Analytical Model of the Buffer Memory of an OpenFlow Switch in a Software-Defined Network (SDN)

Abstract

Introduction. Reliable identification of the probabilistic and temporal features of switching nodes is required for assessing the quantitative characteristics of software-defined networks. Widely used queuing theory (QT) methods only approximately specify and model the processes in an OpenFlow switch and its buffer memory. This results in understated and unrealistic performance estimates for the designed network equipment, causing switch buffer overloading and packet loss. A different modeling approach can solve this problem. The objective of this paper is to develop and study an analytical model for the buffer memory of an OpenFlow switch in an SDN using advanced techniques. Materials and Methods. The discrete Laplace-Stieltjes transform was used. Statistical characteristics of packet flows and the throughput of communication channels for a given packet loss probability were taken into account. The OpenFlow switch buffer memory model was based on the mathematical apparatus of the QT. It was constructed under the assumption of recurrence of input data flows with batch arrivals. The model was based on schematic representations of the switch structure, its record set, and a graph description of the transmission of network packets leaving the switch. We started with schematic representations of the switch structure, its record set, and a graph description of the transmission of network packets exiting the switch. Two model assumptions were taken as acceptable: − arbitrary distribution of the relationship between the volume of data flows and their service time; − discreteness of the distribution of the information flow structure. Results. The developed model integrated the probability of packet flow loss, their statistical characteristics, the throughput of computing devices, and the multiphase service procedure. When testing the model performance, we assumed that the switch load increased from 0.1 to 0.9, and the loss probability — from 10–3 to 10–6. For these metrics, we determined how the switch load affected the buffer memory size and latency. In the first case, the minimum value (memory capacity) was 0.201, the maximum — 10564. In the second, they were 0 and 470 ms, respectively. For simulation modeling, the minimum time was 0 ms, the maximum — 2300 ms. The simulation and analytical modeling indicators were close at loads below 50% and increased several times at loads above 50%. The indicators increased sharply with loads up to 70%, and then increased exponentially. Discussion. At low network loads, queues did not overflow, packets were not lost, and linear dependences were maintained. At medium and high loads, packet flow processing was described by nonlinear dependences. The results of analytical and simulation modeling diverged due to the explosive nature of self-similar network traffic and its approximate description by the Pareto distribution. Switch load determined the feasibility of the proposed approach. The model is suitable for designing elements of software-defined networks to analyze their resilience under various information impacts. Conclusion. The proposed SDN analytical model determined the values and variances of the switch buffer memory size, as well as the memory capacity for constructing address flow tables. The solution performance was tested with switch loads ranging from 0.1 to 0.9. It is planned to create a model that takes into account request flows from both the external network and the server.