class DiffrankWorkload(object): #different rankings with same alpha def __init__(self, topology, n_contents, n_rank, rank_per_group, alpha, beta=0, rate=1.0, n_warmup=10**5, n_measured=4*10**5, seed=None, **kwargs): if alpha < 0: raise ValueError('alpha must be positive') if beta < 0: raise ValueError('beta must be positive') self.receivers = [v for v in topology.nodes_iter() if topology.node[v]['stack'][0] == 'receiver'] self.topology = topology rank_lst = array.array('i',(i for i in range(1,(n_rank+1)))) #differentiate requests distribution inter groups, each group has $rank_per_group distributions. # when num_of_group>N_NODE, multiple groups share a same workload for v in self.receivers: g = self.topology.node[v]['group'] self.topology.node[v]['rank'] = random.choice(array.array('i',(i for i in range(int(rank_per_group*g-rank_per_group+1),int(math.ceil(rank_per_group*g+1)))))) self.n_contents = n_contents self.contents_range = int(n_contents * 32) self.contents = range(1, self.contents_range + 1) self.zipf = TruncatedZipfDist(alpha, self.n_contents) self.n_rank = int(n_rank) self.alpha = alpha self.rate = rate self.n_warmup = n_warmup self.n_measured = n_measured random.seed(seed) self.beta = beta if beta != 0: degree = nx.degree(self.topology) self.receivers = sorted(self.receivers, key=lambda x: degree[iter(topology.edge[x]).next()], reverse=True) self.receiver_dist = TruncatedZipfDist(beta, len(self.receivers)) def __iter__(self): req_counter = 0 t_event = 0.0 while req_counter < self.n_warmup + self.n_measured: t_event += (random.expovariate(self.rate)) if self.beta == 0: receiver = random.choice(self.receivers) else: receiver = self.receivers[self.receiver_dist.rv()-1] self.receiver = receiver rank_receiver = int(self.topology.node[self.receiver]['rank']-1) content = int(self.zipf.rv()) + self.n_contents * rank_receiver #print ("content:%d, self.n_contents:%d, rank_receiver:%d") % (content, self.n_contents, rank_receiver) log = (req_counter >= self.n_warmup) event = {'receiver': receiver, 'content': content, 'log': log} yield (t_event, event) req_counter += 1 raise StopIteration()
def uniform_req_gen(topology, n_contents, alpha, rate=12.0, n_warmup=10**5, n_measured=4 * 10**5, seed=None): """This function generates events on the fly, i.e. instead of creating an event schedule to be kept in memory, returns an iterator that generates events when needed. This is useful for running large schedules of events where RAM is limited as its memory impact is considerably lower. These requests are Poisson-distributed while content popularity is Zipf-distributed Parameters ---------- topology : fnss.Topology The topology to which the workload refers n_contents : int The number of content object alpha : float The Zipf alpha parameter rate : float The mean rate of requests per second n_warmup : int The number of warmup requests (i.e. requests executed to fill cache but not logged) n_measured : int The number of logged requests after the warmup Returns ------- events : iterator Iterator of events. Each event is a 2-tuple where the first element is the timestamp at which the event occurs and the second element is a dictionary of event attributes. """ receivers = [ v for v in topology.nodes_iter() if topology.node[v]['stack'][0] == 'receiver' ] zipf = TruncatedZipfDist(alpha, n_contents) random.seed(seed) req_counter = 0 t_event = 0.0 while req_counter < n_warmup + n_measured: t_event += (random.expovariate(rate)) receiver = random.choice(receivers) content = int(zipf.rv()) log = (req_counter >= n_warmup) event = {'receiver': receiver, 'content': content, 'log': log} yield (t_event, event) req_counter += 1 raise StopIteration()
def uniform_req_gen(topology, n_contents, alpha, rate=12.0, n_warmup=10 ** 5, n_measured=4 * 10 ** 5, seed=None): """This function generates events on the fly, i.e. instead of creating an event schedule to be kept in memory, returns an iterator that generates events when needed. This is useful for running large schedules of events where RAM is limited as its memory impact is considerably lower. These requests are Poisson-distributed while content popularity is Zipf-distributed Parameters ---------- topology : fnss.Topology The topology to which the workload refers n_contents : int The number of content object alpha : float The Zipf alpha parameter rate : float The mean rate of requests per second n_warmup : int The number of warmup requests (i.e. requests executed to fill cache but not logged) n_measured : int The number of logged requests after the warmup Returns ------- events : iterator Iterator of events. Each event is a 2-tuple where the first element is the timestamp at which the event occurs and the second element is a dictionary of event attributes. """ receivers = [v for v in topology.nodes_iter() if topology.node[v]['stack'][0] == 'receiver'] zipf = TruncatedZipfDist(alpha, n_contents) random.seed(seed) req_counter = 0 t_event = 0.0 while req_counter < n_warmup + n_measured: t_event += (random.expovariate(rate)) receiver = random.choice(receivers) content = int(zipf.rv()) log = (req_counter >= n_warmup) event = {'receiver': receiver, 'content': content, 'log': log} yield (t_event, event) req_counter += 1 raise StopIteration()
class StationaryWorkload(object): """This function generates events on the fly, i.e. instead of creating an event schedule to be kept in memory, returns an iterator that generates events when needed. This is useful for running large schedules of events where RAM is limited as its memory impact is considerably lower. These requests are Poisson-distributed while content popularity is Zipf-distributed All requests are mapped to receivers uniformly unless a positive *beta* parameter is specified. If a *beta* parameter is specified, then receivers issue requests at different rates. The algorithm used to determine the requests rates for each receiver is the following: * All receiver are sorted in decreasing order of degree of the PoP they are attached to. This assumes that all receivers have degree = 1 and are attached to a node with degree > 1 * Rates are then assigned following a Zipf distribution of coefficient beta where nodes with higher-degree PoPs have a higher request rate Parameters ---------- topology : fnss.Topology The topology to which the workload refers n_contents : int The number of content object alpha : float The Zipf alpha parameter beta : float, optional Parameter indicating rate : float, optional The mean rate of requests per second n_warmup : int, optional The number of warmup requests (i.e. requests executed to fill cache but not logged) n_measured : int, optional The number of logged requests after the warmup Returns ------- events : iterator Iterator of events. Each event is a 2-tuple where the first element is the timestamp at which the event occurs and the second element is a dictionary of event attributes. """ def __init__(self, topology, n_contents, alpha, beta=0, rate=1.0, n_warmup=10**5, n_measured=4 * 10**5, seed=0, n_services=10, **kwargs): if alpha < 0: raise ValueError('alpha must be positive') if beta < 0: raise ValueError('beta must be positive') self.receivers = [ v for v in topology.nodes_iter() if topology.node[v]['stack'][0] == 'receiver' ] self.zipf = TruncatedZipfDist(alpha, n_services - 1, seed) self.n_contents = n_contents self.contents = range(0, n_contents) self.n_services = n_services self.alpha = alpha self.rate = rate self.n_warmup = n_warmup self.n_measured = n_measured self.model = None self.beta = beta self.topology = topology if beta != 0: degree = nx.degree(self.topology) self.receivers = sorted( self.receivers, key=lambda x: degree[iter(topology.edge[x]).next()], reverse=True) self.receiver_dist = TruncatedZipfDist(beta, len(self.receivers), seed) self.seed = seed self.first = True def __iter__(self): req_counter = 0 t_event = 0.0 flow_id = 0 if self.first: #TODO remove this first variable, this is not necessary here random.seed(self.seed) self.first = False #aFile = open('workload.txt', 'w') #aFile.write("# Time\tNodeID\tserviceID\n") eventObj = self.model.eventQ[0] if len(self.model.eventQ) > 0 else None while req_counter < self.n_warmup + self.n_measured or len( self.model.eventQ) > 0: t_event += (random.expovariate(self.rate)) eventObj = self.model.eventQ[0] if len( self.model.eventQ) > 0 else None while eventObj is not None and eventObj.time < t_event: heapq.heappop(self.model.eventQ) log = (req_counter >= self.n_warmup) event = { 'receiver': eventObj.receiver, 'content': eventObj.service, 'log': log, 'node': eventObj.node, 'flow_id': eventObj.flow_id, 'deadline': eventObj.deadline, 'rtt_delay': eventObj.rtt_delay, 'status': eventObj.status, 'task': eventObj.task } yield (eventObj.time, event) eventObj = self.model.eventQ[0] if len( self.model.eventQ) > 0 else None if req_counter >= (self.n_warmup + self.n_measured): # skip below if we already sent all the requests continue if self.beta == 0: receiver = random.choice(self.receivers) else: receiver = self.receivers[self.receiver_dist.rv() - 1] node = receiver content = int(self.zipf.rv()) log = (req_counter >= self.n_warmup) flow_id += 1 deadline = self.model.services[content].deadline + t_event event = { 'receiver': receiver, 'content': content, 'log': log, 'node': node, 'flow_id': flow_id, 'rtt_delay': 0, 'deadline': deadline, 'status': REQUEST } neighbors = self.topology.neighbors(receiver) s = str(t_event) + "\t" + str( neighbors[0]) + "\t" + str(content) + "\n" #aFile.write(s) yield (t_event, event) req_counter += 1 print "End of iteration: len(eventObj): " + repr(len( self.model.eventQ)) #aFile.close() raise StopIteration()
class YCSBWorkload(object): """Yahoo! Cloud Serving Benchmark (YCSB) The YCSB is a set of reference workloads used to benchmark databases and, more generally any storage/caching systems. It comprises five workloads: +------------------+------------------------+------------------+ | Workload | Operations | Record selection | +------------------+------------------------+------------------+ | A - Update heavy | Read: 50%, Update: 50% | Zipfian | | B - Read heavy | Read: 95%, Update: 5% | Zipfian | | C - Read only | Read: 100% | Zipfian | | D - Read latest | Read: 95%, Insert: 5% | Latest | | E - Short ranges | Scan: 95%, Insert 5% | Zipfian/Uniform | +------------------+------------------------+------------------+ Notes ----- At the moment only workloads A, B and C are implemented, since they are the most relevant for caching systems. """ def __init__(self, workload, n_contents, n_warmup, n_measured, alpha=0.99, seed=None, **kwargs): """Constructor Parameters ---------- workload : str Workload identifier. Currently supported: "A", "B", "C" n_contents : int Number of content items n_warmup : int, optional The number of warmup requests (i.e. requests executed to fill cache but not logged) n_measured : int, optional The number of logged requests after the warmup alpha : float, optional Parameter of Zipf distribution seed : int, optional The seed for the random generator """ if workload not in ("A", "B", "C", "D", "E"): raise ValueError("Incorrect workload ID [A-B-C-D-E]") elif workload in ("D", "E"): raise NotImplementedError("Workloads D and E not yet implemented") self.workload = workload if seed is not None: random.seed(seed) self.zipf = TruncatedZipfDist(alpha, n_contents) self.n_warmup = n_warmup self.n_measured = n_measured def __iter__(self): """Return an iterator over the workload""" req_counter = 0 while req_counter < self.n_warmup + self.n_measured: rand = random.random() op = { "A": "READ" if rand < 0.5 else "UPDATE", "B": "READ" if rand < 0.95 else "UPDATE", "C": "READ" }[self.workload] item = int(self.zipf.rv()) log = (req_counter >= self.n_warmup) event = {'op': op, 'item': item, 'log': log} yield event req_counter += 1 raise StopIteration()
class TraceDrivenWorkload(object): """Parse requests from a generic request trace. This workload requires two text files: * a requests file, where each line corresponds to a string identifying the content requested * a contents file, which lists all unique content identifiers appearing in the requests file. Since the trace do not provide timestamps, requests are scheduled according to a Poisson process of rate *rate*. All requests are mapped to receivers uniformly unless a positive *beta* parameter is specified. If a *beta* parameter is specified, then receivers issue requests at different rates. The algorithm used to determine the requests rates for each receiver is the following: * All receiver are sorted in decreasing order of degree of the PoP they are attached to. This assumes that all receivers have degree = 1 and are attached to a node with degree > 1 * Rates are then assigned following a Zipf distribution of coefficient beta where nodes with higher-degree PoPs have a higher request rate Parameters ---------- topology : fnss.Topology The topology to which the workload refers reqs_file : str The path to the requests file contents_file : str The path to the contents file n_contents : int The number of content object (i.e. the number of lines of contents_file) n_warmup : int The number of warmup requests (i.e. requests executed to fill cache but not logged) n_measured : int The number of logged requests after the warmup rate : float, optional The network-wide mean rate of requests per second beta : float, optional Spatial skewness of requests rates Returns ------- events : iterator Iterator of events. Each event is a 2-tuple where the first element is the timestamp at which the event occurs and the second element is a dictionary of event attributes. """ def __init__(self, topology, reqs_file, contents_file, n_contents, n_warmup, n_measured, rate=1.0, beta=0, **kwargs): """Constructor""" if beta < 0: raise ValueError('beta must be positive') # Set high buffering to avoid one-line reads self.buffering = 64 * 1024 * 1024 self.n_contents = n_contents self.n_warmup = n_warmup self.n_measured = n_measured self.reqs_file = reqs_file self.rate = rate self.receivers = [ v for v in topology.nodes_iter() if topology.node[v]['stack'][0] == 'receiver' ] self.contents = [] with open(contents_file, 'r', buffering=self.buffering) as f: for content in f: self.contents.append(content) self.beta = beta if beta != 0: degree = nx.degree(topology) self.receivers = sorted( self.receivers, key=lambda x: degree[iter(topology.edge[x]).next()], reverse=True) self.receiver_dist = TruncatedZipfDist(beta, len(self.receivers)) def __iter__(self): req_counter = 0 t_event = 0.0 with open(self.reqs_file, 'r', buffering=self.buffering) as f: for content in f: t_event += (random.expovariate(self.rate)) if self.beta == 0: receiver = random.choice(self.receivers) else: receiver = self.receivers[self.receiver_dist.rv() - 1] log = (req_counter >= self.n_warmup) event = {'receiver': receiver, 'content': content, 'log': log} yield (t_event, event) req_counter += 1 if (req_counter >= self.n_warmup + self.n_measured): raise StopIteration() raise ValueError("Trace did not contain enough requests")
class GlobetraffWorkload(object): """Parse requests from GlobeTraff workload generator All requests are mapped to receivers uniformly unless a positive *beta* parameter is specified. If a *beta* parameter is specified, then receivers issue requests at different rates. The algorithm used to determine the requests rates for each receiver is the following: * All receiver are sorted in decreasing order of degree of the PoP they are attached to. This assumes that all receivers have degree = 1 and are attached to a node with degree > 1 * Rates are then assigned following a Zipf distribution of coefficient beta where nodes with higher-degree PoPs have a higher request rate Parameters ---------- topology : fnss.Topology The topology to which the workload refers reqs_file : str The GlobeTraff request file contents_file : str The GlobeTraff content file beta : float, optional Spatial skewness of requests rates Returns ------- events : iterator Iterator of events. Each event is a 2-tuple where the first element is the timestamp at which the event occurs and the second element is a dictionary of event attributes. """ def __init__(self, topology, reqs_file, contents_file, beta=0, **kwargs): """Constructor""" if beta < 0: raise ValueError('beta must be positive') self.receivers = [ v for v in topology.nodes_iter() if topology.node[v]['stack'][0] == 'receiver' ] self.n_contents = 0 with open(contents_file, 'r') as f: reader = csv.reader(f, delimiter='\t') for content, popularity, size, app_type in reader: self.n_contents = max(self.n_contents, content) self.n_contents += 1 self.contents = range(self.n_contents) self.request_file = reqs_file self.beta = beta if beta != 0: degree = nx.degree(self.topology) self.receivers = sorted( self.receivers, key=lambda x: degree[iter(topology.edge[x]).next()], reverse=True) self.receiver_dist = TruncatedZipfDist(beta, len(self.receivers)) def __iter__(self): with open(self.request_file, 'r') as f: reader = csv.reader(f, delimiter='\t') for timestamp, content, size in reader: if self.beta == 0: receiver = random.choice(self.receivers) else: receiver = self.receivers[self.receiver_dist.rv() - 1] event = { 'receiver': receiver, 'content': content, 'size': size } yield (timestamp, event) raise StopIteration()
class YOUTUBE_TRACE(object): """ YOUTUBE_TRACE Parameters ---------- topology : fnss.Topology The topology to which the workload refers n_contents : int The number of content object alpha : float The Zipf alpha parameter beta : float, optional Parameter indicating rate : float, optional The mean rate of requests per second n_warmup : int, optional The number of warmup requests (i.e. requests executed to fill cache but not logged) n_measured : int, optional The number of logged requests after the warmup Returns ------- events : iterator Iterator of events. Each event is a 2-tuple where the first element is the timestamp at which the event occurs and the second element is a dictionary of event attributes. """ def __init__(self, topology, n_contents, alpha, beta=0, rate=1.0, n_warmup=10**3, n_measured=4 * 10**5, seed=None, **kwargs): if alpha < 0: raise ValueError('alpha must be positive') if beta < 0: raise ValueError('beta must be positive') self.receivers = [ v for v in topology.nodes_iter() if topology.node[v]['stack'][0] == 'receiver' ] self.zipf = TruncatedZipfDist(alpha, n_contents) self.n_contents = n_contents self.contents = range(1, n_contents + 1) self.alpha = alpha self.rate = rate self.n_warmup = n_warmup self.n_measured = n_measured random.seed(seed) self.beta = beta if beta != 0: degree = nx.degree(self.topology) self.receivers = sorted( self.receivers, key=lambda x: degree[iter(topology.edge[x]).next()], reverse=True) self.receiver_dist = TruncatedZipfDist(beta, len(self.receivers)) def __iter__(self): req_counter = 0 t_event = 0.0 mypath = "C:\Users\widndows7\Desktop\myresult\youtube.parsed.012908.24.txt" for n in my_parse_youtube_umass(mypath): t_event += (random.expovariate(self.rate)) if self.beta == 0: receiver = self.receivers[int(n['client_addr']) % len(self.receivers)] else: receiver = self.receivers[self.receiver_dist.rv() - 1] content = n['video_id'] log = (req_counter >= self.n_warmup) event = {'receiver': receiver, 'content': content, 'log': log} yield (t_event, event) req_counter += 1 for n in my_parse_youtube_umass(mypath1): t_event += (random.expovariate(self.rate)) if self.beta == 0: receiver = self.receivers[int(n['client_addr']) % len(self.receivers)] else: receiver = self.receivers[self.receiver_dist.rv() - 1] content = n['video_id'] log = (req_counter >= self.n_warmup) event = {'receiver': receiver, 'content': content, 'log': log} yield (t_event, event) req_counter += 1 raise StopIteration()
class My_Workload(object): """This function generates events on the fly, i.e. instead of creating an event schedule to be kept in memory, returns an iterator that generates events when needed. This is useful for running large schedules of events where RAM is limited as its memory impact is considerably lower. These requests are Poisson-distributed while content popularity is Zipf-distributed All requests are mapped to receivers uniformly unless a positive *beta* parameter is specified. If a *beta* parameter is specified, then receivers issue requests at different rates. The algorithm used to determine the requests rates for each receiver is the following: * All receiver are sorted in decreasing order of degree of the PoP they are attached to. This assumes that all receivers have degree = 1 and are attached to a node with degree > 1 * Rates are then assigned following a Zipf distribution of coefficient beta where nodes with higher-degree PoPs have a higher request rate Parameters ---------- topology : fnss.Topology The topology to which the workload refers n_contents : int The number of content object alpha : float The Zipf alpha parameter beta : float, optional Parameter indicating rate : float, optional The mean rate of requests per second n_warmup : int, optional The number of warmup requests (i.e. requests executed to fill cache but not logged) n_measured : int, optional The number of logged requests after the warmup Returns ------- events : iterator Iterator of events. Each event is a 2-tuple where the first element is the timestamp at which the event occurs and the second element is a dictionary of event attributes. """ def __init__(self, topology, n_contents, alpha, beta=0, rate=1.0, n_warmup=10**5, n_measured=4 * 10**5, seed=None, **kwargs): if alpha < 0: raise ValueError('alpha must be positive') if beta < 0: raise ValueError('beta must be positive') self.receivers = [ v for v in topology.nodes_iter() if topology.node[v]['stack'][0] == 'receiver' ] self.zipf = TruncatedZipfDist(alpha, n_contents) self.n_contents = n_contents self.contents = range(1, n_contents + 1) self.alpha = alpha self.rate = rate self.n_warmup = n_warmup self.n_measured = n_measured random.seed(seed) self.beta = beta if beta != 0: degree = nx.degree(self.topology) self.receivers = sorted( self.receivers, key=lambda x: degree[iter(topology.edge[x]).next()], reverse=True) self.receiver_dist = TruncatedZipfDist(beta, len(self.receivers)) def __iter__(self): req_counter = 0 t_event = 0.0 while req_counter < self.n_warmup + self.n_measured: t_event += (random.expovariate(self.rate)) ''' if self.beta == 0: receiver = random.choice(self.receivers) else: receiver = self.receivers[self.receiver_dist.rv() - 1] ''' content = int(self.zipf.rv()) k = random.choice([0, 1]) if k != 0: receiver = random.choice(self.receivers) else: receiver = random.choice( [v for v in self.receivers if content % 64 == v % 64]) log = (req_counter >= self.n_warmup) event = {'receiver': receiver, 'content': content, 'log': log} yield (t_event, event) req_counter += 1 raise StopIteration()
class StationaryWorkload(object): """This function generates events on the fly, i.e. instead of creating an event schedule to be kept in memory, returns an iterator that generates events when needed. This is useful for running large schedules of events where RAM is limited as its memory impact is considerably lower. These requests are Poisson-distributed while content popularity is Zipf-distributed All requests are mapped to receivers uniformly unless a positive *beta* parameter is specified. If a *beta* parameter is specified, then receivers issue requests at different rates. The algorithm used to determine the requests rates for each receiver is the following: * All receiver are sorted in decreasing order of degree of the PoP they are attached to. This assumes that all receivers have degree = 1 and are attached to a node with degree > 1 * Rates are then assigned following a Zipf distribution of coefficient beta where nodes with higher-degree PoPs have a higher request rate Parameters ---------- topology : fnss.Topology The topology to which the workload refers n_contents : int The number of content object alpha : float The Zipf alpha parameter beta : float, optional Parameter indicating rate : float, optional The mean rate of requests per second n_warmup : int, optional The number of warmup requests (i.e. requests executed to fill cache but not logged) n_measured : int, optional The number of logged requests after the warmup Returns ------- events : iterator Iterator of events. Each event is a 2-tuple where the first element is the timestamp at which the event occurs and the second element is a dictionary of event attributes. """ def __init__(self, topology, n_contents, alpha, beta=0, rate=1.0, n_warmup=10 ** 5, n_measured=4 * 10 ** 5, seed=None, **kwargs): if alpha < 0: raise ValueError('alpha must be positive') if beta < 0: raise ValueError('beta must be positive') self.receivers = [v for v in topology.nodes() if topology.node[v]['stack'][0] == 'receiver'] self.zipf = TruncatedZipfDist(alpha, n_contents) self.n_contents = n_contents self.contents = range(1, n_contents + 1) self.alpha = alpha self.rate = rate self.n_warmup = n_warmup self.n_measured = n_measured random.seed(seed) self.beta = beta if beta != 0: degree = nx.degree(self.topology) self.receivers = sorted(self.receivers, key=lambda x: degree[iter(topology.adj[x]).next()], reverse=True) self.receiver_dist = TruncatedZipfDist(beta, len(self.receivers)) def __iter__(self): req_counter = 0 t_event = 0.0 while req_counter < self.n_warmup + self.n_measured: t_event += (random.expovariate(self.rate)) if self.beta == 0: receiver = random.choice(self.receivers) else: receiver = self.receivers[self.receiver_dist.rv() - 1] content = int(self.zipf.rv()) log = (req_counter >= self.n_warmup) event = {'receiver': receiver, 'content': content, 'log': log} yield (t_event, event) req_counter += 1 raise StopIteration()
class YCSBWorkload(object): """Yahoo! Cloud Serving Benchmark (YCSB) The YCSB is a set of reference workloads used to benchmark databases and, more generally any storage/caching systems. It comprises five workloads: +------------------+------------------------+------------------+ | Workload | Operations | Record selection | +------------------+------------------------+------------------+ | A - Update heavy | Read: 50%, Update: 50% | Zipfian | | B - Read heavy | Read: 95%, Update: 5% | Zipfian | | C - Read only | Read: 100% | Zipfian | | D - Read latest | Read: 95%, Insert: 5% | Latest | | E - Short ranges | Scan: 95%, Insert 5% | Zipfian/Uniform | +------------------+------------------------+------------------+ Notes ----- At the moment only workloads A, B and C are implemented, since they are the most relevant for caching systems. """ def __init__(self, workload, n_contents, n_warmup, n_measured, alpha=0.99, seed=None, **kwargs): """Constructor Parameters ---------- workload : str Workload identifier. Currently supported: "A", "B", "C" n_contents : int Number of content items n_warmup : int, optional The number of warmup requests (i.e. requests executed to fill cache but not logged) n_measured : int, optional The number of logged requests after the warmup alpha : float, optional Parameter of Zipf distribution seed : int, optional The seed for the random generator """ if workload not in ("A", "B", "C", "D", "E"): raise ValueError("Incorrect workload ID [A-B-C-D-E]") elif workload in ("D", "E"): raise NotImplementedError("Workloads D and E not yet implemented") self.workload = workload if seed is not None: random.seed(seed) self.zipf = TruncatedZipfDist(alpha, n_contents) self.n_warmup = n_warmup self.n_measured = n_measured def __iter__(self): """Return an iterator over the workload""" req_counter = 0 while req_counter < self.n_warmup + self.n_measured: rand = random.random() op = { "A": "READ" if rand < 0.5 else "UPDATE", "B": "READ" if rand < 0.95 else "UPDATE", "C": "READ" }[self.workload] item = int(self.zipf.rv()) log = (req_counter >= self.n_warmup) event = {'op': op, 'item': item, 'log': log} yield event req_counter += 1 return
class TraceDrivenWorkload(object): """Parse requests from a generic request trace. This workload requires two text files: * a requests file, where each line corresponds to a string identifying the content requested * a contents file, which lists all unique content identifiers appearing in the requests file. Since the trace do not provide timestamps, requests are scheduled according to a Poisson process of rate *rate*. All requests are mapped to receivers uniformly unless a positive *beta* parameter is specified. If a *beta* parameter is specified, then receivers issue requests at different rates. The algorithm used to determine the requests rates for each receiver is the following: * All receiver are sorted in decreasing order of degree of the PoP they are attached to. This assumes that all receivers have degree = 1 and are attached to a node with degree > 1 * Rates are then assigned following a Zipf distribution of coefficient beta where nodes with higher-degree PoPs have a higher request rate Parameters ---------- topology : fnss.Topology The topology to which the workload refers reqs_file : str The path to the requests file contents_file : str The path to the contents file n_contents : int The number of content object (i.e. the number of lines of contents_file) n_warmup : int The number of warmup requests (i.e. requests executed to fill cache but not logged) n_measured : int The number of logged requests after the warmup rate : float, optional The network-wide mean rate of requests per second beta : float, optional Spatial skewness of requests rates Returns ------- events : iterator Iterator of events. Each event is a 2-tuple where the first element is the timestamp at which the event occurs and the second element is a dictionary of event attributes. """ def __init__(self, topology, reqs_file, contents_file, n_contents, n_warmup, n_measured, rate=1.0, beta=0, **kwargs): """Constructor""" if beta < 0: raise ValueError('beta must be positive') # Set high buffering to avoid one-line reads self.buffering = 64 * 1024 * 1024 self.n_contents = n_contents self.n_warmup = n_warmup self.n_measured = n_measured self.reqs_file = reqs_file self.rate = rate self.receivers = [v for v in topology.nodes() if topology.node[v]['stack'][0] == 'receiver'] self.contents = [] with open(contents_file, 'r', buffering=self.buffering) as f: for content in f: self.contents.append(content) self.beta = beta if beta != 0: degree = nx.degree(topology) self.receivers = sorted(self.receivers, key=lambda x: degree[iter(topology.adj[x]).next()], reverse=True) self.receiver_dist = TruncatedZipfDist(beta, len(self.receivers)) def __iter__(self): req_counter = 0 t_event = 0.0 with open(self.reqs_file, 'r', buffering=self.buffering) as f: for content in f: t_event += (random.expovariate(self.rate)) if self.beta == 0: receiver = random.choice(self.receivers) else: receiver = self.receivers[self.receiver_dist.rv() - 1] log = (req_counter >= self.n_warmup) event = {'receiver': receiver, 'content': content, 'log': log} yield (t_event, event) req_counter += 1 if(req_counter >= self.n_warmup + self.n_measured): raise StopIteration() raise ValueError("Trace did not contain enough requests")
class GlobetraffWorkload(object): """Parse requests from GlobeTraff workload generator All requests are mapped to receivers uniformly unless a positive *beta* parameter is specified. If a *beta* parameter is specified, then receivers issue requests at different rates. The algorithm used to determine the requests rates for each receiver is the following: * All receiver are sorted in decreasing order of degree of the PoP they are attached to. This assumes that all receivers have degree = 1 and are attached to a node with degree > 1 * Rates are then assigned following a Zipf distribution of coefficient beta where nodes with higher-degree PoPs have a higher request rate Parameters ---------- topology : fnss.Topology The topology to which the workload refers reqs_file : str The GlobeTraff request file contents_file : str The GlobeTraff content file beta : float, optional Spatial skewness of requests rates Returns ------- events : iterator Iterator of events. Each event is a 2-tuple where the first element is the timestamp at which the event occurs and the second element is a dictionary of event attributes. """ def __init__(self, topology, reqs_file, contents_file, beta=0, **kwargs): """Constructor""" if beta < 0: raise ValueError('beta must be positive') self.receivers = [v for v in topology.nodes() if topology.node[v]['stack'][0] == 'receiver'] self.n_contents = 0 with open(contents_file, 'r') as f: reader = csv.reader(f, delimiter='\t') for content, popularity, size, app_type in reader: self.n_contents = max(self.n_contents, content) self.n_contents += 1 self.contents = range(self.n_contents) self.request_file = reqs_file self.beta = beta if beta != 0: degree = nx.degree(self.topology) self.receivers = sorted(self.receivers, key=lambda x: degree[iter(topology.adj[x]).next()], reverse=True) self.receiver_dist = TruncatedZipfDist(beta, len(self.receivers)) def __iter__(self): with open(self.request_file, 'r') as f: reader = csv.reader(f, delimiter='\t') for timestamp, content, size in reader: if self.beta == 0: receiver = random.choice(self.receivers) else: receiver = self.receivers[self.receiver_dist.rv() - 1] event = {'receiver': receiver, 'content': content, 'size': size} yield (timestamp, event) raise StopIteration()
class TransitLocalWorkload(object): """ This function generates transit and local traffic according to given percentages and content popularity. The following traffic patterns are generated: transit traffic: traffic that transits through the domain. local traffic: both end-points of the traffic is within the domain. ingress traffic: consumer is outside and the producer is inside the domain. egress traffic: consumer is inside and the producer is outside the domain. """ def __init__(self, topology, n_contents, alpha, beta=0, rate=1.0, n_warmup=10 ** 5, n_measured=4 * 10 ** 5, transit=0.7, local=0.1, ingress=0.1, egress=0.1, seed=None, **kwargs): if alpha < 0: raise ValueError('alpha must be positive') if beta < 0: raise ValueError('beta must be positive') self.receivers = [v for v in topology.nodes_iter() if topology.node[v]['stack'][0] == 'receiver'] self.n_contents = n_contents self.contents = range(1, n_contents + 1) self.alpha = alpha self.rate = rate self.n_warmup = n_warmup self.n_measured = n_measured random.seed(seed) self.beta = beta self.local = local self.transit = transit self.ingress = ingress self.egress = egress if beta != 0: degree = nx.degree(self.topology) self.receivers = sorted(self.receivers, key=lambda x: degree[iter(topology.edge[x]).next()], reverse=True) self.receiver_dist = TruncatedZipfDist(beta, len(self.receivers)) def __iter__(self): self.local_contents = list(topology.graph['internal_contents']) self.remote_contents = list(topology.graph['edge_contents']) self.local_receivers = topology.graph['internal_receivers'] self.remote_receivers = topology.graph['edge_receivers'] self.zipf_local = TruncatedZipfDist(alpha, len(self.local_contents)) self.zipf_remote = TruncatedZipfDist(alpha, len(self.transit_contents)) req_counter = 0 t_event = 0.0 while req_counter < self.n_warmup + self.n_measured: t_event += (random.expovariate(self.rate)) x = random.random() content = -1 receiver = -1 if x < self.transit: # transit traffic receiver = random.choice(self.remote_receivers) indx = int(self.zipf_remote.rv()) content = self.remote_contents[indx] elif x < self.transit + self.local: # local traffic receiver = random.choice(self.local_receivers) indx = int(self.zipf_local.rv()) content = self.local_contents[indx] elif x < self.transit + self.local + self.ingress: # ingress traffic receiver = random.choice(self.remote_receivers) indx = int(self.zipf_local.rv()) content = self.local_contents[indx] else: # egress traffic receiver = random.choice(self.local_receivers) indx = int(self.zipf_remote.rv()) content = self.remote_contents[indx] #if self.beta == 0: # receiver = random.choice(self.receivers) #else: # receiver = self.receivers[self.receiver_dist.rv() - 1] #content = int(self.zipf.rv()) log = (req_counter >= self.n_warmup) event = {'receiver': receiver, 'content': content, 'log': log} yield (t_event, event) req_counter += 1 raise StopIteration()
class StationaryWorkload(object): """This function generates events on the fly, i.e. instead of creating an event schedule to be kept in memory, returns an iterator that generates events when needed. This is useful for running large schedules of events where RAM is limited as its memory impact is considerably lower. These requests are Poisson-distributed while content popularity is Zipf-distributed All requests are mapped to receivers uniformly unless a positive *beta* parameter is specified. If a *beta* parameter is specified, then receivers issue requests at different rates. The algorithm used to determine the requests rates for each receiver is the following: * All receiver are sorted in decreasing order of degree of the PoP they are attached to. This assumes that all receivers have degree = 1 and are attached to a node with degree > 1 * Rates are then assigned following a Zipf distribution of coefficient beta where nodes with higher-degree PoPs have a higher request rate Parameters ---------- topology : fnss.Topology The topology to which the workload refers n_contents : int The number of content object alpha : float The Zipf alpha parameter beta : float, optional Parameter indicating rate : float, optional The mean rate of requests per second n_warmup : int, optional The number of warmup requests (i.e. requests executed to fill cache but not logged) n_measured : int, optional The number of logged requests after the warmup Returns ------- events : iterator Iterator of events. Each event is a 2-tuple where the first element is the timestamp at which the event occurs and the second element is a dictionary of event attributes. """ def __init__(self, topology, n_contents, n_segments, time_interval, alpha, beta=0, rate=1.0, n_warmup=10**5, n_measured=4 * 10**5, seed=None, **kwargs): if alpha < 0: raise ValueError('alpha must be positive') if beta < 0: raise ValueError('beta must be positive') self.receivers = [ v for v in topology.nodes_iter() if topology.node[v]['stack'][0] == 'receiver' ] self.zipf = TruncatedZipfDist(alpha, n_contents / n_segments) self.time_interval = time_interval self.n_contents = n_contents self.n_segments = n_segments self.contents = range(1, n_contents + 1) # A list of all segments. self.delay = 0.01 self.alpha = alpha self.rate = rate self.n_warmup = n_warmup self.n_measured = n_measured random.seed(seed) self.beta = beta if beta != 0: degree = nx.degree(self.topology) self.receivers = sorted( self.receivers, key=lambda x: degree[iter(topology.edge[x]).next()], reverse=True) self.receiver_dist = TruncatedZipfDist(beta, len(self.receivers)) def __iter__(self): req_counter = 0 t_event = 0.0 event_dict = dict() # Dictionary: key=time, value=event object time_heap = [] # Heap_queue: item=time while req_counter <= self.n_warmup + self.n_measured: t_event += (random.expovariate(self.rate)) event_time = time_heap[0] if len(time_heap) > 0 else None while event_time is not None and event_time < t_event: event = event_dict[event_time] yield (event_time, event) heapq.heappop(time_heap) # Remove the time from heapq. del event_dict[ event_time] # Remove the time-event pair from dictionary. # If it is not the last segment, append the event for next segment. if event['content'] % self.n_segments != 0: new_event_time = event_time + self.delay new_event = copy.copy(event) new_event['content'] += 1 heapq.heappush(time_heap, new_event_time) event_dict[new_event_time] = new_event event_time = time_heap[0] if len(time_heap) > 0 else None if req_counter == (self.n_warmup + self.n_measured): # Exit the method when there is no pending event and all requests are sent. if len(time_heap) == 0: break else: continue if self.beta == 0: receiver = random.choice(self.receivers) else: receiver = self.receivers[self.receiver_dist.rv() - 1] content = int(self.zipf.rv()) content = ( content - 1 ) * self.n_segments + 1 # This gives the first segment of the content. log = (req_counter >= self.n_warmup) event = { 'receiver': receiver, 'content': content, 'n_segments': self.n_segments, 'time_interval': self.time_interval, 'log': log } yield (t_event, event) # If it is not the last segment, append the event (to heapq) for next segment. if event['content'] % self.n_segments != 0: new_event_time = t_event + self.delay new_event = copy.copy(event) new_event['content'] += 1 heapq.heappush(time_heap, new_event_time) event_dict[new_event_time] = new_event req_counter += 1 raise StopIteration()
class StationaryUpdated(object): '''Represents a content as a multiple objects, while keeping track of the link_delay ''' def __init__(self, topology, n_contents, alpha, chunks_per_content, beta=0, rate=1.0, n_warmup=10**5, n_measured=4 * 10**5, seed=None, **kwargs): if alpha < 0: raise ValueError('alpha must be positive') if beta < 0: raise ValueError('beta must be positive') self.model = None self.controller = None self.contents = [] self.receivers = [ v for v in topology.nodes() if topology.node[v]['stack'][0] == 'receiver' ] self.zipf = TruncatedZipfDist(alpha, n_contents) self.n_contents = n_contents self.chunks_per_content = chunks_per_content self.contents = [] self.n_chunks = [] if (sum([pair[0] for pair in self.chunks_per_content]) != 100): raise ValueError('The percents should add up to a 100') for pair in self.chunks_per_content: for j in range(self.n_contents * pair[0] / 100): self.n_chunks.append(pair[1]) if (len(self.n_chunks) != self.n_contents): for j in range(len(self.n_chunks), self.n_contents): self.n_chunks.append(self.chunks_per_content[-1][-1]) if (self.n_contents != len(self.n_chunks)): raise ValueError('n_chunks must be the same length as n_contents ') # define a two-dimensional array which stores the contents for easier index manipulations self.content_objects = [[ Content(i, j) for j in range(self.n_chunks[i]) ] for i in range(self.n_contents)] # create a list of the two-dimensional array for i in range(self.n_contents): for j in range(self.n_chunks[i]): self.content_objects[i][j].chunks = self.n_chunks[i] self.contents.append(self.content_objects[i][j]) self.alpha = alpha self.rate = rate self.n_warmup = n_warmup self.n_measured = n_measured random.seed(seed) self.beta = beta if beta != 0: degree = nx.degree(self.topology) self.receivers = sorted( self.receivers, key=lambda x: degree[iter(topology.adj[x]).next()], reverse=True) self.receiver_dist = TruncatedZipfDist(beta, len(self.receivers)) def __iter__(self): req_counter = 0 t_content = 0.0 t_total = 0.0 t_arrival = 0.01 while req_counter < self.n_warmup + self.n_measured: # check whether the list is empty if not (self.model.queue_event): t_total = t_content if ( t_total >= t_content ): # if download of current content interrupted with new request t_content += (random.expovariate(self.rate)) req_counter += 1 if self.beta == 0: receiver = random.choice(self.receivers) else: receiver = self.receivers[self.receiver_dist.rv() - 1] # choose the content randomly new_content = int(self.zipf.rv() - 1) log = (req_counter >= self.n_warmup) # push new objects into heap for t in range(self.n_chunks[new_content]): add_event = Event() packet = Packet() packet.receiver = receiver packet.current_node = receiver packet.content = self.content_objects[new_content][t] t_total += t_arrival add_event.timing = t_total add_event.log = log add_event.packet = packet self.controller.queue_push(add_event) else: event_dispatched = self.controller.queue_pop() t_total = event_dispatched.timing event = { 'packet': event_dispatched.packet, 'log': event_dispatched.log, 'type': event_dispatched.type_chunk, 'session_id': event_dispatched.session_id } yield (t_total, event) raise StopIteration()