Adoption of Software-Defined Networks(SDN) as a new networking paradigm, promises solutions for Quality of Service(QoS) management issues raised at the service providers level due to the massive increase in connected devices (e.g., Servers, Mobile devices, M2M, Things, etc.), traffic in between, and the diverse user and service demands (e.g., delay, bandwidth, etc.). Although its advantage in network control flexibility compared to traditional networking, SDN still requires a design of robust and fast QoS guaranteeing routing algorithms due to its central control computation overhead. In this article, we present a new QoS-aware routing algorithm designed to work on top of SDN and serves delay-sensitive services that require certain delay requirements with low computation time. Such problems are usually modeled as Delay Constrained Least Cost (DCLC) problem which is a well-known NP-hard problem. We adopted the Lagrangian relaxation-based approach that incorporates the delay constraint into the objective cost function to solve the DCLC problem. Differently than existing Lagrangian relaxation-based algorithms, we designed our algorithm to reduce the solution search space by exploiting the problem lower-bound cost and delay paths. Moreover, we defined three different constraint tightness factors inferred from the problem current state to avoid extra non-useful Dijkstra calls. To improve the solution quality, we propose a novel approach that use state information obtained from previous optimization iterations to obtain a small-size network subgraph. Then it search the obtained subgraph for a better solution that the Lagrangian relaxation-based approach may miss. We performed an extensive python-based simulation to evaluate the performance of MODLARAC and compared it with two Lagrangian relaxation-based heuristic algorithms proposed in the literature to solve the DCLC problem, LARAC, and BiLAD algorithms. The obtained results showed improvement up to a 15% reduction in internal Dijkstra calls count with about a 2% increase in the total path cost compared to those obtained by the optimal solution.

采用软件定义网络 (SDN) 作为一种新的网络范式，有望解决由于连接设备（例如，服务器、移动设备、M2M、事物等）、两者之间的流量，以及不同的用户和服务需求（例如，延迟、带宽等）。尽管与传统网络相比，SDN在网络控制灵活性方面具有优势，但由于其中央控制计算开销，SDN仍需要设计稳健且快速的QoS保证路由算法。在本文中，我们提出了一种新的 QoS 感知路由算法，旨在在 SDN 之上工作，并为需要特定延迟要求且计算时间短的延迟敏感服务提供服务。此类问题通常被建模为延迟约束最小成本 (DCLC) 问题，这是一个众所周知的 NP-hard 问题。我们采用了基于拉格朗日松弛的方法，将延迟约束结合到目标成本函数中来解决 DCLC 问题。与现有的基于拉格朗日松弛的算法不同，我们设计的算法通过利用问题的下界成本和延迟路径来减少解决方案搜索空间。此外，我们定义了从问题当前状态推断出的三个不同的约束紧密度因子，以避免额外的无用 Dijkstra 调用。为了提高解决方案的质量，我们提出了一种新颖的方法，该方法使用从先前的优化迭代中获得的状态信息来获得小型网络子图。然后它搜索获得的子图以寻找基于拉格朗日松弛的方法可能会遗漏的更好的解决方案。我们进行了广泛的基于 python 的模拟来评估 MODLARAC 的性能，并将其与文献中提出的解决 DCLC 问题、LARAC 和 BiLAD 算法的两种基于拉格朗日松弛的启发式算法进行比较。获得的结果表明，与通过最佳解决方案获得的相比，内部 Dijkstra 调用计数减少了 15%，总路径成本增加了约 2%。