience.

Possible Solutions and Future Optimizations

Upon diagnosing the network’s weaknesses, the next logical step was to consider possible solutions. Several areas required improvement, from the wireless bridge itself to the routing configurations.

One potential solution was to upgrade the wireless bridge to a higher-bandwidth solution, capable of handling the increasing demand for data throughput. By implementing a bridge with a bandwidth of 100Mbps or more, the network would be better equipped to handle the increasing amount of traffic, reducing congestion and improving overall performance.

Additionally, revisiting the network design for better redundancy and failover could help mitigate the impact of packet loss. A dual-path configuration, perhaps using fiber optics or a higher-speed wireless bridge, would ensure that there was an alternative path available should one link fail or degrade. In particular, employing advanced error-checking mechanisms could help recover lost packets, preventing performance issues from escalating.

Another recommendation was to implement Quality of Service (QoS) policies to prioritize latency-sensitive traffic, such as voice and video calls, over less critical data. By assigning higher priority to real-time applications, the network could ensure that these services continued to function optimally, even during periods of congestion.

Lastly, continuous monitoring and testing were essential to ensure that any new changes made to the infrastructure were delivering the desired improvements. Using advanced monitoring tools like Cisco’s SPAN and RSPAN could help provide real-time insights into network performance, allowing administrators to identify and address issues as they arise.

In conclusion, by conducting a thorough analysis of the network topology and performing latency and packet loss tests, we were able to uncover critical vulnerabilities in the network’s design. The wireless bridge, with its limited bandwidth and higher latency, was identified as a major contributor to the issues affecting the network. By upgrading the wireless bridge, revisiting the routing configurations, and implementing better redundancy and error-handling mechanisms, the network’s performance could be significantly improved. Moreover, by continuously monitoring the network, we could ensure that these optimizations remained effective over time, creating a more robust and reliable network infrastructure.

Testing Methodology: Gathering Crucial Data

In the intricate world of network optimization and troubleshooting, data is paramount. Armed with a thorough understanding of the network topology and potential pain points, the next step was to execute the testing phase. The goal of this phase was clear: identify where performance bottlenecks were occurring and diagnose what might be causing delays or retransmissions in the system. Capturing traffic at critical junctures along the data path is a proven method for uncovering the underlying issues that could impact performance, whether it’s slow response times, high latency, or unexpected packet losses.

This process would rely heavily on the power of packet capture and detailed traffic analysis to track down the root causes of inefficiencies. Each packet that flows through a network contains valuable information that can help pinpoint trouble spots, from congestion points to network misconfigurations. By meticulously gathering and analyzing the data at strategic locations, it was possible to uncover the bottleneck and take steps to remedy it, ensuring that the network would perform optimally moving forward.

Defining the Test Plan: Strategic Data Collection

To begin, I crafted a comprehensive test plan that involved capturing traffic at four distinct and strategically selected locations across the network. These points were specifically chosen because they offered visibility into different stages of the data flow and would provide insight into various segments of the path between the client and the application server. With this plan, I could analyze the entire journey of the data, from the client-side interactions to the server-side processing, and everything in between. The points of capture were carefully chosen to offer the most relevant data without overwhelming the process with unnecessary or redundant information.

Capture Point A (HQ Data Center – Client-Side before WAN)

The first capture point was situated on the client side before the traffic entered the Wide Area Network (WAN). This was a critical starting point, as it allowed me to observe the exact traffic being sent by the client. At this stage, there were no external influences—no firewall inspection, load balancing, or other network devices intervening. The data here would provide a clear baseline of the information the client intended to transmit, offering a snapshot of the initial request and the raw packet data before any network delays, retransmissions, or routing complexities were introduced.

By capturing this traffic, I could confirm whether there were any issues originating from the client itself, such as poorly formed packets, excessively large data payloads, or any signs of poor application design. This would set the stage for the rest of the tests, helping to determine whether the issue lay with the client, the network, or the server-side infrastructure.

Capture Point B (HQ Data Center–Server–Side before Firewall)

The second capture point was placed on the server-side, just before the traffic was inspected by the firewall. This location was crucial because it captured the traffic right before it encountered any security filters or inspection processes. At this point, the data had already crossed the WAN and entered the local data center, where the firewall would begin its work.

Analyzing traffic here offered insight into the first significant transition point in the data flow, where security mechanisms could potentially introduce delays. If there were any signs of packet loss or delays, I would be able to pinpoint if the firewall was responsible. This stage also allowed me to examine whether any routing issues within the data center were causing inefficiencies, such as the misrouting of packets or unnecessary network hops that added to the overall latency.

Capture Point C (HQ Data Center–Server–Side Post-Firewall)

After the traffic had been inspected by the firewall, it entered the Application Delivery Controller (ADC) systems, which were responsible for load balancing and managing traffic to the application front-end servers. Capturing traffic at this point, post-firewall but before it reached the ADC, provided insight into how the system was handling the data after security checks had been performed.

The ADC is a critical component in the application delivery pipeline, as it optimizes the distribution of requests to different application servers, balancing the load to ensure that no single server becomes overwhelmed. However, it’s also a point where delays can be introduced if not properly configured or if the system is overloaded. Analyzing traffic after the firewall but before the ADC allowed me to isolate potential delays or inefficiencies in the load balancing process, as well as any network-related issues that may be hindering the smooth operation of the ADC systems.

Capture Point D (Branch Office – Client-Side before WAN)

The final capture point was located on the remote branch office side, capturing traffic before it entered the WAN. This remote location was significant because it provided insight into how the network was performing from the perspective of a client situated far away from the data center. Often, branch offices face unique challenges related to network performance, such as lower bandwidth, higher latency, or congestion at the WAN gateway.

This capture point offered a comprehensive view of how network conditions in the branch office could impact the traffic, potentially adding delays before the data even began its journey through the WAN. It allowed me to determine whether the performance issues were stemming from the client-side network at the branch or if they were related to issues further along the path, such as WAN congestion or problems within the data center.

By capturing data from these four strategic points, I was able to gain a holistic view of the network’s performance, pinpointing where the bottleneck or delay occurred. Each capture point provided crucial context for interpreting the data, helping to narrow down the root causes and focus on the most critical areas for improvement.

Test Execution: Comparing ADC to Direct Server Access

With the testing points established, I proceeded to execute the tests themselves. The goal of the tests was to compare two distinct scenarios—accessing the application via the ADC versus connecting directly to one of the front-end servers. This would help to determine if the ADC was contributing to performance degradation or if the issue lay elsewhere in the infrastructure.

Client A (ADC Virtual Server Access)

The first test involved Client A, which was configured to connect to the application through the ADC virtual server address (172.16.4.106). In this setup, the traffic passed through the ADC, which was responsible for distributing the requests across multiple application servers. This architecture is common in large-scale deployments, where load balancing is essential to prevent overloading a single server and to improve fault tolerance.

I focused on capturing the login times, the traffic flows, and the response times as the data passed through the ADC. By analyzing this scenario, I could assess how well the ADC was performing in terms of load balancing, the speed of request routing, and how much latency was added during the traffic’s journey through this intermediary layer.

Client B (Direct Server Access)

The second test involved Client B, which was configured to connect directly to one of the application front-end servers (172.16.4.107). This test offered a more straightforward path from the client to the server, bypassing the ADC entirely. The goal was to compare the login times and overall network performance between a direct connection and one that went through the ADC, offering valuable insights into whether the ADC was introducing delays or whether the issue lay elsewhere in the network.

By executing both tests concurrently and capturing the relevant data at all the designated capture points, I could directly compare the two approaches and understand how each configuration impacted the performance of the application. Any differences in latency, packet retransmissions, or response times could provide crucial clues about the source of the bottleneck.

Pinpointing the Bottleneck

The packet captures, combined with the test scenarios, provided a wealth of data to analyze. By reviewing the captured packets and measuring the differences between the ADC-based access and direct server access, I could identify where performance degradation was occurring. Whether it was due to load balancing inefficiencies, network congestion, firewall filtering, or some other underlying issue, the data gathered from the tests would help pinpoint the exact cause.

Ultimately, the goal of this testing methodology was not only to troubleshoot the performance issues but also to provide actionable insights that could inform optimization efforts. By understanding where the bottlenecks lay, I could work with the network and server teams to make targeted adjustments, ensuring that the network performed at its best and delivered a smooth, seamless experience for users across all locations.

 Analyzing the Data: Uncovering the Cause of Slow TCP Retransmissions

In any network troubleshooting exercise, data analysis plays an indispensable role in uncovering the root cause of performance issues. For the barcode inventory system in question, the analysis of various network tests and packet captures revealed a striking pattern of inefficiency and delay, particularly related to the use of an Application Delivery Controller (ADC). By carefully examining the results from a series of tests, we were able to identify specific anomalies in the traffic flow and pinpoint the cause of the slow TCP retransmissions that were plaguing the system’s performance.

Test Results: The Evidence Speaks

When analyzing the test results, the disparity between the two client configurations—Client A, using the ADC, and Client B, accessing the server directly—was glaring. Client A experienced significant delays, with login times soaring to 191 seconds, a vast contrast to Client B’s remarkably quick response times, which only ranged between 2 and 3 seconds.

The data from the packet capture analysis painted a vivid picture of what was happening at various points within the network. The primary goal of these tests was to identify factors influencing the performance degradation, particularly focusing on the behavior of TCP connections, retransmissions, and packet loss.

The ADC tests were the standout, with packet captures indicating a notably high incidence of retransmissions and packet loss. This raised the immediate suspicion that the ADC might be introducing delays or misconfiguring packets, leading to the need for retransmissions and, ultimately, slow login times.

From the client-side capture at Capture Point D, it was interesting to note that there were no retransmissions observed. This observation reinforced the notion that the ADC was indeed acting as a proxy for the client-server connection, managing the traffic between them. The absence of retransmissions on the client side suggested that the bottleneck and issues were introduced by the ADC during its processing of the data before it reached the server.

Retransmissions and Lost Segments: Identifying the Culprit

The packet capture analysis revealed an important trend: retransmissions and lost segments were disproportionately high in the ADC tests, particularly at server-side capture points B and C. These issues were critical in understanding the broader network problems and performance issues.

Retransmissions occur when packets are lost or delayed in transit, requiring the sender to retransmit them in an attempt to complete the communication. This is particularly evident when the network is experiencing congestion or faulty hardware. In this case, the ADC was likely introducing delays, either due to over-processing or because of misconfigurations, which led to packet loss and retransmission.

The high rate of lost segments was equally concerning. Packet loss is a sign of congestion, network instability, or insufficient bandwidth, which often leads to delays in transmission. The high percentage of lost segments observed in the ADC tests strongly pointed to the possibility that the ADC was not handling the traffic in an optimal manner. This was evident when looking at the number of segments the ADC failed to forward correctly, resulting in subsequent retransmissions that significantly contributed to slow login times.

Another layer of the issue revealed itself upon further inspection of the network topology. The ADC was not simply a passive agent but was actively managing and controlling the client-server traffic. As a result, any misconfigurations within the ADC would lead to broader disruptions throughout the communication process, resulting in the poor user experience that was being observed on Client A.

Packet Sizes: Small Packets, Big Issues

Another key observation was the noticeable discrepancy in the packet sizes between the two test scenarios. In the case of Client B, which accessed the server directly, the packets were consistently close to the maximum segment size (MSS) of 1380 Bytes. This indicates that the server was sending packets as efficiently as possible without unnecessary fragmentation. On the other hand, Client A’s packets, which were passing through the ADC, were significantly smaller in size.

This difference in packet sizes suggests that the ADC might have been segmenting the traffic in ways that introduced inefficiencies. TCP segmentation refers to the process of breaking down large chunks of data into smaller packets for transmission across the network. While segmentation is a necessary process to ensure data can traverse the network correctly, excessive segmentation can result in overhead, increasing the number of packets that need to be sent and creating inefficiencies that burden the network.

If the ADC were breaking down the packets into smaller pieces than necessary, it would lead to additional processing overhead and greater network traffic, exacerbating the congestion and delays that were already affecting the communication. The fact that these smaller packets were observed only in the ADC scenario further confirmed that the ADC’s handling of the traffic was contributing to the delays and performance degradation.

TCP Retransmissions: The Smoking Gun

The most compelling evidence came from the TCP retransmissions. The number of retransmissions observed in the ADC tests was disproportionately high, confirming that the ADC was, indeed, a significant factor in the slow performance. Retransmissions happen when packets fail to arrive at their destination within the expected time window, prompting the sender to resend them. This not only increases network traffic but also introduces significant delays, especially when the retransmitted packets are large or when there are multiple retransmissions for the same segment.

Retransmissions are often caused by network congestion, insufficient bandwidth, or issues with the underlying infrastructure. In this case, the retransmissions observed in the ADC tests indicated that the ADC was struggling to handle the traffic efficiently. The device was either unable to properly forward the packets to the server or was misconfiguring the connections in such a way that the server failed to receive the packets promptly, prompting the need for retransmissions.

This was a clear sign that the ADC was introducing unwanted delays and inefficiencies into the communication process, slowing down the login times for Client A and causing significant frustration for end users.

Conclusion

Upon reviewing the data and conducting a thorough analysis, the cause of the slow performance in the barcode inventory system became evident: the ADC and its impact on TCP communication were the primary culprits. The introduction of the ADC into the network path had added layers of processing that significantly slowed down the communication process, resulting in the high latency and poor user experience that were observed.

The ADC’s handling of the traffic, including its failure to properly configure packets, excessive segmentation, and high retransmission rates, contributed directly to the delays. The network’s wireless bridge, although not completely innocent, did not appear to play a significant role in the problem, as it only introduced minor additional delays.

By identifying the root cause—specifically, the ADC’s interference with the TCP communication—we were able to propose effective solutions to address the issue. Adjusting the ADC’s configuration, improving its processing efficiency, and optimizing packet handling would go a long way toward resolving the issue and improving the overall performance of the barcode inventory system.

The results of this investigation serve as a testament to the importance of data-driven troubleshooting. Without the careful collection and analysis of test results, the performance issues may have remained unresolved, leading to continued frustration for end users. By following a systematic approach to testing, packet capture, and analysis, we were able to uncover the hidden causes of the problem and implement effective solutions.