PARC: Placement Availability Resilient Controller Scheme for Software-Defined Datacenters

Abstract

Software-Defined Datacenters (SDDC) have been widely used for load-aware data management for different applications across the globe. Due to its centralized architecture, the issues of scalability along with resilience (to overcome the failure of single or multiple controllers) are still challenging because of an exponential increase in the data generated from different smart devices. Most of the solutions reported in the literature for this problem use a single controller which may not address the scalability issues. However, the issues as mentioned above of scalability and resilience in SDDC can be solved by deploying multiple distributed controllers at the control plane. However, the primary concern in a network having various controllers is the optimal Controller Placement Problem (CPP) to resolve the issues of fault-tolerance, latency among controllers, availability, and placement. Hence, to resolve the issues described above, in this paper, we propose Placement Availability Resilient Controller (PARC) scheme. The PARC scheme works in the following four phases: (i) stable network partitioning (ii) localization of controllers using the cooperative game theory (iii) computation of an optimal number of multiple controllers and (iv) computation of minimal extra backup controllers to improve the overall network cost. The numerical results of the PARC scheme are evaluated on Internet2 OS3E topology using POCO-toolset simulated in Matlab. The experimental results demonstrated that the cost of deploying the number of controllers using the PARC scheme has reduced to 12.98%, 8.16%, and 6.25% as compared to the POCO-SA, POCO-MOALO, and CNCP schemes respectively. Moreover, the PARC scheme outperforms the existing state-of-the-art schemes (POCO-SA, POCO-MOALO, and CNCP) for inter-controller as well as switch-to-controller latency.