Smart SDN Management of Fog Services to Optimize QoS and Energy
1.1. Optimization Techniques for SDN Networks and the Fog
1.2. Content of This Paper
- We constructed a new objective or “goal function” for client-to-service allocation, which combines the total response time experienced and measured at the client end (including the round-trip network delay and the service time at the server), plus the power consumed in the SDN network by the client request.
- Since load-dependent power measurements of the actual “NUC” hardware  that we use for each SDN switches is not available in the literature, we conducted accurate power versus traffic load measurements with a specific Hall effect apparatus. We note that the idle power consumption that we measured and report in Figure 1 is not negligible. Indeed while the NUC peak power consumption at maximum load is approximately 30 Watts, the idle consumption is approximately 20 Watts.
- We detailed the adaptive control algorithm based on RL  and a random neural network [46,47,48] that acts as an adaptic critic, using the real-time measurement of the overall service response time, including the round-trip delay to send the request and receive the result through the SDN network as well as the server response time for processing the service request, and the traffic-driven power consumption in the SDN network. Note that others have used the RNN as a tool for controlling the online performance of packet networks and mobile networks [49,50,51,52,53,54].
- This work extends on previous research that only addressed the network aspects with regard to QoS  and QoS and security . We discuss in detail the computational complexity of the algorithm and show that it is where n is the number of different possible connection paths between clients and services.
- The RL algorithm is implemented in the SDN controller, and takes online decisions in real time that minimize the composite goal function.
- We show the effectiveness of our technique by exhibiting measurement results on a multi-hop SDN network, together with client software requests and servers, with multiple users and multiple servers. Our experiments show in particular that our adaptive controller achieves power savings of the order of 15% with a very moderate (but consistent) less than 2% increase in average response time.
2. The Decision System
- For each Fog node and client, we need to estimate the response time which is the overall time (including any waiting time) it takes the server f to service i for client u.
- Similarly, for the specific path , we will need to estimate the round-trip transfer time for transferring the service request and any needed data from u to the service i at f, and for transferring the results back from f to u.
- Finally, we will also require an estimate of the network power consumption for the request of client u for service i, which includes the round-trip transfer associated with the amount of data involved in the request, and the energy consumption characteristics of the SDN switches on the path .
- Selecting the optimum path in the network to connect a user to a specific Fog server for a given service, since the choice of the path determines the choice of the Fog server that is selected.
- Moreover, the practical consequence is that this can be implemented by a SDN controller whose the normal function was to select a path in the network for a given connection.
2.1. Random Neural Network and Reinforcement Learning
The Reinforcement Learning Algorithm
- After server f is chosen to execute service i to satisfy the request of client u, the resulting total client response time, i.e., the first term in Equation , is measured from the simple difference of the time-stamp when the request is sent by the client and the time-stamp when the result is received by the client. The traffic rate on the path being used is also measured during the transfer of the request and the second term of is obtained from a table look-up of power consumption versus traffic rate. As a result, the value of the goal function is computed with two multiplications and one addition, from the measurement data regarding client response time and path consumption.
- The historical value of the “reward”, defined as the inverse of the goal, i.e., , defined as is updated:
- Subsequently, the RNN weights are updated:
- Then, to prevent the weights from constantly increasing:
- Finally, with these updated values of the weights, we compute all the using the system of Equation (3), which is a fixed point iteration of complexity .
- Then, we obtain the new value of the best choice of path for the request from client u for service i, including the path itself and the Fog server at the end of the path:
3. Linking Network Power Consumption to SDN Switch Traffic Rate
Measuring the Power Characteristic of the SDN Switches
4. The Test-Bed and the Experimental Results
4.1. Experimental Results Regarding the Response Time Only
- In the time window , each client sent one request to one service, and all services are available and are responding as fast as they can. The resulting response time for the request which is satisfied for each user is denoted , from the instant when the user makes the request to the instant when the response is received back at the use. The overall average value of the n requests that are satisfied over the duration of the experiment is denoted:This experiment was repeated five times consecutively, and the overall average response time, which is an “average of averages”, taking the average of the 5 values of the individual average values T, is shown in Figure 5, in the part of the figure which does not have a background color.
- Secondly, the same experiment was run with a “stress test”, which is an additional program that executes 50% of the services which have been chosen at random, and simultaneously increases the CPU utilization rate to , resulting in a substantial increase in the time required to process the corresponding service requests. Its effect is shown with a red background in Figure 5. Interestingly, we notice that the effect of the stress test is mostly seen at the beginning of the “red period”, due to the RL-based adaptive control which dynamically shifts the load towards those service instances which do not have an overload. However, since 50% of services are systematically affected, we do have an increase in average response time. After the stress test ends, everything goes back to the prior condition.
- Thirdly, during the time span shown with an orange background in Figure 5, the links between , experience a major increase in delay caused by a DDoS attack. As a result, the clients also experience a major increase in response time due to the additional transfer delay of requests and of the corresponding responses. Again, we observe that the worse effect is at the beginning of the “yellow period”, since the RL-based adaptive control shifts the workload to the longer two-hop paths that are not under attack. Obviously, an increase in response time still occurs because of the longer paths, but it is not as bad as that at the beginning of the attack since the RL-based control has been able to avoid the links which are under attack. After the DDoS attack ends, the average response times fall back to “normal”.
4.2. Experiments Concerning Energy Optimization
- Each client sends one request in the 10 s time window as described in the previous section.
- The actual throughput is measured at the SDN switches (NUCs) and the instantaneous power consumption is computed from Figure 2.
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
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Fröhlich, P.; Gelenbe, E.; Fiołka, J.; Chęciński, J.; Nowak, M.; Filus, Z. Smart SDN Management of Fog Services to Optimize QoS and Energy. Sensors 2021, 21, 3105. https://doi.org/10.3390/s21093105
Fröhlich P, Gelenbe E, Fiołka J, Chęciński J, Nowak M, Filus Z. Smart SDN Management of Fog Services to Optimize QoS and Energy. Sensors. 2021; 21(9):3105. https://doi.org/10.3390/s21093105Chicago/Turabian Style
Fröhlich, Piotr, Erol Gelenbe, Jerzy Fiołka, Jacek Chęciński, Mateusz Nowak, and Zdzisław Filus. 2021. "Smart SDN Management of Fog Services to Optimize QoS and Energy" Sensors 21, no. 9: 3105. https://doi.org/10.3390/s21093105