Merge pull request #5 from adnan904/master

Minor refactoring/updates

Author: adnan904

README.md

@@ -1,5 +1,27 @@
 # baseline-algorithms
-Baseline algorithms for coordination of chained VNFs. Includes Non-RL algorithms (Random Schedule, Shortest Path, & Load Balance).
+Baseline algorithms for coordination of service mesh consisting of multiple microservices. Includes Non-RL algorithms (Random Schedule, Shortest Path, & Load Balance).
+
+<p align="center">
+    <img src="https://raw.githubusercontent.com/RealVNF/deep-rl-service-coordination/master/docs/realvnf_logo.png?token=AIDTJSQ4PTVX6A4D6HSPDDC6RNE54" height="150" hspace="30"/>
+	<img src="https://raw.githubusercontent.com/RealVNF/deep-rl-service-coordination/master/docs/upb.png?token=AIDTJSXSEB2M2BEFGFU4N3S6RNFGA" width="200" hspace="30"/>
+	<img src="https://raw.githubusercontent.com/RealVNF/deep-rl-service-coordination/master/docs/huawei_horizontal.png?token=AIDTJSSKOEGP7GI6K5YIWUC6RNFH2" width="250" hspace="30"/>
+</p>
+
+
+## Citing this work
+
+If you are using this work in whole or in part in your project, please cite it as follows:
+
+```
+@inproceedings{schneider2020coordination,
+	author = {Schneider, Stefan and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Uthe, Sven and Karl, Holger and Hecker, Artur},
+	booktitle = {ACM SIGCOMM NetAI Workshop 2020 (SIGCOMM NetAI ’20)},
+	pages = {1--8},
+	title = {Self-Driving Coordination of Massively Scaled Services Using Deep Reinforcement Learning},
+	year = {2020},
+	note = {under review}
+}
+```
 
 ## Project structure
 
@@ -16,7 +38,7 @@ Baseline algorithms for coordination of chained VNFs. Includes Non-RL algorithms
 
 ### Load Balance algorithm
 
-Always returns equal distribution for all nodes and SFs. Places all SFs everywhere.
+Always returns equal distribution for all nodes having capacities and SFs. Places all SFs on  all nodes having some capacity.
 
 ### Shortest Path algorithm
 
@@ -70,3 +92,12 @@ bash scripts/run_simultaneous
 ```
 
 This would link the paths in the `network_files`, `config_files`, and `service_files`, as per line number and then run them for all the seeds within the `30seeds` file. E.g: Network file location at line no. 1 of `network_files` + Simulator Config file location at line no. 1 of `config_files` + SFC file location at line no. 1, would be run for all the seeds within the `30seeds` file. Similary for line numbers 2,3, and so on.
+
+## Acknowledgement
+
+This project has received funding from German Federal Ministry of Education and Research ([BMBF](https://www.bmbf.de/)) through Software Campus grant 01IS17046 ([RealVNF](https://realvnf.github.io/)).
+
+<p align="center">
+	<img src="https://raw.githubusercontent.com/RealVNF/deep-rl-service-coordination/master/docs/software_campus.png?token=AIDTJSQS7WZNSZHFL23FNYS6RNFJK" width="200"/>
+	<img src="https://raw.githubusercontent.com/RealVNF/deep-rl-service-coordination/master/docs/BMBF_sponsored_by.jpg?token=AIDTJSRMQYSKOC7K3EWIQ5S6RNFKW" width="250"/>
+</p>

res/config/rand-mmp-arrival12-8_det-size001_dur100.yaml

@@ -0,0 +1,28 @@
+# module for configuring the simulator
+# configuration parameters are loaded and used both when using the simulator via the CLI and via the interface
+# all parameters are required, defaults are in comments
+
+inter_arrival_mean: 12.0           # default: 10.0
+deterministic_arrival: False
+flow_dr_mean: 1.0                  # default: 1.0
+flow_dr_stdev: 0.0                 # default: 0.0
+flow_size_shape: 0.001             # default: 0.001
+deterministic_size: True
+run_duration: 100                   # default: 100
+
+# Optional: Trace file trace relative to the CWD.
+# Until values start in the trace file, the defaults from this file are used
+# trace_path: res/traces/scenario09.csv
+
+# States (two state markov arrival)
+# Optional param: states: True | False
+use_states: True
+init_state: state_1
+
+states:
+  state_1:
+    inter_arr_mean: 12.0
+    switch_p: 0.05
+  state_2:
+    inter_arr_mean: 8.0
+    switch_p: 0.05

src/algorithms/loadBalance.py

@@ -92,7 +92,6 @@ def main():
     args = parse_args()
     if not args.seed:
         args.seed = random.randint(1, 9999)
-    # os.makedirs("logs", exist_ok=True)
     logging.basicConfig(level=logging.INFO)
     logging.getLogger("coordsim").setLevel(logging.WARNING)
 
@@ -128,7 +127,6 @@ def main():
     log.info(f"Running for {args.iterations} iterations...")
     for i in tqdm(range(args.iterations)):
         _ = simulator.apply(action)
-        # log.info("Network Stats after apply() # %s: %s", i + 1, apply_state.network_stats)
     # We copy the input files(network, simulator config....) to  the results directory
     copy_input_files(results_dir, os.path.abspath(args.network), os.path.abspath(args.service_functions),
                      os.path.abspath(args.config))

src/algorithms/randomSchedule.py

@@ -14,9 +14,6 @@
 from spinterface import SimulatorAction
 from tqdm import tqdm
 
-# select which simulator to use by (un-)commenting the corresponding imports
-# from dummy_env import DummySimulator as Simulator
-
 log = logging.getLogger(__name__)
 DATETIME = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
 PROJECT_ROOT = str(Path(__file__).parent.parent.parent)
@@ -99,7 +96,6 @@ def main():
     args = parse_args()
     if not args.seed:
         args.seed = random.randint(1, 9999)
-    # os.makedirs("logs", exist_ok=True)
     logging.basicConfig(level=logging.INFO)
     logging.getLogger("coordsim").setLevel(logging.WARNING)
 

src/algorithms/shortestPath.py

@@ -18,8 +18,17 @@
 
 
 def get_closest_neighbours(network, nodes_list):
-    # Finding the closest neighbours to each node in the network. For each node of the network we maintain a list of
-    # neighbours sorted in increasing order of distance to it.
+    """
+    Finding the closest neighbours to each node in the network. For each node of the network we maintain a list of
+    neighbours sorted in increasing order of distance to it.
+    params:
+        network: A networkX graph
+        nodes_list: a list of nodes in the Network
+    Returns:
+         closest_neighbour: A dict containing lists of closest neighbour to each node in the network sorted in
+                            increasing order to distance.
+    """
+
     all_pair_shortest_paths = network.graph['shortest_paths']
     closest_neighbours = defaultdict(list)
     for source in nodes_list:
@@ -35,12 +44,13 @@ def get_closest_neighbours(network, nodes_list):
 
 def next_neighbour(index, num_vnfs_filled, node, placement, closest_neighbours, sf_list, nodes_cap):
     """
+    Finds the next available neighbour to the index node
     Args:
         index: closest neighbours of 'node' is a list, index tells which closest neighbour to start looking from
         num_vnfs_filled: Tells the number of VNFs present on all nodes e.g: every node in the network has atleast 1 VNF,
                           some might have more than that. This tells us the minimum every node has
         node: The node whose closest neighbour is to be found
-        placement: plaecement of VNFs in the entire network
+        placement: placeement of VNFs in the entire network
         closest_neighbours: neighbours of each node in the network in the increasing order of distance
         sf_list: The VNFs in the network
         nodes_cap: Capacity of each node in the network
@@ -180,7 +190,6 @@ def main():
     args = parse_args()
     if not args.seed:
         args.seed = random.randint(1, 9999)
-    # os.makedirs("logs", exist_ok=True)
     logging.basicConfig(level=logging.INFO)
     logging.getLogger("coordsim").setLevel(logging.WARNING)
 
@@ -211,7 +220,6 @@ def main():
     log.info(f"Running for {args.iterations} iterations...")
     for i in tqdm(range(args.iterations)):
         _ = simulator.apply(action)
-        # log.info("Network Stats after apply() # %s: %s", i + 1, apply_state.network_stats)
     # We copy the input files(network, simulator config....) to  the results directory
     copy_input_files(results_dir, os.path.abspath(args.network), os.path.abspath(args.service_functions),
                      os.path.abspath(args.config))