End-user traffic policing for QoS assurance in polyservice RINA networks

Abstract

Looking at the ever-increasing amount of heterogeneous distributed applications supported on current data transport networks, it seems evident that best-effort packet delivery falls short to supply their actual needs. Multiple approaches to Quality of Service (QoS) differentiation have been proposed over the years, but their usage has always been hindered by the rigidness of the TCP/IP-based Internet model, which does not even allow for applications to express their QoS needs to the underlying network. In this context, the Recursive InterNetwork Architecture (RINA) has appeared as a clean-slate network architecture aiming to replace the current Internet based on TCP/IP. RINA provides a well-defined QoS support across layers, with standard means for layers to inform of the different QoS guarantees that they can support. Besides, applications and other processes can express their flow requirements, including different QoS-related measures, like delay and jitter, drop probability or average traffic usage. Greedy end-users, however, tend to request the highest quality for their flows, forcing providers to apply intelligent data rate limitation procedures at the edge of their networks. In this work, we propose a new rate limiting policy that, instead of enforcing limits on a per QoS class basis, imposes limits on several independent QoS dimensions. This offers a flexible traffic control to RINA network providers, while enabling end-users freely managing their leased resources. The performance of the proposed policy is assessed in an experimental RINA network test-bed and its performance compared against other policies, either RINA-specific or adopted from TCP/IP. Results show that the proposed policy achieves an effective traffic control for high QoS traffic classes, while also letting lower QoS classes to take profit of the capacity initially reserved for the former ones when available.

Fine-Grained Traffic Differentiation in Tactile Internet Scenarios Using RINA

“…The C/U matrix has an MxN matrix where M represents the delay, and N represents the loss [13]. This matrix-based approach has been studied in [14], and the authors demonstrated that incorporating ∆Qbased scheduling policy enhanced network performance with heterogeneous distributed applications.…”

ARCFIRE: Experimentation with the Recursive InterNetwork Architecture

“…The experiment established 6 IP VPNs, with 4 CE routers at each IP VPN. The core DIF uses Quantitative Transport Agreement (QTA) resource allocation policies [16] to support four classes of service (QoS cubes) with differential loss In this experiment we analyzed how the complete naming and addressing architecture embodied by RINA allows RINA networks to be renumbered live, without significantly impacting the performance perceived by existing flows or impairing the ability to crate new ones. Renumbering a network with multiple layers today is a maintenance event: it has to be carefully planned, requires humans in the loop, and it takes one or more days to complete.…”

“…The experiment established 6 IP VPNs, with 4 CE routers at each IP VPN. The core DIF uses Quantitative Transport Agreement (QTA) resource allocation policies [16] to support four classes of service (QoS cubes) with differential loss and delay: urgent, low loss and urgent, high loss and non-urgent, low loss and best effort. Figure 9 shows the Cumulative Distribution Functions (CDF) of the end-to-end delay experienced by flows between PE routers, for medium-and high-load scenarios in two configurations: one in which the DIF uses FIFO (First In First Out) schedulers, and the other QTA schedulers.…”