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python/ICPCP.py

+'''
+Created on Nov 20, 2015
+
+@author: junchao
+'''
+
+import os
+import sys
+import re
+import random
+import networkx as nx
+import numpy as np
+import json
+from subprocess import call
+import time
+from NewInstance import NewInstance       
+import collections
+
+class Workflow():     
+    
+    def add_entry_exit(self, g):
+        # add entry node to the graph
+        g.add_node(0, est = -1, eft = -1, lft =-1)
+        for vertex in g:
+            if len(g.predecessors(vertex)) == 0 and not vertex == 0:   
+                self.G.add_weighted_edges_from([(0, vertex, 0)])
+        # add exit node to the graph
+        g.add_node(self.vertex_num-1, est = -1, eft = -1, lft =-1)
+        startnodes = []
+        for vertex in g:
+            if len(g.successors(vertex)) == 0 and not vertex == self.vertex_num-1:   
+                startnodes.append(vertex)
+                
+        for node in startnodes:        
+            self.G.add_weighted_edges_from([(node, self.vertex_num-1, 0)])
+
+    def init(self, content):
+        self.G = nx.DiGraph()
+        self.vertex_num =0 
+         
+        for (key, value) in content['workflow'].items():
+            if isinstance(value, list) :
+                if key == 'nodes' :
+                    self.vertex_num = len(value)
+                    for node in value:
+                        self.G.add_node(node['name'], est = -1, eft = -1, lft =-1)    
+                if key == 'links' :
+                    for link in value:
+                        self.G.add_weighted_edges_from([(link['source'], link['target'], link['weight'])])
+        print self.G.nodes
+        
+        #parse the performance matrix
+        p = []
+        od = collections.OrderedDict(sorted(content['performance'].items()))
+        for (key, value) in od.items():
+            row = []
+            row.append(0)
+            for i in value.split(','):
+                row.append(int(i))
+            row.append(0)
+            print row
+            p.append(row)
+        self.p_table = np.matrix(p)
+        #parse the price vector
+        self.vm_price = []
+        for i in content['price'].split(','):
+            self.vm_price.append(int(i))
+        
+        #parse the deadline
+        self.d_list = []
+        for (key, value) in content['deadline'].items():
+            self.d_list.append([int(key), int(value)])
+        self.d_table = np.matrix(self.d_list)
+        self.successful = 0
+     
+        self.vertex_num += 2
+        self.add_entry_exit(self.G) 
+        
+        #test whether the DAG contains cycles
+        if len(list(nx.simple_cycles(self.G))) > 0:
+            print "the DAG contains cycles"
+            sys.exit()
+
+        self.assigned_list = [-1]*(self.vertex_num)
+                
+        self.instances = []
+        self.G.node[0]['est'] = 0
+        self.G.node[0]['eft'] = 0
+        self.cal_est(0)
+        self.G.node[self.vertex_num-1]['lft'] = self.d_table[self.d_table.shape[0]-1,1]#self.p_table[0, child], self.p_table.shape[0]
+        self.cal_lft(self.vertex_num-1)
+    
+    def init1(self, workflow_file_name, performance_file_name, price_file_name, deadline_file_name):
+        
+        #Initialization
+        self.G=nx.DiGraph()
+        self.vertex_num = 0
+        self.successful = 0
+        #Read the workflow information
+
+        graph = pydot.graph_from_dot_file(workflow_file_name)
+        nx_graph = nx.from_pydot(graph)
+        self.G=nx.DiGraph()
+        for node in nx_graph:
+            #print node
+            self.G.add_node(int(node)+1, est = -1, eft = -1, lft =-1)
+            self.vertex_num += 1 
+        #print nx_graph.edge
+        #print workflow_file_name
+        for link in nx_graph.edges_iter():
+            #print link[0], link[1]
+            self.G.add_weighted_edges_from([(int(link[0])+1, int(link[1])+1, int(float(nx_graph[link[0]][link[1]][0]['weight'])))])   
+        
+        self.vertex_num += 2
+        self.add_entry_exit(self.G) 
+        
+        #test whether the DAG contains cycles
+        if len(list(nx.simple_cycles(self.G))) > 0:
+            print "the DAG contains cycles"
+            sys.exit()
+        #read performance table
+        l = [ map(int,line.split(',')) for line in open(performance_file_name, 'r')]
+        #append the entry and exit node information
+        for row in l:
+            row.insert(0, 0)
+            row.insert(len(row),0)
+        self.p_table = np.matrix(l)
+        
+        self.assigned_list = [-1]*(self.vertex_num)
+        
+        self.vm_price = map(int,open(price_file_name,'r').readline().split(','))
+        
+        self.instances = []
+        
+        self.d_list = [ map(int,line.split('\t')) for line in open(deadline_file_name, 'r')]
+        tmpList = self.d_list[len(self.d_list)-1]
+        self.d_table = np.matrix(tmpList)
+        #deadline = open(deadline_file_name, 'r').readline()  
+        self.G.node[0]['est'] = 0
+        self.G.node[0]['eft'] = 0
+        self.cal_est(0)
+        self.G.node[self.vertex_num-1]['lft'] = self.d_table[self.d_table.shape[0]-1,1]#self.p_table[0, child], self.p_table.shape[0]
+        self.cal_lft(self.vertex_num-1)
+        
+    #The following two functions are to initialize the EST, EFT and LFT
+    #calculate the earliest start time and earliest finish time    
+    def cal_est(self, i):
+        for child in self.G.successors(i):
+            est = self.G.node[i]['eft']+self.G[i][child]['weight']
+            if est>self.G.node[child]['est']:
+                self.G.node[child]['est'] = est
+                self.G.node[child]['eft'] = est + self.p_table[0, child]
+                self.cal_est(child)
+    
+    def cal_lft(self, d):
+        for parent in self.G.predecessors(d):
+            lft  = self.G.node[d]['lft'] - self.p_table[0, d] - self.G[parent][d]['weight']
+            d_table_list = []
+            for deadline in self.d_table:
+                d_table_list.append(deadline[0, 0])
+                
+            if parent in d_table_list:
+                deadline = self.d_table[d_table_list.index([parent]),1]
+                if deadline < lft:
+                    lft = deadline
+            if self.G.node[parent]['lft'] == -1 or lft<self.G.node[parent]['lft']:
+                # parent may not finish later
+                self.G.node[parent]['lft'] = lft
+                #print "call graphAssignLFT(",graph.node[parent]['name'],")"
+                self.cal_lft(parent)     
+    
+    #Finding critical path
+    def ic_pcp(self):
+        self.assigned_list[0] = 0
+        self.assigned_list[self.vertex_num-1] = 0  
+        self.assign_parents(self.vertex_num-1)
+    
+    def has_unassigned_parent(self, i):
+        for parent in self.G.predecessors(i): 
+            if (self.assigned_list[parent] == -1): 
+                return True
+        return False
+                    
+    def assign_parents(self, i):  
+        while (self.has_unassigned_parent(i)):  
+            if self.successful == 1:   #resources cannot be met
+                break            
+            pcp = []
+            self.find_critical_path(i, pcp)
+            
+            assigned_vms = self.assign_path(pcp)     
+            
+            if -1 in assigned_vms:
+                print 'resource cannot be met'
+                break
+            self.G.node[pcp[len(pcp)-1]]['eft'] = self.G.node[pcp[len(pcp)-1]]['est'] + self.p_table[assigned_vms[len(pcp)-1], pcp[len(pcp)-1]]
+            self.update_est(pcp[len(pcp)-1], pcp, assigned_vms)
+            self.update_lft(pcp[0], pcp, assigned_vms)
+            #split according to the types of assigned VMs and add it to the new instance
+            ni = NewInstance(assigned_vms, self.G.node[pcp[len(pcp)-1]]['est'], self.G.node[pcp[0]]['eft'], pcp)
+            
+            for j in xrange(len(pcp)):
+                ni.cost = ni.cost + self.vm_price[assigned_vms[j]]
+            
+            self.instances.append(ni)                 
+            for j in reversed(pcp):                 #also in the paper they didn't mention the order
+                self.assign_parents(j)
+                    
+    #TODO: A very tricky thing on updating the EST and EFT.
+       
+    def update_est(self, i, pcp, assigned_vms):
+        for child in self.G.successors(i):
+            if child not in pcp:
+                est = self.G.node[i]['eft']+self.G[i][child]['weight']
+                
+                if self.assigned_list[i] == -1:
+                    eft = est + self.p_table[0, child]
+                else:
+                    eft = est + self.p_table[self.assigned_list[child], child]
+            else:
+                if pcp.index(child) == len(pcp) - 1:
+                    est = self.G.node[i]['eft'] 
+                    eft = est + self.p_table[self.assigned_list[child], child]
+                else:
+                    pos = pcp.index(child)
+                    est = self.G.node[i]['eft'] + self.G[pcp[pos+1]][pcp[pos]]['weight']
+                    eft = est + self.p_table[self.assigned_list[child], child] 
+                                                
+            #decide whether the assignment will violate other parent data dependency            
+            all_smaller = True
+            for parent in self.G.predecessors(child):
+                if not parent == i:
+                    if self.G.node[parent]['eft'] + self.G[parent][child]['weight'] > est:   
+                        all_smaller = False
+            if all_smaller:
+                self.G.node[child]['est'] = est
+                self.G.node[child]['eft'] = eft
+                self.update_est(child, pcp, assigned_vms)         
+                      
+    def update_lft(self, d, pcp, assigned_vms):        
+        for parent in self.G.predecessors(d):            
+            if parent not in pcp:                
+                if self.assigned_list[d] == -1:
+                    lft = self.G.node[d]['lft'] - self.p_table[0, d] - self.G[parent][d]['weight']  
+                else:
+                    lft = self.G.node[d]['lft'] - self.p_table[self.assigned_list[d], d] - self.G[parent][d]['weight']           
+            else:
+                pos = pcp.index(parent)
+                #if pos < len(pcp) -1:
+                if assigned_vms[pos] == assigned_vms[pos-1]:#9, 6, 2
+                    lft = self.G.node[d]['lft'] - self.p_table[self.assigned_list[d], d]
+                else:
+                    lft = self.G.node[d]['lft'] - self.p_table[self.assigned_list[d], d] - self.G[pcp[pos]][pcp[pos-1]]['weight']
+ 
+            if lft < self.G.node[parent]['lft']:
+                self.G.node[parent]['lft'] = lft
+                self.update_lft(parent, pcp, assigned_vms)    
+
+
+    def find_critical_path(self, i, pcp):
+        cal_cost = 0
+        critical_parent = -1
+        for n in self.G.predecessors(i):
+            if self.assigned_list[n] == -1: #parent of node i is not assigned
+                if self.G.node[n]['eft'] + self.G.edge[n][i]['weight'] > cal_cost:
+                    cal_cost = self.G.node[n]['eft'] + self.G.edge[n][i]['weight']
+                    critical_parent = n
+        if not critical_parent == -1:
+            pcp.append(critical_parent)   
+            self.find_critical_path(critical_parent, pcp)
+    
+    def exec_time_sum(self, pcp, vm_type):
+        sum = 0
+        for i in pcp:
+            sum += self.p_table[vm_type, i]
+        return sum
+    
+    #look forward one step when assigning a vm to a pcp how the est varies
+    def est_vary(self, pcp, d):
+        head_pcp = pcp[len(pcp)-1]  
+        original_est = self.G.node[head_pcp]['est']       
+        biggest_est = -1 
+        biggest_parent = -1
+        for parent in self.G.predecessors(head_pcp):
+            if parent == d:
+                est = self.G.node[parent]['eft']
+            else:
+                est = self.G.node[parent]['eft'] + self.G[parent][head_pcp]['weight']
+            if biggest_est < est:
+                biggest_est = est
+                biggest_parent =  parent
+        return original_est-biggest_est
+            
+    #choose the best existing available instance for the pcp
+    def choose_exist_instance(self, pcp):
+        best_vm = None
+        best_exec_time = -1
+        best_vary_time = -1
+        for vm in self.instances:
+            head_pcp = pcp[len(pcp)-1]     
+            for parent in self.G.predecessors(head_pcp):
+                head_exist_pcp = vm.task_list[0]   #The last node of the previous critical path
+                if parent == head_exist_pcp:
+                    if best_vm == None:
+                        best_vm = vm
+                        exec_time = self.exec_time_sum(pcp, vm.vm_type)
+                        best_exec_time = exec_time
+                        best_vary_time = self.est_vary(pcp, head_exist_pcp)
+                    else:
+                        best_vm_head = vm.task_list[0]
+                        # if assigned to the vm, what will the est be
+                        exec_time = self.exec_time_sum(pcp, vm.vm_type)
+                        # calculate the lft
+                        varied_time = self.G.node[head_pcp]['est']-self.est_vary(pcp, head_exist_pcp)
+                        lft = varied_time+exec_time
+                        if (exec_time - self.est_vary(pcp, head_exist_pcp))*self.vm_price[vm.vm_type]> \
+                        (best_exec_time - self.est_vary(pcp, best_vm.task_list[0]))*self.vm_price[best_vm.vm_type] \
+                        and lft < self.G.node[head_pcp]['lft']:  #also should not violate the lft
+                            best_vm = vm
+                            best_exec_time = exec_time
+                            best_vary_time = varied_time
+        if not best_vm == None:
+            best_vm.vm_end = self.G.node[pcp[len(pcp)-1]]['est']-best_vary_time+best_exec_time
+        return best_vm
+               
+    def assign_path(self, pcp): 
+        cheapest_vm = -1
+        cheapest_sum = 9999999  #the initialized value should be a very large number
+
+        for i in xrange(self.p_table.shape[0]):   # use the the shape of the performance table to identify how many VM types are there
+            violate_LFT = 0
+            start = self.G.node[pcp[len(pcp)-1]]['est'] 
+            cost_sum = 0
+            for j in xrange(len(pcp)-1, -1, -1):
+                cost_sum += self.vm_price[i]
+                if j == len(pcp)-1:
+                    start = start + self.p_table[i, pcp[j]]
+                else:
+                    start = start + self.p_table[i, pcp[j]]+ self.G[pcp[j+1]][pcp[j]]['weight']
+                    
+                if self.G.node[pcp[j]]['lft'] < start:
+                    violate_LFT = 1
+                #launch a new instance of the cheapest service which can finish each task of P before its LFT
+            if violate_LFT == 0 and cost_sum < cheapest_sum:
+                cheapest_vm = i
+                cheapest_sum = cost_sum
+        for i in xrange(len(pcp)):
+            self.assigned_list[pcp[i]] =  cheapest_vm
+    
+        return [cheapest_vm]*len(pcp)
+  
+     
+    def generate_string(self, node):
+        s = "name "+str(node)+"\n"
+        for i in xrange(len(self.vm_price)):
+            s = s+str(self.p_table[i, node])+"\n"
+        s = s+"assigned vm: " + str(self.assigned_list[node]+1) 
+        return s             
+    
+    def generateJSON(self):
+        content = {}
+        for i in xrange(1, self.vertex_num-1, 1):
+            if self.assigned_list[i] == 0:
+                content[str(i)] = 'large'
+            if self.assigned_list[i] == 1:
+                content[str(i)] = 'Medium'
+            if self.assigned_list[i] == 2:
+                content[str(i)] = 'Small'
+        return content
+    
+    #calculate the total execution cost
+    def cal_cost(self):
+        cost = 0
+        for vm in self.instances:
+            cost = cost + vm.cost
+        return cost
+    
+    def cal_cost1(self):
+        cost = 0
+        for i in range(1, len(self.assigned_list)-1):
+            cost += self.vm_price[self.assigned_list[i]]
+        
+        return cost
+        
+    def has_edge_vm(self, vm1, vm2):
+        for node1 in vm1.task_list:
+            for node2 in vm2.task_list:
+                if self.G.has_edge(node1, node2) or self.G.has_edge(node2, node1):
+                    return True
+        return False
+

python/NewInstance.py

+class NewInstance(object):
+    def __init__(self, vm_type, vm_start, vm_end, pcp):
+        self.vm_type = vm_type
+        self.vm_start = vm_start
+        self.vm_end = vm_end
+        self.task_list = pcp
+        self.cost = 0
\ No newline at end of file

python/rpc_server.py

 #!/usr/bin/env python
 import pika
+import networkx as nx
+import sys
+import numpy as np
+import sys, argparse
+import operator
+import os
+from toscaparser import *
+from toscaparser.tosca_template import ToscaTemplate
+import re
+import getopt
+from ICPCP import Workflow
+import random
+import time
 import json
 
 
@@ -24,7 +37,86 @@ def on_request(ch, method, props, body):
 
     print(" Message %s" % body)
     response = "AAAAAAAAAAAAAAAAAAAAAA"
+    json1 = response.get('parameters')[0].get('value').get('topology_template').get('node_templates')
+    deadline = 0
 
+    for j in json1:
+        #print json[j]
+        if not json1[j]['type'] == "Switch.nodes.Application.Connection":
+            deadline = int(re.search(r'\d+', json1[j]['properties']['QoS']['response_time']).group())
+
+    #get the nodes from the json
+    nodeDic = {}
+    nodeDic1 = {}
+    i = 1
+    for j in json1:
+        if not json1[j]['type'] == "Switch.nodes.Application.Connection":
+            print j, json1[j]
+        nodeDic[j] = i
+        nodeDic1[i] = j
+        i = i + 1
+
+    #get the links from the json
+    links = []
+    for j in json1:
+        if json1[j]['type'] == "Switch.nodes.Application.Connection":
+            print json1[j]['properties']['source']['component_name']
+            print json1[j]['properties']['target']['component_name']
+            link= {}
+            link['source'] = nodeDic[json1[j]['properties']['source']['component_name']]
+            link['target'] = nodeDic[json1[j]['properties']['target']['component_name']]
+            link['weight'] = random.randint(1, 10)
+            links.append(link)
+
+    # compose the json as input of the workflow
+    wfJson = {}
+    wfJson['workflow'] = {}
+
+    nodesList = []
+    sorted_nodeDic = sorted(nodeDic.items(), key=operator.itemgetter(1))
+    for key, value in sorted_nodeDic:
+        v = {}
+        v['name'] = value
+        nodesList.append(v)
+    wfJson['workflow']['nodes'] = nodesList
+    wfJson['workflow']['links'] = links
+    #print deadline
+    
+    wfJson['price'] = "5,2,1"
+    wfJson['deadline'] = {'2': deadline}
+
+    #generate performance
+    performance = {}
+    for key, value in sorted_nodeDic:
+        performance[str(value)] = "1,2,3"
+    wfJson['performance'] = performance
+    
+    print wfJson
+
+    #send request to the server
+    start = time.time()
+    wf = Workflow()
+    wf.init(wfJson)
+    wf.ic_pcp()
+    #print content['workflow']
+    #return 
+    res = wf.generateJSON()
+    
+    end = time.time()
+    print (end - start)
+    
+    # generate the json files in the corresponding format as the 
+    outcontent = {}
+    outcontent["creationDate"] = 1487002029722
+    outcontent["parameters"] = []
+    par1 = {}
+    par1["url"] = "null"
+    par1["encoding"] = "UTF-8"
+    par1["value"] = res
+    par1["attributes"] = "null"
+    outcontent["parameters"].append(par1)
+    
+    response = outcontent
     ch.basic_publish(exchange='',
                      routing_key=props.reply_to,
                      properties=pika.BasicProperties(correlation_id = \

python/server.py

+#https://github.com/skoulouzis/DRIP/blob/package/doc/json_samples/kbExampleMessage.json
+
+import networkx as nx
+import sys
+import numpy as np
+import sys, argparse
+import operator
+import os
+from toscaparser import *
+from toscaparser.tosca_template import ToscaTemplate
+import re
+import getopt
+from ICPCP import Workflow
+import random
+import time
+import json
+
+def main(argv):
+    workflow_file = ""
+    
+    try:
+        opts, args = getopt.getopt(argv,"hw:s:",["workflow=", "SDI="])
+    except getopt.GetoptError:
+        print 'server.py -w <workflowfile> -s <SDI>'
+        sys.exit(2)
+    for opt, arg in opts:
+        if opt == '-h':
+            print 'server.py -w <workflowfile> -s <SDI>'
+            sys.exit()
+        elif opt in ("-w", "--workflow"):
+            workflow_file = arg
+        elif opt in ("-s", "--SDI"):
+            SDI_file = arg
+    
+    data = {}
+    print workflow_file
+    with open(workflow_file) as data_file:    
+        data = json.load(data_file)     
+        #print data         
+        
+    #path = "input.yaml"
+    
+    '''
+    a_file = os.path.isfile(path)
+    tosca = ToscaTemplate(path)
+    #print tosca.tpl
+    json = tosca.tpl.get('topology_template').get('node_templates')
+    #print json
+    '''
+    json1 = data.get('parameters')[0].get('value').get('topology_template').get('node_templates')
+    deadline = 0
+
+    for j in json1:
+        #print json[j]
+        if not json1[j]['type'] == "Switch.nodes.Application.Connection":
+            deadline = int(re.search(r'\d+', json1[j]['properties']['QoS']['response_time']).group())
+
+    #get the nodes from the json
+    nodeDic = {}
+    nodeDic1 = {}
+    i = 1
+    for j in json1:
+        if not json1[j]['type'] == "Switch.nodes.Application.Connection":
+            print j, json1[j]
+        nodeDic[j] = i
+        nodeDic1[i] = j
+        i = i + 1
+
+    #get the links from the json
+    links = []
+    for j in json1:
+        if json1[j]['type'] == "Switch.nodes.Application.Connection":
+            print json1[j]['properties']['source']['component_name']
+            print json1[j]['properties']['target']['component_name']
+            link= {}
+            link['source'] = nodeDic[json1[j]['properties']['source']['component_name']]
+            link['target'] = nodeDic[json1[j]['properties']['target']['component_name']]
+            link['weight'] = random.randint(1, 10)
+            links.append(link)
+
+    # compose the json as input of the workflow
+    wfJson = {}
+    wfJson['workflow'] = {}
+
+    nodesList = []
+    sorted_nodeDic = sorted(nodeDic.items(), key=operator.itemgetter(1))
+    for key, value in sorted_nodeDic:
+        v = {}
+        v['name'] = value
+        nodesList.append(v)
+    wfJson['workflow']['nodes'] = nodesList
+    wfJson['workflow']['links'] = links
+    #print deadline
+    
+    wfJson['price'] = "5,2,1"
+    wfJson['deadline'] = {'2': deadline}
+
+    #generate performance
+    performance = {}
+    for key, value in sorted_nodeDic:
+        performance[str(value)] = "1,2,3"
+    wfJson['performance'] = performance
+    
+    print wfJson
+
+    #send request to the server
+    start = time.time()
+    wf = Workflow()
+    wf.init(wfJson)
+    wf.ic_pcp()
+    #print content['workflow']
+    #return 
+    res = wf.generateJSON()
+    
+    end = time.time()
+    print (end - start)
+    
+    # generate the json files in the corresponding format as the 
+    outcontent = {}
+    outcontent["creationDate"] = 1487002029722
+    outcontent["parameters"] = []
+    par1 = {}
+    par1["url"] = "null"
+    par1["encoding"] = "UTF-8"
+    par1["value"] = res
+    par1["attributes"] = "null"
+    outcontent["parameters"].append(par1)
+    return outcontent
+ 
+    
+if __name__ == '__main__':
+    main(sys.argv[1:])
\ No newline at end of file