def test_remove_two_servers_while_request_in_progress(): sim = SimulatorKernel(outputDirectory = None) server1 = MockServer(sim, latency = 10) server2 = MockServer(sim, latency = 10) server3 = MockServer(sim, latency = 10) lb = LoadBalancer(sim) lb.algorithm = 'SQF' lb.addBackend(server1) lb.addBackend(server2) lb.addBackend(server3) onShutdownCompleted = Mock() r1 = Request() r1.onCompleted = Mock() r2 = Request() r2.onCompleted = Mock() r3 = Request() r3.onCompleted = Mock() sim.add(0, lambda: lb.request(r1)) sim.add(0, lambda: lb.request(r2)) sim.add(0, lambda: lb.request(r3)) sim.add(1, lambda: lb.removeBackend(server1, onShutdownCompleted)) sim.add(2, lambda: lb.removeBackend(server2, onShutdownCompleted)) sim.run() r1.onCompleted.assert_called_once_with() r2.onCompleted.assert_called_once_with() r3.onCompleted.assert_called_once_with() assert onShutdownCompleted.call_count == 2 assert server1.numSeenRequests == 1 assert server2.numSeenRequests == 1 assert server3.numSeenRequests == 1
def test_with_controller_always_yes(): completedRequests = [] controller = Mock() controller.withOptional.return_value = True, 1 sim = SimulatorKernel(outputDirectory = None) server = Server(sim, serviceTimeY = 10, serviceTimeYVariance = 0, serviceTimeN = 1, serviceTimeNVariance = 0) server.controller = controller r = Request() r.onCompleted = lambda: completedRequests.append(r) sim.add(0, lambda: server.request(r)) r2 = Request() r2.onCompleted = lambda: completedRequests.append(r2) sim.add(0, lambda: server.request(r2)) sim.run() controller.withOptional.assert_called_with() assert set(completedRequests) == set([ r, r2 ]) assert abs(server.getActiveTime() - 20.0) < eps, server.getActiveTime()
def test_remove_while_request_not_in_progress(): sim = SimulatorKernel(outputDirectory = None) server1 = MockServer(sim, latency = 0.1) server2 = MockServer(sim, latency = 0.1) lb = LoadBalancer(sim) lb.addBackend(server1) lb.addBackend(server2) onShutdownCompleted = Mock() def remove_active_server(): if server1.numSeenRequests: lb.removeBackend(server1, onShutdownCompleted) else: lb.removeBackend(server2, onShutdownCompleted) r1 = Request() r1.onCompleted = Mock() sim.add(0, lambda: lb.request(r1)) sim.add(1, lambda: remove_active_server()) sim.add(1, lambda: lb.request(Request())) sim.add(2, lambda: lb.request(Request())) sim.add(2, lambda: lb.request(Request())) sim.run() r1.onCompleted.assert_called_once_with() onShutdownCompleted.assert_called_once_with() assert server1.numSeenRequests == 1 or server2.numSeenRequests == 1 assert server1.numSeenRequests == 3 or server2.numSeenRequests == 3
def request(self, request): newRequest = Request() newRequest.originalRequest = request newRequest.onCompleted = lambda: self.onCompleted(newRequest) self.loadBalancer.request(newRequest) action = self.controller.onRequest(newRequest) self.scaleBy(action)
def test_without_controller(): completedRequests = [] sim = SimulatorKernel(outputDirectory = None) server = Server(sim, serviceTimeY = 1, serviceTimeYVariance = 0) r = Request() r.onCompleted = lambda: completedRequests.append(r) sim.add(0, lambda: server.request(r)) r2 = Request() r2.onCompleted = lambda: completedRequests.append(r2) sim.add(0, lambda: server.request(r2)) sim.run() assert set(completedRequests) == set([ r, r2 ]) assert server.getActiveTime() == 2.0, server.getActiveTime()
def request(self, request): #self.sim.log(self, "Got request {0}", request) request.arrival = self.sim.now if self.algorithm in [ 'weighted-RR', 'theta-diff', 'theta-diff-plus', 'equal-thetas', 'ctl-simplify' ]: chosenBackendIndex = \ weightedChoice(zip(range(0, len(self.backends)), self.weights), self.random) elif self.algorithm == 'equal-thetas-SQF' or self.algorithm == 'equal-thetas-fast' or self.algorithm == 'equal-thetas-fast-mul': # Update controller in the -fast version if self.algorithm == 'equal-thetas-fast' or self.algorithm == 'equal-thetas-fast-mul': dt = self.sim.now - self.lastDecision if dt > 1: dt = 1 for i in range(0, len(self.backends)): # Gain gamma = self.equal_thetas_fast_gain * dt # Calculate the negative deviation from the average e = self.lastThetas[i] - avg(self.lastThetas) # Integrate the negative deviation from the average self.queueOffsets[ i] += gamma * e # + Kp * (e - self.lastThetaErrors[i]) self.lastThetaErrors[i] = e self.lastDecision = self.sim.now # To prevent starvation, choose a random empty server.. empty_servers = [i for i in range(0, len(self.queueLengths)) \ if self.queueLengths[i] == 0] if empty_servers: chosenBackendIndex = self.random.choice(empty_servers) else: if self.algorithm == 'equal-thetas-fast-mul': # ...or choose replica with shortest (queue * 2 ** queueOffset) chosenBackendIndex = \ min(range(0, len(self.queueLengths)), \ key = lambda i: self.queueLengths[i] * (2 ** (-self.queueOffsets[i]))) else: # ...or choose replica with shortest (queue + queueOffset) chosenBackendIndex = \ min(range(0, len(self.queueLengths)), \ key = lambda i: self.queueLengths[i]-self.queueOffsets[i]) elif self.algorithm == 'theta-diff-plus-SQF': # choose replica with shortest (queue + queueOffset) chosenBackendIndex = \ min(range(0, len(self.queueLengths)), \ key = lambda i: self.queueLengths[i]-self.queueOffsets[i]) pass elif self.algorithm == 'random': # round robin chosenBackendIndex = \ self.random.choice(range(0, len(self.backends))) elif self.algorithm == 'RR': # round robin chosenBackendIndex = \ (self.numRequests % len(self.backends)) - 1 elif self.algorithm == 'SQF': # choose replica with shortest queue chosenBackendIndex = \ min(range(0, len(self.queueLengths)), \ key = lambda i: self.queueLengths[i]) elif self.algorithm == 'SQF-plus': # choose replica with shortest queue minIndices = [ i for i, x in enumerate(self.queueLengths) if x == min(self.queueLengths) ] if len(minIndices) == 1: chosenBackendIndex = minIndices[0] else: dimmers = [self.lastThetas[i] for i in minIndices] maxDimmerIndex = dimmers.index(max(dimmers)) chosenBackendIndex = minIndices[maxDimmerIndex] elif self.algorithm == '2RC': maxlat = [max(x) if x else 0 for x in self.lastLatencies] if len(self.backends) == 1: chosenBackendIndex = 0 # randomly select two backends and send it to the one with lowest latency else: backends = set(range(0, len(self.backends))) randomlychosen = self.random.sample(backends, 2) if maxlat[randomlychosen[0]] > maxlat[randomlychosen[1]]: chosenBackendIndex = randomlychosen[1] else: chosenBackendIndex = randomlychosen[0] elif self.algorithm == 'FRF': # choose replica with minimum latency maxlat = [max(x) if x else 0 for x in self.lastLatencies] chosenBackendIndex = \ maxlat.index(min(maxlat)) elif self.algorithm == 'FRF-EWMA': # choose replica with minimum EWMA latency #self.sim.log(self, "EWMA RT {0}", self.ewmaResponseTime) chosenBackendIndex = \ min(range(0, len(self.backends)), \ key = lambda i: self.ewmaResponseTime[i]) elif self.algorithm == 'predictive': maxlat = np.array([max(x) if x else 0 for x in self.lastLatencies]) maxlatLast = np.array( [max(x) if x else 0 for x in self.lastLastLatencies]) wlat = 0.2 wqueue = 0.8 points = wlat * (maxlat - maxlatLast) + wqueue * ( np.array(self.queueLengths) - np.array(self.lastQueueLengths)) # choose replica with shortest queue chosenBackendIndex = \ min(range(0, len(points)), \ key = lambda i: points[i]) elif self.algorithm == "SRTF": # choose replica with shortest "time" queue #chosenBackendIndex = \ # min(range(0, len(self.backends)), \ # key = lambda i: sum([r.remainingTime if hasattr(r, 'remainingTime') else 0 for r in self.backends[i].activeRequests])) chosenBackendIndex = \ min(range(0, len(self.queueLengths)), \ key = lambda i: self.queueLengths[i] * (self.backends[i].serviceTimeY * self.lastThetas[i] + self.backends[i].serviceTimeN * (1 - self.lastThetas[i]))) elif self.algorithm == 'theta-diff-plus-fast': dt = self.sim.now - self.lastDecision if dt > 1: dt = 1 for i in range(0, len(self.backends)): # Gain Kp = 0.25 Ti = 5.0 gammaTr = .01 # PI control law e = self.lastThetas[i] - self.lastLastThetas[i] self.queueOffsets[i] += (Kp * e + (Kp / Ti) * self.lastThetas[i]) * dt # Anti-windup self.queueOffsets[i] -= gammaTr * (self.queueOffsets[i] - self.queueLengths[i]) * dt self.lastThetaErrors[i] = e self.lastDecision = self.sim.now # choose replica with shortest (queue + queueOffset) chosenBackendIndex = \ min(range(0, len(self.queueLengths)), \ key = lambda i: self.queueLengths[i]-self.queueOffsets[i]) else: raise Exception("Unknown load-balancing algorithm " + self.algorithm) request.chosenBackend = self.backends[chosenBackendIndex] newRequest = Request() newRequest.originalRequest = request newRequest.onCompleted = lambda: self.onCompleted(newRequest) #self.sim.log(self, "Directed request to {0}", chosenBackendIndex) self.queueLengths[chosenBackendIndex] += 1 self.numRequestsPerReplica[chosenBackendIndex] += 1 self.backends[chosenBackendIndex].request(newRequest)