| 134 | | Same as Part (2) PI Controller in [wiki:GENIExperimenter/Tutorials/NFV/Ryu/LoadBalancePIwithRyu Experiment 2]. |
| | 135 | (Same as Part (2) PI Controller in [wiki:GENIExperimenter/Tutorials/NFV/Ryu/LoadBalancePIwithRyu Experiment 2]. ) |
| | 136 | |
| | 137 | {{{ |
| | 138 | #!html |
| | 139 | <table id="Table_02" width = "1150" border="0" cellpadding="0" cellspacing="10" > |
| | 140 | <tr> |
| | 141 | <td> <img src = "http://csr.bu.edu/rina/grw-bu2016/tutorial_files/image068.gif" > </td> |
| | 142 | <td> The PI-controller gets the load information of VNF1 and VNF2 using RINA's distributed application and makes the load balancing decision. |
| | 143 | </td></tr></table> |
| | 144 | }}} |
| | 145 | |
| | 146 | |
| | 147 | |
| | 148 | |
| | 149 | The figure below shows the block diagram of the Proportional Integral (PI) controlled NFV system. |
| | 150 | |
| | 151 | {{{ |
| | 152 | #!html |
| | 153 | |
| | 154 | <table id="Table_02" width = "1150" border="0" cellpadding="0" cellspacing="0" align="center" > |
| | 155 | <tr> |
| | 156 | <td><img src="http://csr.bu.edu/rina/grw-bu2016/tutorial_files/image074.gif" hspace=2/></td></tr> |
| | 157 | |
| | 158 | <tr><td> <i style="font-size:9px">Block diagram of the PI-controller NFV system. System load L and target load T(s)=T/s of VNF1 is used to compute X, i.e. ratio of traffic diverted to VNF2. K` = K/T.</i></td></tr> |
| | 159 | </table> |
| | 160 | |
| | 161 | }}} |
| | 162 | |
| | 163 | The RINA-based distributed monitoring application provides the VNF1 state (average CPU load) information L(t) to the PI controller. The maximum capacity of a VNF instance is T. If the load on VNF1 exceeds T, new traffic flows are forwarded to a second VNF instance, VNF2. Assuming instantaneous feedback / measured load L(t), the PI control equation is given by: |
| | 164 | |
| | 165 | |
| | 166 | {{{ |
| | 167 | #!html |
| | 168 | <img src="http://csr.bu.edu/rina/grw-bu2016/tutorial_files/image076.gif" hspace=70> |
| | 169 | |
| | 170 | }}} |
| | 171 | |
| | 172 | The code for the PI controller is based on the following algorithm. Input ''IDS,,load,,.txt'' is the file generated by the RINA distributed application. This file has load information of the VNFs. |
| | 173 | |
| | 174 | {{{ |
| | 175 | #!html |
| | 176 | <img src="http://csr.bu.edu/rina/grw-bu2016/tutorial_files/image078.gif" hspace=40> |
| | 177 | |
| | 178 | }}} |
| | 179 | |
| | 180 | 1. To run the PI-controller, open a new controller window and execute: |
| | 181 | |
| | 182 | - ''' cd ~/Control/PI_controller ''' |
| | 183 | - ''' python PI_controller.py ~/Control/RINA/NFV1.txt''' |
| | 184 | |
| | 185 | |
| | 186 | |
| | 187 | Note that here we are directing ''PI_controller.py'' to the ''NFV1.txt'' file that is constantly updated by the RINA distributed application with the load information of VNFs. |
| | 188 | 2. You should see the VNF state information printed on the screen. A sample output is shown below. |
| | 189 | |
| | 190 | {{{ |
| | 191 | #!html |
| | 192 | <img src="http://csr.bu.edu/rina/grw-bu2016/nfv_ryu/pics/PIPic.png" hspace=40> |
| | 193 | |
| | 194 | }}} |
| | 195 | Here the target load on VNF1 is 30.0% of CPU usage, i.e. if the CPU load on VNF1 is more than 30.0%, traffic flows will be diverted to VNF2. The `current CPU load` shows the load on VNF1. The next line of the output shows the percentage of flows that will be directed to VNF2 and the last line shows the flows that were being directed to VNF2 before the current control update. |
| | 196 | |
| | 197 | '''Do not close this window; leave the PI controller running.''' |
| | 198 | |
| 137 | | Same as Part (3) PI-based Ryu Controller in [wiki:GENIExperimenter/Tutorials/NFV/Ryu/LoadBalancePIwithRyu Experiment 2]. |
| | 201 | (Same as Part (3) PI-based Ryu Controller in [wiki:GENIExperimenter/Tutorials/NFV/Ryu/LoadBalancePIwithRyu Experiment 2]. ) |
| | 202 | |
| | 203 | Now we will run the Ryu controller that will get the load balancing decision from the PI-controller and direct the flows accordingly. |
| | 204 | |
| | 205 | 1. First we will update the ''port.config'' file to direct the controller to the ''NFV_ratio_PI.txt'' file generated by the PI-controller, which has the load balancing decision information. In a new controller window, execute: |
| | 206 | |
| | 207 | - ''' nano /tmp/ryu/ryu/app/nfv.config''' |
| | 208 | |
| | 209 | o Change the value of '''controller_type''' to '''PI''' [[BR]] |
| | 210 | o Change the value of '''''file_path_pi''''' to the text file that has the PI controller`s output.[[BR]] |
| | 211 | '''''/users/<!UserName>/Control/PI_controller/NFV_ratio_PI.txt'''''[[BR]] |
| | 212 | Change the '''''<!UserName>''''' to your user name.[[BR]] |
| | 213 | |
| | 214 | |
| | 215 | 2. Now we can run the Ryu controller. Execute |
| | 216 | |
| | 217 | - ''' /tmp/ryu/bin/ryu-manager --verbose /tmp/ryu/ryu/app/nfv_controller.py''' |
| | 218 | |
| | 219 | == (4) Run Snort and Generate Traffic == |
| | 220 | |
| | 221 | 1. First we will run Snort IDS on VNF1 and VNF2. In separate windows for VNF1 and VNF2, execute the following command: |
| | 222 | |
| | 223 | - '''sudo /usr/local/bin/snort -A full -dev -i eth1''' |
| | 224 | |
| | 225 | |
| | 226 | |
| | 227 | 2. We will use the iperf application to generate flows between a source and destination. If iperf is not installed on your nodes, execute: |
| | 228 | |
| | 229 | - ''' sudo apt-get install iperf ''' |
| | 230 | |
| | 231 | |
| | 232 | 3. Run iperf server on the destination node: |
| | 233 | |
| | 234 | - ''' iperf -u -s ''' |
| | 235 | |
| | 236 | |
| | 237 | 4. Now we will generate traffic from the sources (s1 and s2) to the destination node using ''iperf'' and see how it effects the CPU utilization at VNF1 and VNF2 running Snort IDS. Note that if we run multiple instances of ''iperf'', we can generate significant load on the VNF instances. To run ''iperf'' client on a source, execute: |
| | 238 | |
| | 239 | - ''' iperf -u -c destination -t 500 & ''' |
| | 240 | |
| | 241 | {{{ |
| | 242 | #!html |
| | 243 | <table id="Table_02" width = "1150" border="0" cellpadding="0" cellspacing="10" > |
| | 244 | <tr> |
| | 245 | <td> <img src = "http://csr.bu.edu/rina/grw-bu2016/tutorial_files/image068.gif" > </td> |
| | 246 | <td> <i>Note that you can run multiple instances of iperf by running <span style="background:#c0c0c0"> iperf -c destination -t 500 & </span> multiple times in s1 and s2 nodes. This flow lasts for 500 seconds. For this experiment, you may try to run 2-3 iperf instances. To kill all the flows generated at a node, run <span style="background:#c0c0c0"> killall –v iperf </span> </i> |
| | 247 | </td></tr></table> |
| | 248 | }}} |
| | 249 | |
| | 250 | 5. Now if you look at the controller window, which is running the PI-controller, you can see the load on VNF1 has significantly increased. If the load is more than 30%, some percentage of the traffic flows will be diverted to VNF2. |
| | 251 | |
| | 252 | {{{ |
| | 253 | #!html |
| | 254 | <table id="Table_02" width = "700" border="0" cellpadding="0" cellspacing="10" > |
| | 255 | <tr> |
| | 256 | <td> <img src = "http://csr.bu.edu/rina/grw-bu2016/tutorial_files/image022.gif"> </td> |
| | 257 | <td> <b>OPTIONAL: Review the PI Ryu Controller Code</b> </td> </tr></table> |
| | 258 | }}} |
| | 259 | |
| | 260 | |
| | 261 | The Ryu controller gets the load balancing information from the output text file generated by the PI-controller. Based on the value of the control variable (variable X), it sends each new flow to either VNF1 or VNF2. The algorithm for the PI-based OVS controller is shown below. |
| | 262 | |
| | 263 | |
| | 264 | {{{ |
| | 265 | #!html |
| | 266 | <img src="http://csr.bu.edu/rina/grw-bu2016/tutorial_files/image092.gif" hspace=40> |
| | 267 | |
| | 268 | }}} |
| | 269 | |
