The Mystery Machine: End-to-end performance analysis of large-scale Internet services

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5 Step 1: Refine dependency graph with counter examples No Traces After Trace 1 After Trace 2 Timing Model t Step 2: Calculate critical path of dependency graph through longest path analysis Refined Model Critical Path of Trace 1 Critical Path of Trace 2 Timing Model Critical Path t Figure 2: Dependency model generation and critical path calculation. This figure provides an example of discovering the true dependency model through iterative refinement. We show only a few segments and relationships for the sake of simplicity. Without any traces, the dependency model is a fully connected graph. By eliminating dependency edges invalidated by counterexamples, we arrive at the true model. With a refined model, we can reprocess the same traces and derive the critical path for each. We use techniques from the race detection literature to map these static relationships to dynamic happens-before relationships. Note that mutual exclusion is a static property; e.g., two components A and B that share a lock are mutually exclusive. Dynamically, for a particular request, this relationship becomes a happens-before relationship: either A B or B A, depending on the order of execution. Pipeline relationships are similar. Thus, for any given request, all of these static relationships can be expressed as dynamic causal relationships between pairs of segments. 4.2 Algorithm The Mystery Machine uses iterative refinement to infer causal relationships. It first generates all possible hypotheses for causal relationships among segments. Then, it iterates through a corpus of traces and rejects a hypothesis if it finds a counterexample in any trace. Step 1 of Figure 2 illustrates this process. We depict the set of hypotheses as a graph where nodes are segments ( S nodes are server segments, N nodes are network segments and C nodes are client segments) and edges are hypothesized relationships. For the sake of simplicity, we restrict this example to consider only happens-before relationships; an arrow from A to B shows a hypothesized A happens before B relationship. The No Traces column shows that all possible relationships are initially hypothesized; this is a large number because the possible relationships scale quadratically as the number of segments increases. Several hypotheses are eliminated by observed contradictions in the first request. For example, since S2 happens after S1, the hypothesized relationship, S2 S1, is removed. Further traces must be processed to complete the model. For instance, the second request eliminates the hypothesized relationship, N1 N2. Additional traces prune new hypotheses due to the natural perturbation in timing of segment processing; e.g., perhaps the second user had less friends, allowing the network segments to overlap due to

8 Average Duration (ms) Javascript DOM CSS Network Server 0 Summed Delay Critical Path Percent of end-to-end latency Summed Delay Critical Path Figure 5: Mean End-to-End Performance Breakdown. Simply summing delay measured at each system component ( Summed Delay ) ignores overlap and underestimates the importance of server latency relative to the actual mean critical path ( Critical Path ). pothesizing new possible relationships and removing relationships in which a counterexample is found. This is shown in Figure 3 by the increase in number of total relationships over time. To account for segments that are eliminated and invariants that are added, one can simply run a new Hadoop job to generate the model over a different time window of traces. Excluding new segments, the rate at which new relationships are added levels off. The rate at which relationships are removed due to counterexamples also levels off. Thus, the model converges on a set of true dependencies. The Mystery Machine relies on ÜberTrace for complete log messages. Log messages, however, may be missing for two reasons: the component does no logging at all for a segment of its execution or the component logs messages for some requests but not others. In the first case, The Mystery Machine cannot identify causal relationships involving the unlogged segment, but causal relationships among all other segments will be identified correctly. When a segment is missing, the model overestimates the concurrency in the system, which would affect the critical path/slack analysis if the true critical path includes the unlogged segment. In the second case, The Mystery Machine would require more traces in order to discover counterexamples. This is equivalent to changing the sampling frequency. 5 Results We demonstrate the utility of The Mystery Machine with two case studies. First, we demonstrate its use for aggregate performance characterization. We study live traffic, stratifying the data to identify factors that influence which system components contribute to the critical path. We find that the critical path can shift between three major components (servers, network, and client) and that these shifts correlate with the client type and network connection quality. This variation suggests one possible performance optimization for Facebook servers: provide differentiated service by prioritizing service for connections where the server has no slack while deprioritizing those where network and client latency will likely dominate. Our second case study demonstrates how the natural variance across a large trace set enables testing of such performance hypotheses without expensive modifications to the system under observation. Since an implementation that provided differential services would require large-scale effort to thread through hundreds of server components, we use our dataset to first determine whether such an optimization is likely to be successful. We find that slack, as detected by The Mystery Machine, indeed indicates that slower server processing time minimally impacts end-toend latency. We also find that slack tends to remain stable for a particular user across multiple Facebook sessions, so the observed slack of past connections can be used to predict the slack of the current connection. 5.1 Characterizing End-to-End Performance In our first case study, we characterize the end-toend performance critical path of Web accesses to the home.php Facebook endpoint. The Mystery Machine analyzes traces of over 1.3 million Web accesses collected over 30 days in July and August Importance of critical path analysis. Figure 5 shows mean time breakdowns over the entire trace dataset. The breakdown is shown in absolute time in the left graph, and as a percent of total time on the right. We assign segments to one of five categories: Server for segments on a Facebook Web server or any internal service accessed from the Web server over RPC, Network for segments in which data traverses the network, DOM for

10 Average Duration (ms) Browser Platform Connection type Computed bandwidth Safari (4%) Chrome (59%) Firefox (18%) IE (14%) Mobile Safari (1%) Mac OS X (6%) Win8 (7%) Win7 (60%) WinVista (18%) WinXP (18%) ios (1%) cable (13%) xdsl (20%) broadband (58%) mobile (4%) p80 (20%) p60 (20%) p40 (20%) p20 (20%) p0 (20%) Javascript DOM CSS Network Server Figure 7: Critical path breakdowns stratified by browser, platform, connection type, and computed bandwidth client operating system is a hint to the kind of client device, which in turn may suggest relative client performance. Figure 7 shows a critical path breakdown for the five most common client platforms in our traces, again annotated with the fraction of requests represented by the bar. Note that we are considering only Web browser requests, so requests initiated by Facebook cell phone apps are not included. The most striking feature of the graph is that Mac OS X users (a small minority of Facebook connections at only 7.1%) tend to connect to Facebook from faster networks than Windows users. We also see that the bulk of connecting Windows users still run Windows 7, and many installations of Windows XP remain deployed. Client processing time has improved markedly over the various generations of Windows. Nevertheless, the breakdowns are all quite similar, and we again find insufficient predictive power for differentiating service time by platform. Stratification by browser. The browser type is also indicated in the HTTP headers transmitted with a request. In Figure 7, we see critical paths for the four most popular browsers. Safari is an outlier, but this category is strongly correlated with the Mac OS X category. Chrome appears to offer slightly better JavaScript performance than the other browsers. Stratification by measured network bandwidth. All of the preceding stratifications only loosely correlate to performance ASN is a poor indication of network connection quality, and browser and OS do not provide a reliable indication of client performance. We provide one more example stratification where we subdivide the population of requests into five categories directly from the measured network bandwidth, which can be deduced from our traces based on network time and cdf Amount of Slack (ms) Figure 8: Slack CDF for Last Data Item. Nearly 20% of traces exhibit considerable slack over 2 s for the server segment that generates the last pagelet transmitted to the client. Conversely, nearly 20% of traces exhibit little (< 250 ms) slack. bytes transmitted. Each of the categories are equally sized to represent 20% of requests, sorted by increasing bandwidth (p80 is the quintile with the highest observed bandwidth). As one would expect, network critical path is strongly correlated to measured network bandwidth. Higher bandwidth connections also tend to come from more capable clients; low-performance clients (e.g., smart phones) often connect over poor networks (3G and Edge networks). 5.2 Differentiated Service using Slack Our second case study uses The Mystery Machine to perform early exploration of a potential performance optimization differentiated service without undertaking the expense of implementing the optimization.

11 End to End Latency (ms) Server generation time (ms) Server generation time (ms) Figure 9: Server vs. End-to-end Latency. For the traces with slack below 25ms (left graph), there is strong correlation (clustering near y = x) between server and end-to-end latency. The correlation is much weaker (wide dispersal above y = x) for the traces with slack above 2.5s (right graph). The characterization in the preceding section reveals that there is enormous variation in the relative importance of client, server, and network performance over the population of Facebook requests. For some requests, server segments form the bulk of the critical path. For these requests, any increase in server latency will result in a commensurate increase in end-to-end latency and a worse user experience. However, after the initial critical segment, many connections are limited by the speed at which data can be delivered over the network or rendered by the client. For these connections, server execution can be delayed to produce data as needed, rather than as soon as possible, without affecting the critical path or the endto-end request latency. We use The Mystery Machine to directly measure the slack in server processing time available in our trace dataset. For simplicity of explanation, we will use the generic term slack in this section to refer to the slack in server processing time only, excluding slack available in any other types of segments. Figure 8 shows the cumulative distribution of slack for the last data item sent by the server to the client. The graph is annotated with a vertical line at 500 ms of slack. For the purposes of this analysis, we have selected 500 ms as a reasonable cut-off between connections for which service should be provided with best effort (< 500 ms slack), and connections for which service can be deprioritized (> 500 ms). However, in practice, the best cut-off will depend on the implementation mechanism used to deprioritize service. More than 60% of all connections exhibit more than 500 ms of slack, indicating substantial opportunity to defer server processing. We find that slack typically increases monotonically during server processing as data items are sent to the client during a request. Thus, we conclude that slack is best consumed equally as several segments execute, as opposed to consuming all slack at the start or end of processing. Validating Slack Estimates It is difficult to directly validate The Mystery Machine s slack estimates, as we can only compute slack once a request has been fully processed. Hence, we cannot retrospectively delay server segments to consume the slack and confirm that the endto-end latency is unchanged. Such an experiment is difficult even under highly controlled circumstances, since it would require precisely reproducing the conditions of a request over and over while selectively delaying only a few server segments. Instead, we turn again to the vastness of our trace data set and the natural variance therein to confirm that slack estimates hold predictive power. Intuitively, small slack implies that server latency is strongly correlated to endto-end latency; indeed, with a slack of zero we expect any increase in server latency to delay end-to-end latency by the same amount. Conversely, when slack is large, we expect little correlation between server latency and endto-end latency; increases in server latency are largely hidden by other concurrent delays. We validate our notion of slack by directly measuring the correlation of server and end-to-end latency. Figure 9 provides an intuitive view of the relationship for which we are testing. Each graph is a heat map of server generation time vs. end-to-end latency. The left graph includes only requests with the lowest measured slack, below 25 ms. There are slightly over 115,000 such requests in this data set. For these requests, we expect a strong correlation between server time and end-to-end time. We find that this subset of requests is tightly clustered just above the y = x (indicated by the line in the figure), indicating a strong correlation. The right figure includes roughly 100,000 requests with the greatest slack (above 2500 ms). For these, we expect no particular relationship between server time and end-to-end time (except that end-to-end time must be at least as large as slack, since this is an invariant of request processing).

12 2000 Type I Error Type II Error 1500 Second Slack (ms) Figure 10: Server End-to-end Latency Correlation vs. Slack. As reported slack increases, the correlation between total server processing time and end-to-end latency weakens, since a growing fraction of server segments are non-critical First Slack (ms) Figure 11: Historical Slack as Classifier. The clustering around the line y = x shows that slack is relatively stable over time. The history-based classifier is correct 83% of the time. A type I error is a false positive, reporting slack as available when it is not. A type II error is a false negative. Indeed, we find the requests dispersed in a large cloud above y = x, with no correlation visually apparent. We provide a more rigorous validation of the slack estimate in Figure 10. Here, we show the correlation coefficient between server time and end-to-end time for equally sized buckets of requests sorted by increasing slack. Each block in the graph corresponds to 5% of our sample, or roughly 57,000 requests (buckets are not equally spaced since the slack distribution is heavytailed). As expected, the correlation coefficient between server and end-to-end latency is quite high, nearly 0.8, when slack is low. It drops to 0.2 for the requests with the largest slack. Predicting Slack. We have found that slack is predictive of the degree to which server latency impacts endto-end latency. However, The Mystery Machine can discover slack only through a retrospective analysis. To be useful in a deployed system, we must predict the availability or lack of slack for a particular connection as server processing begins. One mechanism to predict slack is to recall the slack a particular user experienced in a prior connection to Facebook. Previous slack was found to be more useful in predicting future slack than any other feature we studied. Most users connect to Facebook using the same device and over the same network connection repeatedly. Hence, their client and network performance are likely to remain stable over time. The user id is included as part of the request, and slack could be easily associated with the user id via a persistent cookie or by storing the most recent slack estimate in Memcache [20]. We test the hypothesis that slack remains stable over time by finding all instances within our trace dataset where we have multiple requests associated with the same user id. Since the request sampling rate is exceedingly low, and the active user population is so large, selecting the same user for tracing more than once is a relatively rare event. Nevertheless, again because of the massive volume of traces collected over the course of 30 days of sampling, we have traced more than 1000 repeat users. We test a simple classifier that predicts a user will experience a slack greater than 500 ms if the slack on their most recent preceding connection was also greater than 500 ms. Figure 11 illustrates the result. The graph shows a scatter plot of the first slack and second slack in each pair; the line at y = x indicates slack was identical between the two connections. Our simple historybased classifier predicts the presence or absence of slack correctly 83% of the time. The shaded regions of the graph indicate cases where we have misclassified a connection. A type I error indicates a prediction that there is slack available for a connection when in fact server performance turns out to be critical 8% of requests fall in this category. Conversely, a type II error indicates a prediction that a connection will not have slack when in fact it does, and represents a missed opportunity to throttle service 9% of requests fall in this category. Note that achieving these results does not require frequent sampling. The repeated accesses we study are often several weeks separated in time, and, of course, it is likely that there have been many intervening unsampled requests by the same user. Sampling each user once every few weeks would therefore be sufficient. Potential Impact. We have shown that a potential performance optimization would be to offer differentiated service based on the predicted amount of slack available per connection. Deciding which connections to service is equivalent to real-time scheduling with deadlines.

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