Flexibility, scalability andsecurity

THE OF INFORMATION TECHNOLOGY SYSTEMS An Official Publication of BICSI January/February 2014 l Volume 35, Number 1 data center Flexibility, scalability andsecurity plus + The Next Five Years in AV + Measuring Low Loss Optical Networks + Pros and Cons of the Fat-Tree

If the OTdR s auto event analysis algorithm is inaccurate, the user must revert to manual trace analysis using cursors across the trace. Improving Measurement Accuracy in Low Loss Optical Fiber Networks By Mark Miller and dr. Fang Xu All optical fiber networks contain loss events that can be challenging to locate and measure. These challenges are greater in networks that have low end-to-end insertion loss, such as data center and enterprise networks. These networks tend to have added measurement complexity thanks to network designs that include short jumpers that result in closely spaced connectors. Higher network speeds drive the need to ensure low loss and reflectance. High optical fiber density results in a need for auto event analysis to cope with the large number of optical fibers that need to be tested quickly and accurately. 18 u The Journal of Information Technology Systems

When two or more events are closely spaced, the particular combination and sequence of events can be challenging to analyze for most OTdRs today. Performance Criterion Matched (or found) event rate False event rate Pass rate Description Should be as high as possible, with 100% representing that every event has been found. Often due to noise spikes where the OTDR reports an event that is not present. This should be as low as possible. Every OTDR is subject to showing false events. Represents the repeatability of the measurement. This should be as high as possible, indicating pass or fail consistently when a network is tested multiple times. FIgURE 1: A typical OTDR trace. TABLE 1: Criteria for comparing accuracy and reliability of OTDR even results. What are Events? Events are disruptions to the light path within an optical fiber, caused by connectors, fusion splices, optical splitters or kinks/tight bends that induce macrobends. These events leave characteristic footprints on a trace acquired by an optical time domain reflectometer (OTDR). This trace is a graphical representation of the light loss and reflected along the entire length of the optical fiber link under test (see Figure 1). The Importance of Accurate Event Analysis Traditional Tier 1 loss measurements, using an optical light source and optical power meter, are less effective for low loss networks due to the inherent inaccuracies of this measurement technique. New methods, such as using encircled flux compliant light sources, can increase accuracy. However, Tier 1 measurements only provide the total loss value and cannot locate the position of faults. In addition, the constant reuse of jumper cables causes their connector loss to degrade over time. Only an OTDR can provide all of the required measurements needed to effectively qualify and troubleshoot these networks. OTDRs provide information on: u Location. u Event type. u Loss. u Reflectivity. These Tier 2 OTDR measurements are required by a number of ANSI, BICSI, TIA and ISO/IEC standards. OTDR users assume that the displayed event information (e.,g., loss, event location) are accurate. In fact, OTDR event analysis is often far from perfect and results cannot be entirely trusted. Unfortunately, many OTDRs simply do not find all the events or they find and report events that are not really there (e.g., false events). Evaluating Event Analysis performance Unlike most other OTDR performance specifications, such as dynamic range and dead zone, there are no industry standards to define how to evaluate event analysis performance. To evaluate the performance of various event analysis methods, a set of criteria was established to compare the accuracy and reliability of event results (see Table 1). Another tool needed to optimize the event analysis algorithm is a set of optical fiber networks, representative of various field applications, with known events. These golden networks are built in a laboratory setting and constructed to provide many different event scenarios for testing OTDR performance. For a complete analysis, several networks were built: u Multimode and singlemode optical fiber networks of various lengths. Januaury/February 2013 t 19

A FIgURE 2: An example of a golden network constructed to provide many different event scenarios for testing OTDR performance. TECHNIqUE... LIMITATIONS Derivative...Sensitive to noise produces false events Linear regression...challenging to determine segment start and end Correlation... No universal model of events to run correlation Event magnitude*... Accuracy depends on distance; noise increases as event location is further form launch User setting event threshold... Extra user intervention. Highly dependent on user s knowledge and skill *The greatest weakness of event magnitude algorithms is that there has to be a trade-off between increasing matched event rate and the false event rate. B TABLE 2: Limitations of techniques used for identifying OTDR events. FIgURE 3: Deficiencies become more severe as events get close enough to merge together due to the OTDR s dead zone. u u u u Known reflective events with different loss and reflectivity values. Non-reflective events with known loss. Macrobend induced loss points. Events with various spacing, especially closely spaced events (i.e., connectors) representing patch panel applications. Special attention was given to the closely spaced events. When two or more events are closely spaced, the particular combination and sequence of events can be challenging to analyze for most OTDRs today. An example of a golden network is shown in Figure 2. This network has a total of 12 events, including the launch cable. Section G3 contains five embedded events, and section G4 contains two embedded events. Event Analysis Challenges An OTDR s auto event analysis uses specific software to find and measure events. This software should allow for consistent interpretation of events, regardless of user expertise. If the OTDR s auto event analysis algorithm is inaccurate, the user must revert to manual trace analysis using cursors across the trace. This is time consuming, requiring highly skilled labor and is subjected to interpretation and human error. The challenges to produce accurate auto event analysis include: u Incomplete knowledge of the network being tested not able to optimize settings. u Time required to analyze a trace improved accuracy cannot significantly impact test times. u Closely spaced events and event sequence making it difficult to quantify each event: - Connector followed by connector. - Connector followed by splice. - Splice followed by connector. u Accurate determination of the start and end of the optical fiber under test. u Small event magnitude difficulty to separate from noise. OTDRs commonly have problems separating closely spaced events. Even when the OTDR s dead zone is short enough to show these events in the trace, the event analysis will often not detect all of the events. After the first event, the OTDR s event table may miss the subsequent close events entirely, or classify them as hidden or group events. The problem with hidden events is that the OTDR lumps them together with the first event and it does not provide a loss measurement for the hidden event. In many applications, a full set of measurements is needed for every event. These deficiencies become more severe as the events get close enough to merge together due to the OTDR s dead zone (see Figure 3). In the top portion (A) of Figure 3, the two similar magnitude events are clearly visible to the eye. In the lower portion (B), the second event appears as just an inflection in the decaying attenuation dead zone of the first event. The Current Technology Current OTDR event analysis algorithms are based on each potential event s apparent magnitude. The user sets a threshold level for loss and reflectance parameters. If the identified event is greater than the threshold, it is declared an event. 20 u The Journal of Information Technology Systems

These algorithms use rules that are based on gradient (i.e., derivative), linear regression and correlation techniques. These techniques have inherent limitations outlined in Table 2. New Event Analysis Algorithm Based on Likelihood Rules Noise, including interference, is one of the major limiting factors in the performance of an OTDR. Because noise is additive, the only way to reduce its effect is to increase averaging time. However, there is a limit to how much improvement can be gained by increasing averaging time. At some point, the detection of a small loss or reflection becomes unreliable or impossible when its signal magnitude is comparable to, or below, the noise level. Users set a threshold of smallest loss or reflection to be reported as an event during OTDR testing. We know that the optical signal is strong at a short distance, and it becomes weaker as distances increase. As distance increases, noise becomes more pronounced. A single threshold that is appropriate for the near end of the optical fiber will not work for the far end. The OTDR will often report an increasing number of false events as the far end is approached. This is because the OTDR interprets the noise as events. If the threshold is set higher so that noise at the far end is not falsely identifying noise as events, the instrument will likely miss significant events at near end. In a worst case scenario, as the threshold is continuously decreased, the number of false events can surpass that of true events. A new algorithm has been developed using likelihood analysis. Likelihood is the probability that an event that has already occurred would yield a specific outcome. The concept differs from that of a probability event, in that probability refers to the occurrence of future events, while likelihood refers to past events with known outcomes. The overall likelihood of a possible event being an actual event consists of the combination of a number of individual likelihood components. The reliability of the overall likelihood analysis is increased as more components are used. Some of the components of this technique are described on page 23. Januaury/February 2013 t 21

ODTR with Likelihood Algorithm 16 events (perfect score) FIgURE 4: Both the shape of potential events and the trace surrounding these events is used as part of the likelihood picture. ODTR with Magnitude-based Algorithm 29 events (13 false events) FIgURE 5: In a phase plane plot, the difference function X n = X n-1 -X n are plotted using X n as the horizontal coordinate and X n as the vertical coordinate. FIgURE 6: The performance of the new likelihood-based algorithm compared to the event analysis on various OTDRs available from several manufacturers, using the known golden networks. FIgURE 7: A 16-event network measured using an OTDR with the likelihood algorithm vs. traditional magnitude-based event analysis. 22 u The Journal of Information Technology Systems

Signal-to-noise Ratio Signal-to-noise ratio is a better criterion than signal magnitude to differentiate a true event from noise. Using signal-to-noise ratio, it is possible to detect all major events without any false events. By decreasing the signal-to-noise ratio threshold, it becomes possible to report lower level events without a significant increase in false events. Compared with magnitude threshold, the ratio of false events to true events is much lower with signal-to-noisebased thresholds. Pulse Duration Comparison The width of the OTDR s transmitted light pulse is a known value. This can be compared to the width of potential events in the return trace. Potential events that are either too wide or too narrow can be eliminated. Event Shape Analysis Both the shape of potential events, as well as the trace surrounding these events, is used as part of the likelihood picture (see Figure 4). Looking at Figure 4, based on traditional magnitude-based event analysis, event B would be reported as an event. Shape analysis indicates that event B has a lower likelihood than event A, even though A has a smaller magnitude. different perspective. A series of attenuation values X n denotes a trace captured by an OTDR. We compute the difference function X n = X n-1 -X n and plot in a chart using X n as the horizontal coordinate and X n as vertical coordinate. These plots are shown in Figure 5. In a phase plane plot, reflective events have a characteristic spiral shape, non-reflective events have a characteristic semi-circular shape, and noise appears a series of loops. In addition to the characteristic shape itself, other features of the shape can be included in the likelihood calculation. Comparison of Event Analysis performance As shown in Figure 6, the performance of the new likelihoodbased algorithm was compared to the event analysis on various OTDRs available today (i.e., E, F, J, L, M, O, X) from several manufacturers, using the known golden networks. Performance was evaluated with different event magnitude thresholds to see the ratio between increased matched events and false events. In Figure 6, the perfect performance point is the bull seye at the lower right (e.g., 100 percent found events with no false events reported). It is clear that the likelihood algorithm has far better performance than any of the other OTDRs. Not only are the matched and false event rates better, but the accuracy is independent of the event threshold setting. There is no tradeoff between reporting more matched events at the expense of increasing the false event rate. The three data points (connected by a line) for OTDR F shows this tradeoff in action. When unit F s event threshold is reduced, a small increase in matched events is accompanied by a very large increase in the false event rate. Phase Plane Analysis Phase plane techniques are used in many scientific and engineering applications, such as solving differential equations or designing feedback loops, particularly for non-linear systems. OTDR traces are nothing more than a response signal of a non-linear system consisting of an optical fiber network and the electro-optical system of an OTDR. Phase plan analysis can unveil this system response from a completely Januaury/February 2013 t 23

Figure 7 shows the practical implication of these results. A 16-event network was measured using an OTDR with the likelihood algorithm and using an OTDR with traditional magnitude-based event analysis. The likelihood algorithm reports perfect results, while the magnitude event analysis reports a number of false events. FIgURE 8: Closely spaced events; testing a short 2 m (6.6 ft) jumper cable s performance. FIgURE 9: Typical data center network configuration with a total of nine events. FIgURE 10: Test results for a data center network with short jumper cables zoomed at beginning. practical Applications Measuring a Jumper Cable Figure 8 shows results from testing a 2 meter (m [6.6 feet (ft)]) jumper cable. The reflectance and insertion loss of the connections at both ends of the jumper are measured. This is a valuable tool for verifying the performance of jumper cables. High reflectance is a concern in LANs operating at 10, 40 or 100 gigabits per second (Gb/s), for long haul networks and in networks carrying analog video. Data Center Network Figure 9 shows a typical data center network configuration. This is a short network overall in which there are multiple short jumper cables and other closely spaced events for a total of nine events. The 3 m (10 ft) section followed by the 2 m (6.6 ft) section at the beginning of the cable is particularly challenging. This network also contains a gainer event at 15 m (50 ft), followed by a non-reflective loss event at 25 m (82 ft). Gainer events occur at splice points when joining two different types of optical fiber with different backscatter coefficients or with different mode field diameters (i.e., core size). When measured in the opposite direction, the gainer will appear as a non-reflective loss. A normal splice loss will appear as a loss in both directions. Figure 10 shows a zoomed portion of the beginning of the network with the first three events successfully detected and measured, including the short jumper cables (i.e., events 2 and 3). More traditional OTDR algorithms would likely not provide loss measurements for these events. 24 u The Journal of Information Technology Systems

FIgURE 11: Zoomed gainer and non-reflective loss events. FIgURE 12: Data center network zoomed end portion. Figure 11 shows the zoomed middle portion of the network containing the gainer and nonreflective loss events (i.e., events 4 and 5). Even to a trained eye, these two events are difficult to pick out from the noise. Figure 12 shows the zoomed end portion of the network (i.e., events 6-9). Launch and Receive Cables To measure the loss and reflectance of the first and last events of the optical fiber under test, all of the events in the previous examples have been measured using launch and receive cables. One of the features of the likelihood event analysis is the ability to compensate for normal variations in the length of launch and receive cables. It accurately identifies the beginning and end of the optical fiber under test, without needing to run a separate calibration test for these cables. Conclusion Likelihood-based event analysis has a number of advantages over traditional, magnitude-based event analysis. It offers superior accuracy and reliability based on repeated measured performance. It eliminates the trade-off between detecting more events (i.e., lower loss events) and false events. Since overall likelihood is based on a composite of multiple individual components, this method is subject to continuous improvement. All of this provides a better user experience, since the ultimate purpose of using an OTDR is to analyze events and report accurate network data. t AuThOR BIOGRAPhIES: Mark Miller s telecommunications experience includes optical fiber, copper and wireless in a variety of engineering and marketing roles at Bell Labs, Hewlett Packard, Agilent Technologies and AFL. He received an MSEE degree from Pennsylvania State University and a BSEE from Northeastern University. Dr. Fang Xu is a senior technologist at AFL. He received a Docteur en Science from the Université Paris Sud, France, and holds 18 U.S. patents for instrumentation technology. His work has included high performance, low noise electronics and signal generation. He also is a member of IEEE committees and has contributed to several IEEE standards. He can be reached at fang.xu@aflglobal.com. 26 u The Journal of Information Technology Systems