3 ###############################################################################
6 # Copyright (C) 2014 INRIA
8 # This program is free software: you can redistribute it and/or modify
9 # it under the terms of the GNU General Public License version 2 as
10 # published by the Free Software Foundation;
12 # This program is distributed in the hope that it will be useful,
13 # but WITHOUT ANY WARRANTY; without even the implied warranty of
14 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
15 # GNU General Public License for more details.
17 # You should have received a copy of the GNU General Public License
18 # along with this program. If not, see <http://www.gnu.org/licenses/>.
21 # Author: Alina Quereilhac <alina.quereilhac@inria.fr>
23 ###############################################################################
26 # This library contains functions to parse (CCNx) ccnd logs.
28 # Results from experiments must be stored in a directory
29 # named with the experiment run id.
30 # ccnd logs are stored in .log files in a subdirectory per node.
31 # The following diagram exemplifies the experiment result directory
32 # structure (nidi is the unique identifier assigned to node i):
43 from __future__ import print_function
52 from nepi.util.timefuncs import compute_delay_ms
53 from nepi.util.statfuncs import compute_mean
54 import nepi.data.processing.ping.parser as ping_parser
56 def is_control(content_name):
57 return content_name.startswith("ccnx:/%C1") or \
58 content_name.startswith("ccnx:/ccnx") or \
59 content_name.startswith("ccnx:/...")
62 def parse_file(filename):
63 """ Parses message information from ccnd log files
65 filename: path to ccndlog file
72 with open(filename, "r") as f:
77 cols = line.strip().split(sep)
80 # MESSAGE interest_from
81 # 1374181938.808523 ccnd[9245]: debug.4352 interest_from 6 ccnx:/test/bunny.ts (23 bytes,sim=0CDCC1D7)
84 # 1374181938.812750 ccnd[9245]: debug.3502 interest_to 5 ccnx:/test/bunny.ts (39 bytes,i=2844,sim=0CDCC1D7)
86 # MESSAGE CONTENT FROM
87 # 1374181938.868682 ccnd[9245]: debug.4643 content_from 5 ccnx:/test/bunny.ts/%FD%05%1E%85%8FVw/%00/%9E%3D%01%D9%3Cn%95%2BvZ%8
90 # 1374181938.868772 ccnd[9245]: debug.1619 content_to 6 ccnx:/test/bunny.ts/%FD%05%1E%85%8FVw/%00/%9E%3D%01%D9%3Cn%95%2BvZ%8
92 # 1375596708.222304 ccnd[9758]: debug.3692 interest_expiry ccnx:/test/bunny.ts/%FD%05%1E%86%B1GS/%00%0A%F7 (44 bytes,c=0:1,i=2819,sim=49FA8048)
94 # External face creation
95 # 1374181452.965961 ccnd[9245]: accepted datagram client id=5 (flags=0x40012) 204.85.191.10 port 9695
97 if line.find("accepted datagram client") > -1:
98 face_id = (cols[5]).replace("id=",'')
101 faces[face_id] = (ip, port)
104 # 1374181452.985296 ccnd[9245]: releasing face id 4 (slot 4)
105 if line.find("releasing face id") > -1:
115 message_type = cols[3]
117 if message_type not in ["interest_from", "interest_to", "content_from",
118 "content_to", "interest_dupnonce", "interest_expiry"]:
122 content_name = cols[5]
124 # Interest Nonce ? -> 412A74-0844-0008-50AA-F6EAD4
126 if message_type in ["interest_from", "interest_to", "interest_dupnonce"]:
128 if len(last.split("-")) == 5:
132 size = int((cols[6]).replace('(',''))
134 print("interest_expiry without face id!", line)
137 # If no external IP address was identified for this face
138 # asume it is a local face
142 peer, port = faces[face_id]
144 data.append((content_name, timestamp, message_type, peer, face_id,
149 def dump_content_history(content_history):
150 f = tempfile.NamedTemporaryFile(delete=False)
151 pickle.dump(content_history, f)
155 def load_content_history(fname):
156 with open(fname, "r") as f:
157 content_history = pickle.load(f)
160 return content_history
162 def annotate_cn_node(graph, nid, ips2nid, data, content_history):
163 for (content_name, timestamp, message_type, peer, face_id,
164 size, nonce, line) in data:
166 # Ignore control messages for the time being
167 if is_control(content_name):
170 if message_type == "interest_from" and \
172 graph.node[nid]["ccn_consumer"] = True
173 elif message_type == "content_from" and \
175 graph.node[nid]["ccn_producer"] = True
177 # Ignore local messages for the time being.
178 # They could later be used to calculate the processing times
180 if peer == "localhost":
184 if message_type in ["content_from", "content_to"]:
185 content_name = "/".join(content_name.split("/")[:-1])
187 if content_name not in content_history:
188 content_history[content_name] = list()
190 peernid = ips2nid[peer]
191 graph.add_edge(nid, peernid)
193 content_history[content_name].append((timestamp, message_type, nid,
194 peernid, nonce, size, line))
196 def annotate_cn_graph(logs_dir, graph, parse_ping_logs = False):
197 """ Adds CCN content history for each node in the topology graph.
201 # Make a copy of the graph to ensure integrity
206 for nid in graph.nodes():
207 ips = graph.node[nid]["ips"]
213 # Now walk through the ccnd logs...
214 for dirpath, dnames, fnames in os.walk(logs_dir):
215 # continue if we are not at the leaf level (if there are subdirectories)
219 # Each dirpath correspond to a different node
220 nid = os.path.basename(dirpath)
222 # Cast to numeric nid if necessary
223 if int(nid) in graph.nodes():
226 content_history = dict()
229 if fname.endswith(".log"):
231 filename = os.path.join(dirpath, fname)
232 data = parse_file(filename)
233 annotate_cn_node(graph, nid, ips2nid, data, content_history)
235 # Avoid storing everything in memory, instead dump to a file
236 # and reference the file
237 fname = dump_content_history(content_history)
238 graph.node[nid]["history"] = fname
241 msg = "No CCND output files were found to parse at %s " % logs_dir
242 raise RuntimeError(msg)
245 ping_parser.annotate_cn_graph(logs_dir, graph)
249 def ccn_producers(graph):
250 """ Returns the nodes that are content providers """
251 return [nid for nid in graph.nodes() \
252 if graph.node[nid].get("ccn_producer")]
254 def ccn_consumers(graph):
255 """ Returns the nodes that are content consumers """
256 return [nid for nid in graph.nodes() \
257 if graph.node[nid].get("ccn_consumer")]
259 def process_content_history(graph):
260 """ Compute CCN message counts and aggregates content historical
261 information in the content_names dictionary
265 ## Assume single source
266 source = ccn_consumers(graph)[0]
268 interest_expiry_count = 0
269 interest_dupnonce_count = 0
272 content_names = dict()
274 # Collect information about exchanged messages by content name and
276 for nid in graph.nodes():
277 # Load the data collected from the node's ccnd log
278 fname = graph.node[nid]["history"]
279 history = load_content_history(fname)
281 for content_name in history:
282 hist = history[content_name]
284 for (timestamp, message_type, nid1, nid2, nonce, size, line) in hist:
285 if message_type in ["content_from", "content_to"]:
286 # The first Interest sent will not have a version or chunk number.
287 # The first Content sent back in reply, will end in /=00 or /%00.
288 # Make sure to map the first Content to the first Interest.
289 if content_name.endswith("/=00"):
290 content_name = "/".join(content_name.split("/")[0:-2])
292 # Add content name to dictionary
293 if content_name not in content_names:
294 content_names[content_name] = dict()
295 content_names[content_name]["interest"] = dict()
296 content_names[content_name]["content"] = list()
298 # Classify interests by replica
299 if message_type in ["interest_from"] and \
300 nonce not in content_names[content_name]["interest"]:
301 content_names[content_name]["interest"][nonce] = list()
303 # Add consumer history
305 if message_type in ["interest_to", "content_from"]:
306 # content name history as seen by the source
307 if "consumer_history" not in content_names[content_name]:
308 content_names[content_name]["consumer_history"] = list()
310 content_names[content_name]["consumer_history"].append(
311 (timestamp, message_type))
313 # Add messages per content name and cumulate totals by message type
314 if message_type == "interest_dupnonce":
315 interest_dupnonce_count += 1
316 elif message_type == "interest_expiry":
317 interest_expiry_count += 1
318 elif message_type == "interest_from":
320 # Append to interest history of the content name
321 content_names[content_name]["interest"][nonce].append(
322 (timestamp, nid2, nid1))
323 elif message_type == "content_from":
325 # Append to content history of the content name
326 content_names[content_name]["content"].append((timestamp, nid2, nid1))
332 # Compute the time elapsed between the time an interest is sent
333 # in the consumer node and when the content is received back
334 for content_name in content_names:
335 # order content and interest messages by timestamp
336 content_names[content_name]["content"] = sorted(
337 content_names[content_name]["content"])
339 for nonce, timestamps in content_names[content_name][
341 content_names[content_name]["interest"][nonce] = sorted(
344 history = sorted(content_names[content_name]["consumer_history"])
345 content_names[content_name]["consumer_history"] = history
347 # compute the rtt time of the message
349 waiting_content = False
350 interest_timestamp = None
351 content_timestamp = None
353 for (timestamp, message_type) in history:
354 if not waiting_content and message_type == "interest_to":
355 waiting_content = True
356 interest_timestamp = timestamp
359 if waiting_content and message_type == "content_from":
360 content_timestamp = timestamp
363 # If we can't determine who sent the interest, discard it
365 if interest_timestamp and content_timestamp:
366 rtt = compute_delay_ms(content_timestamp, interest_timestamp)
368 content_names[content_name]["rtt"] = rtt
369 content_names[content_name]["lapse"] = (interest_timestamp, content_timestamp)
373 interest_expiry_count,
374 interest_dupnonce_count,
378 def process_content_history_logs(logs_dir, graph, parse_ping_logs = False):
379 """ Parse CCN logs and aggregate content history information in graph.
380 Returns annotated graph and message countn and content names history.
383 ## Process logs and analyse data
385 graph = annotate_cn_graph(logs_dir, graph,
386 parse_ping_logs = parse_ping_logs)
388 print("Skipping: Error parsing ccnd logs", logs_dir)
391 source = ccn_consumers(graph)[0]
392 target = ccn_producers(graph)[0]
394 # Process the data from the ccnd logs, but do not re compute
399 interest_expiry_count,
400 interest_dupnonce_count,
402 content_count) = process_content_history(graph)
404 print("Skipping: Error processing ccn data", logs_dir)
409 interest_expiry_count,
410 interest_dupnonce_count,