Add evaluation code and data for NSDI'18.

nsdi2018/xlap.yml

+data_files:
+    sender: "sender.csv"
+    receiver: "receiver.csv"
+threads:
+    - app_send
+    - trans_send
+    - trans_recv
+    - app_recv
+cycle_reference:
+    app_send:
+        Start: PrrtSendStart
+        Stop: PrrtSendEnd
+    trans_send:
+        Start: PrrtTransmitStart
+        Stop: LinkTransmitEnd
+    trans_recv:
+        Start: LinkReceive
+        Stop: PrrtReturnPackage
+    app_recv:
+        Start: PrrtReceivePackage
+        Stop: PrrtDeliver
+time_reference:
+    sender:
+        Start: PrrtSendStart
+        Stop: LinkTransmitEnd
+    receiver:
+        Start: LinkReceive
+        Stop: PrrtDeliver
+stamps:
+    PrrtSendStart:
+        Source: sender
+        Thread: app_send
+        Type: time
+    PrrtSubmitPackage:
+        Source: sender
+        Thread: app_send
+        Type: cycle
+    PrrtSendEnd:
+        Source: sender
+        Thread: app_send
+        Type: time
+
+    PrrtTransmitStart:
+        Source: sender
+        Thread: trans_send
+        Type: time
+    PrrtTransmitEnd:
+        Source: sender
+        Thread: trans_send
+        Type: cycle
+    PrrtEncodeStart:
+        Source: sender
+        Thread: trans_send
+        Type: cycle
+    PrrtEncodeEnd:
+        Source: sender
+        Thread: trans_send
+        Type: cycle
+    LinkTransmitStart:
+        Source: sender
+        Thread: trans_send
+        Type: cycle
+    ChannelTransmit:
+        Source: sender
+        Thread: trans_send
+        Type: time
+    LinkTransmitEnd:
+        Source: sender
+        Thread: trans_send
+        Type: time
+
+    ChannelReceive:
+        Source: receiver
+        Thread: trans_recv
+        Type: time
+    LinkReceive:
+        Source: receiver
+        Thread: trans_recv
+        Type: time
+    SendFeedbackStart:
+        Source: receiver
+        Thread: trans_recv
+        Type: cycle
+    SendFeedbackEnd:
+        Source: receiver
+        Thread: trans_recv
+        Type: cycle
+    DecodeStart:
+        Source: receiver
+        Thread: trans_recv
+        Type: cycle
+    DecodeEnd:
+        Source: receiver
+        Thread: trans_recv
+        Type: cycle
+    HandlePacketStart:
+        Source: receiver
+        Thread: trans_recv
+        Type: cycle
+    HandlePacketEnd:
+        Source: receiver
+        Thread: trans_recv
+        Type: cycle
+    PrrtReturnPackage:
+        Source: receiver
+        Thread: trans_recv
+        Type: time
+
+    PrrtDeliver:
+        Source: receiver
+        Thread: app_recv
+        Type: time
+    CopyOutputStart:
+        Source: receiver
+        Thread: app_recv
+        Type: cycle
+    CopyOutputEnd:
+        Source: receiver
+        Thread: app_recv
+        Type: cycle
+    PrrtReceivePackage:
+        Source: receiver
+        Thread: app_recv
+        Type: time
+durations:
+    Send:
+        Start: PrrtSendStart
+        Stop: PrrtSendEnd
+        Source: sender
+    PrrtTransmit:
+        Start: PrrtTransmitStart
+        Stop: PrrtTransmitEnd
+        Source: sender
+    LinkTransmit:
+        Start: LinkTransmitStart
+        Stop: LinkTransmitEnd
+        Source: sender
+    Submit:
+        Start: PrrtSendStart
+        Stop: PrrtSubmitPackage
+        Source: sender
+    Enqueue:
+        Start: PrrtSubmitPackage
+        Stop: PrrtSendEnd
+        Source: sender
+    SenderIPC:
+        Start: PrrtSubmitPackage
+        Stop: PrrtTransmitStart
+        Source: sender
+    SenderEnqueued:
+        Start: PrrtSendEnd
+        Stop: LinkTransmitStart
+        Source: sender
+
+    ReceiverIPC:
+        Start: PrrtReturnPackage
+        Stop: PrrtReceivePackage
+        Source: receiver
+    HandlePacket:
+        Start: HandlePacketStart
+        Stop: HandlePacketEnd
+        Source: receiver
+    Feedback:
+        Start: SendFeedbackStart
+        Stop: SendFeedbackEnd
+        Source: receiver
+    Decoding:
+        Start: DecodeStart
+        Stop: DecodeEnd
+        Source: receiver
+
+packet_types:
+    Data: 0
+    Redundancy: 1

xlap.c

@@ -24,7 +24,8 @@ static inline unsigned long long timestampByTime(struct timespec *ts)
 void XlapTimestampTableDump(FILE *out, XlapTimestampPacketKind kind, XlapTimestampTable *table)
 {
 #	define OUT(id) fprintf(out, ",%llu,%llu", timestampByTime(&table->rows[row].time[ts_##id].actual.t), (unsigned long long) table->rows[row].time[ts_##id].actual.c);
-	for (unsigned int row = 0; row < TS_ROWS; row++) {
+    // Start at 1 to remove the scratch row 0 from output.
+	for (unsigned int row = 1; row < TS_ROWS; row++) {
 		fprintf(out, "%u,%u", row, (unsigned) kind);
 		PP_foreach(PP_join_space, OUT, TIMESTAMP_ID_LIST)
 		fprintf(out, "\n");

xlap.yml

 data_files:
-    sender: "rtn2017/results/on/2017_03_28_09_33_00_Sender.csv"
-    receiver: "rtn2017/results/on/2017_03_28_09_33_00_Receiver.csv"
+    sender: "nsdi2018/results/2018_02_14_12_57_01_sender.csv"
+    receiver: "nsdi2018/results/2018_02_14_12_57_01_receiver.csv"
+threads:
+    - app_send
+    - trans_send
+    - trans_recv
+    - app_recv
 cycle_reference:
-    sender:
+    app_send:
         Start: PrrtSendStart
+        Stop: PrrtSendEnd
+    trans_send:
+        Start: PrrtTransmitStart
         Stop: LinkTransmitEnd
-    receiver:
+    trans_recv:
+        Start: LinkReceive
+        Stop: PrrtReturnPackage
+    app_recv:
         Start: PrrtReceivePackage
         Stop: PrrtDeliver
 time_reference:
@@ -18,70 +29,98 @@ time_reference:
 stamps:
     PrrtSendStart:
         Source: sender
-        Type: time
-    PrrtSendEnd:
-        Source: sender
+        Thread: app_send
         Type: time
     PrrtSubmitPackage:
         Source: sender
+        Thread: app_send
         Type: cycle
-    PrrtEncodeStart:
-        Source: sender
-        Type: cycle
-    PrrtEncodeEnd:
+    PrrtSendEnd:
         Source: sender
-        Type: cycle
+        Thread: app_send
+        Type: time
+
     PrrtTransmitStart:
         Source: sender
+        Thread: trans_send
+        Type: time
+    PrrtTransmitEnd:
+        Source: sender
+        Thread: trans_send
         Type: cycle
-    PrrtTransmitStart:
+    PrrtEncodeStart:
         Source: sender
+        Thread: trans_send
         Type: cycle
-    PrrtTransmitEnd:
+    PrrtEncodeEnd:
         Source: sender
+        Thread: trans_send
         Type: cycle
     LinkTransmitStart:
         Source: sender
+        Thread: trans_send
         Type: cycle
+    ChannelTransmit:
+        Source: sender
+        Thread: trans_send
+        Type: time
     LinkTransmitEnd:
         Source: sender
+        Thread: trans_send
         Type: time
 
-    LinkReceive:
+    ChannelReceive:
         Source: receiver
+        Thread: trans_recv
         Type: time
-    PrrtDeliver:
+    LinkReceive:
         Source: receiver
+        Thread: trans_recv
         Type: time
     SendFeedbackStart:
         Source: receiver
+        Thread: trans_recv
         Type: cycle
     SendFeedbackEnd:
         Source: receiver
+        Thread: trans_recv
         Type: cycle
     DecodeStart:
         Source: receiver
+        Thread: trans_recv
         Type: cycle
     DecodeEnd:
         Source: receiver
+        Thread: trans_recv
         Type: cycle
     HandlePacketStart:
         Source: receiver
+        Thread: trans_recv
         Type: cycle
     HandlePacketEnd:
         Source: receiver
+        Thread: trans_recv
         Type: cycle
+    PrrtReturnPackage:
+        Source: receiver
+        Thread: trans_recv
+        Type: time
+
+    PrrtDeliver:
+        Source: receiver
+        Thread: app_recv
+        Type: time
     CopyOutputStart:
         Source: receiver
+        Thread: app_recv
         Type: cycle
     CopyOutputEnd:
         Source: receiver
-        Type: cycle
-    PrrtReturnPackage:
-        Source: receiver
+        Thread: app_recv
         Type: cycle
     PrrtReceivePackage:
         Source: receiver
+        Thread: app_recv
         Type: time
 durations:
     Send:
@@ -129,6 +168,7 @@ durations:
         Start: DecodeStart
         Stop: DecodeEnd
         Source: receiver
+
 packet_types:
     Data: 0
     Redundancy: 1

xlap/analyse/jitter.py

@@ -28,12 +28,13 @@ def jitter_causes(df, durations, export=False, file_name=None):
     print("Outliers:", len(outliers), ";", "Threshold[us]:", threshold)
 
 
-def trace_jitter(data_frame, export=False, file_name=None):
+def trace_jitter(data_frame, threshold=None, export=False, file_name=None):
     """
     Displays (and saves) a stacked boxplot of durations.
     """
-    thresh = get_outlier_threshold(data_frame["EndToEnd_D"].describe())
-    df_no_outliers = data_frame[data_frame["EndToEnd_D"] <= thresh]
+    if threshold is None:
+        threshold = get_outlier_threshold(data_frame["EndToEnd_D"].describe())
+    df_no_outliers = data_frame[data_frame["EndToEnd_D"] <= threshold]
     box(df_no_outliers, export, file_name)
     print("{} / {} are no outliers.".format(len(df_no_outliers), len(data_frame)))
     fig = plt.gcf()

xlap/analyse/latency.py

+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.stats.stats import pearsonr
+import numpy as np
+import sys
+import graphviz
+
+
+class LatencyAnalysis():
+    def __init__(self, cfg=None, hdb=None):
+        self.cfg = cfg
+
+        correlations = []
+        labels = []
+        for x in hdb:
+            correlations += [x["Correlation"]]
+            labels += ["{} -> {}".format(x["Start"], x["End"])]
+
+        corr = pd.Series(correlations, index=labels)
+
+        self.corr = corr
+
+
+def _get_thread_for_event(config, e):
+    name = str(e)[:-2]
+    try:
+        return config["stamps"][name]["Thread"]
+    except KeyError:
+        print("Cannot find {}".format(name), file=sys.stderr)
+        return None
+
+
+def _happens_before(df, a, b, config):
+    """
+    check if a happens-before b in the trace
+    """
+    # check whether a and b occur in the same thread. If so, a and b cannot be
+    # concurrent.
+    ta = _get_thread_for_event(config, a)
+    tb = _get_thread_for_event(config, b)
+    cname_a = str(a)[:-2] + "_C"
+    cname_b = str(b)[:-2] + "_C"
+    if (ta == tb and ta != None and tb != None):
+        tg = df[a] > df[b]
+        if tg.any():
+            return False
+        df2 = df[df[a] == df[b]]
+        return not ((df2[cname_a] > df2[cname_b]) & df2[cname_b] != 0).any()
+
+    # since a and b occur in different threads, we cannot compare cyclestamps.
+    # If in doubt, a and b are concurrent.
+    return not (df[a] >= df[b]).any()
+
+
+def _fast_happens_before(df, a, b, hb):
+    """
+    check if a happens-before b, using a pre-computed relation
+    """
+    return any(r['Start'] == str(a) and r['End'] == str(b) for r in hb)
+
+
+def _happens_directly_before(df, a, b, hb):
+    """
+    check if a happens-directly-before b in the trace
+    """
+
+    if not _fast_happens_before(df, a, b, hb):
+        return False
+    for event in df:
+        if str(event) == str(a) or str(event) == str(b):
+            continue
+        if _fast_happens_before(df, a, event, hb) and _fast_happens_before(df, event, b, hb):
+            return False
+    return True
+
+
+def _locally_happens_directly_before(df, a, b, hb, config):
+    """
+    check if a happens-directly-before b in the trace but, be a little bit more
+    tolerant regarding intra-thread durations. Consider the following scenario
+
+    Thread A: A1 -> A2 -> A3
+    Thread B: B1 -> B2
+
+    This setup can result in the following directly-happens-before graph:
+
+    A1   B1
+     \  /
+      A2
+     /  \
+    A3   B2
+
+    In this case, B1 does not happens-directly-before B2 because, by chance, A2
+    happens inbetween. If we ignore this anomaly, we only analyse <B1,A2> and
+    <A2,B2>. Both ranges probably have low latency criticality, since their
+    durations are random (because they depend on thread interleavings).
+    However, we really want to analyse <B1,B2> because they actually represent
+    a meaningful range in thread B.
+
+    This function therefore computes a modified happens-directly-before graph
+    with relaxed restrictions for intra-thread ranges:
+
+    A1    B1
+      \  /|
+       A2 |
+      /  \|
+    A3    B2
+    """
+    if not _fast_happens_before(df, a, b, hb):
+        return False
+    ta = _get_thread_for_event(config, a)
+    tb = _get_thread_for_event(config, b)
+    if ta == tb and ta != None and tb != None:
+        for c in df:
+            if _get_thread_for_event(config, c) != ta:
+                continue
+            if _fast_happens_before(df, a, c, hb) and _fast_happens_before(df, c, b, hb):
+                return False
+        return True
+    else:
+        return _happens_directly_before(df, a, b, hb)
+
+
+def _plot_controlflow_graph(df, hdb):
+    """
+    generate the control flow graph using dot
+    """
+    t_columns = [x for x in df.columns if x.endswith("_T")]
+    graph = graphviz.Digraph(filename="graph", format="pdf")
+    for event1 in df[t_columns]:
+        graph.node(str(event1)[:-2])
+    for edge in hdb:
+        graph.edge(edge["Start"][:-2], edge["End"][:-2])
+    graph.render()  # saves to graph.pdf in local folder
+    return graph
+
+
+# Taken from: http://composition.al/blog/2015/11/29/a-better-way-to-add-labels-to-bar-charts-with-matplotlib/
+def autolabel(rects, ax, labels):
+    # Get y-axis height to calculate label position from.
+    (x_left, x_right) = ax.get_xlim()
+    x_width = x_right - x_left
+
+    for i, rect in enumerate(rects):
+        width = rect.get_width()
+        color = "black"
+        align = "left"
+
+        # Fraction of axis height taken up by this rectangle
+        p_width = (width / x_width)
+
+        # If we can fit the label above the column, do that;
+        # otherwise, put it inside the column.
+        if p_width > 0.50:  # arbitrary; 95% looked good to me.
+            label_position = width - (x_width) + 0.7
+            color = "white"
+            align = "right"
+        else:
+            label_position = width + (x_width * 0.01)
+
+        ax.text(label_position, rect.get_y(), labels[i], ha=align, va='bottom', rotation=0, color=color)
+
+
+def _plot_critical_regions(hdb):
+    """
+    plot regions, sorted by latency criticality
+    """
+    relevant = sorted([x for x in hdb if x['Correlation'] > 0], key=lambda x: -x['Correlation'], reverse=True)
+    x = np.arange(len(relevant))
+
+    correlations = list(map(lambda x: x['Correlation'], relevant))
+    ticks = list(map(lambda x: "%s-%s" % (x['Start'][:-2], x['End'][:-2]), relevant))
+    fig, ax = plt.subplots()
+    rects = ax.barh(x, correlations, align="center", tick_label="")
+    autolabel(rects, ax, ticks)
+
+    plt.tight_layout()
+    plt.savefig("latency-criticality.pdf")
+    plt.close()
+
+def analyse(df, config):
+    hb = []
+
+    events = [column for column in df.columns if column.endswith("_T")]
+
+    for event1 in df[events]:
+        for event2 in df[events]:
+            if str(event1) == str(event2):
+                continue
+            if _happens_before(df, event1, event2, config):
+                hb += [{'Start': str(event1), 'End': str(event2)}]
+
+    hdb = []
+    e2e = list(df['EndToEnd_D'])
+    for event1 in df[events]:
+        for event2 in df[events]:
+            if str(event1) == str(event2):
+                continue
+            # if _locally_happens_directly_before(df, event1, event2, hb, config):
+            if _happens_directly_before(df, event1, event2, hb):
+                # compute the correlation between e2e latency and event1-event2 latency
+                l3 = list(df[event2] - df[event1])
+                if any(map(lambda x: x != 0, l3)):
+                    correlation = pearsonr(l3, e2e)[0]
+                else:
+                    correlation = 0
+
+                hdb += [{'Start': str(event1), 'End': str(event2), 'Correlation': correlation}]
+
+    cfg = _plot_controlflow_graph(df, hdb)
+    _plot_critical_regions(hdb)
+    return LatencyAnalysis(cfg=cfg, hdb=hdb)

xlap/command_line.py

@@ -2,9 +2,11 @@ import argparse
 
 from xlap.parse import evaluate, parse_config
 import xlap.analyse.jitter as jitter
+import xlap.analyse.latency as latency
 
 tasks = {
     "jitter": None,
+    "latency": None,
     "capture": None
 }
 
@@ -39,6 +41,10 @@ def main():
                     f.write("\n")
             else:
                 print(output)
+        elif command == "latency":
+            df_data = evaluate(data_files["sender"], data_files["receiver"], config=config, kind=0)
+            a = latency.analyse(df_data, config)
+            print(a.corr.sort_values(ascending=False))
         else:
             df_data = evaluate(data_files["sender"], data_files["receiver"], config=config, kind=0)
 
@@ -53,3 +59,6 @@ def main():
                 df = jitter.prep(df_data, config)
                 jitter.trace_jitter(df, **params1)
                 jitter.jitter_causes(df, config["durations"], **params2)
+
+if __name__ == "__main__":
+    main()

xlap/parse.py

 import pandas as pd
+import numpy as np
 import ruamel.yaml
 import ruamel.yaml as yaml
-
+import logging
+import copy
 
 def _stamp_name_by_src_and_type(all_stamps, src, kind=None):
     if kind is None:
-        kind = ["time", "cycle"]
+        kind = ["time", "cycle", "none"]
     return [c for c, v in all_stamps.items() if v["Source"] == src and v["Type"] in kind]
 
+def _stamp_name_by_thread_and_type(all_stamps, thread, kind=None):
+    if kind is None:
+        kind = ["time", "cycle"]
+    return [c for c, v in all_stamps.items() if v["Thread"] == thread and v["Type"] in kind]
 
-def _extract_stamps_by_type(all_stamps, src, kind=None):
+def _extract_stamps_by_src_and_kind(all_stamps, src, kind=None):
     columns = _stamp_name_by_src_and_type(all_stamps, src, kind)
     return [x + "_T" for x in columns] + [x + "_C" for x in columns]
 
 
-def _evaluate_file(file_name, stamps, kind, sender=False):
-    # Remove first line, as this is the dummy line for intermittently storing data.
-    df = pd.read_csv(file_name)[1:]
+def _evaluate_file(file_name, stamps, kind, measured_column, sender=False):
+    df = pd.read_csv(file_name)
 
     df = df[df["Kind"] == kind].drop(["Kind"], axis=1).set_index("SeqNo")
 
     # Drop columns of opposing side.
     if sender:
-        df.drop(_extract_stamps_by_type(stamps, "receiver"), axis=1, inplace=True)
+        df.drop(_extract_stamps_by_src_and_kind(stamps, "receiver"), axis=1, inplace=True)
     else:
-        df.drop(_extract_stamps_by_type(stamps, "sender"), axis=1, inplace=True)
+        df.drop(_extract_stamps_by_src_and_kind(stamps, "sender"), axis=1, inplace=True)
 
-    # Drop empty rows (as they have probably not been written out).
-    return df[pd.notnull(df).all(axis=1)]
+    # Drop rows with value 0 (as they have probably not been written out).
+    return df[df[measured_column + "_T"] != 0]
 
 
 def _diff_t_c(df, start, stop):
@@ -36,49 +41,79 @@ def _diff_t_c(df, start, stop):
     return time.astype(float), cycles.astype(float)
 
 
+def _generate_thread_durations(df, cycle_reference, thread, stamps):
+    # Generate Processing Duration
+    src_name = "".join(map(str.capitalize, thread.split("_")))
+    # Generate Cycle Times
+    time,