Delay-constrained Least-cost Problem Research Articles

Bootstrapped LARAC algorithm for fast delay‐sensitive QoS provisioning in SDN networks

SummaryIn today's networks, QoS provisioning becomes a major concern for service providers due to the massive increase in connected devices (e.g., mobile devices, servers, and Things), traffic in between, and the diverse service requirements (e.g., delay and bandwidth). Software‐Defined Networking (SDN) promises solution for such issues with its central network control flexibility compared to traditional networking; nonetheless, a design of a fast and QoS guaranteeing routing algorithm is still needed. Delay Constrained Least Cost (DCLC) is a well‐known NP‐hard problem. LAgrange Relaxation‐based Aggregated Cost (LARAC) algorithm is a Dijkstra‐based QoS‐aware algorithm and one of many heuristic algorithms proposed in the literature to solve the DCLC problem. Despite its outstanding performance over other algorithms in SDN, it still needs improvement. In this article, we present a modified version of LARAC, called MODLARAC, to improve the solution feasibility search state by exploiting LARAC's lower‐bound paths before the approximation process start. Also, we modify its stop condition to avoid extra non‐useful Dijkstra calls. We implement and evaluate MODLARAC using a realistic ISP network topology using Mininet and controlled by Floodlight SDN controller. We then compare MODLARAC against the original LARAC and BiLAD algorithms. The obtained results showed improvement up to 20% reduction in Dijkstra calls count compared to original LARAC and BiLAD algorithms without a significant increase in path's cost or delay metrics. It reached only 3% increase in path cost and 7% in path delay in its worst case with a safe distance of 11% lower than the delay demand.

A scalable method for DCLC problem using hierarchical MDP model

It is well known that the delay-constrained least-cost (DCLC) routing problem is NP-complete, hence various heuristic methods have been proposed for this problem. However, these heuristic methods have poor scalability as the network scale increases. In this paper we propose a new method based on the Markov Decision Process (MDP) theory and the hierarchical routing scheme to address the scalability issue of the DCLC routing problem. We construct a new two-level hierarchy MDP model and apply an infinite-horizon discounted cost model to the upper level for the end-to-end inter-domain link selection. Since the infinite-horizon discounted cost model is independent of network scale, the scalability problem is resolved. With the proposed model, we further give the algorithm of solving the optimal policy to obtain the DCLC routing. Simulation results show that the proposed method improves the scalability significantly.

An efficient quality of service routing algorithm for delay-sensitive applications

It is well known that the Delay-Constrained Least-Cost (DCLC) unicast routing problem is NP-complete, hence various heuristic algorithms have been developed for this problem. In this paper, we propose a more efficient distributed algorithm, namely, Selection-Function-based DCLC (SF-DCLC), based on a novel selection function for the DCLC problem. The proposed SF-DCLC algorithm requires limited network state information at each node and is always able to find a loop-free path satisfying the delay bound if such paths exist. Simulation study shows that SF-DCLC is not as sensitive to the delay bound and the size of networks as some other DCLC routing algorithms, and has very low cost-inefficiency compared to the optimal one in various network scenarios we have studied. A noteworthy feature of SF-DCLC is that SF-DCLC has very high probability of finding the optimal solution in polynomial time with low computational complexity and message complexity.