Congestion control for M2M traffic with heterogeneous throughput demands

In order to guarantee traffic flow medium in network, congestion control strategy is adopted in traffic flow medium. According to the delay sensitivity of flow medium data, this paper proposes a novel congestion control for traffic flow medium in network known as Adaptive Delay Congestion Control (ADCC). ADCC improves the QoS of multimedia stream in Network by detecting and discarding these packets that their accumulated delay exceeds multimedia stream's tolerance delay threshold, and maintain high bandwidth utilization. Another notable feature of the scheme is that ADCC implement congestion control by adaptive adjusting the switch device's queue length threshold according to propagation delay. Simulation results shows that the proposed scheme can effectively reduce the average delay than that of using traditional congestion control algorithm. Moreover, it is suitable to be used in the ever-changing network environment and can improve utilization of the network resources.

Multipath Transmission Control Protocol (MPTCP) enables multi-homed devices to establish multiple simultaneous routes for data transmission. Congestion Control (CC) is a fundamental mechanism for implementing and designing MPTCP. The Internet of Things (IoT) networks generate a massive volume of heterogeneous traffic with high dimensional states and diverse QoS characteristics. The existing MPTCP CC algorithms are unable to perform efficiently under highly mobile and dynamic IoT environments. We propose a novel model-free SDN-based adaptive actor-critic deep reinforcement learning framework based on a fuzzy normalized neural network to address the issue of CC for MPTCP in the IoT networks. In the proposed method, an agent can learn efficiently and better approximate the state-action value function of the actor and the action function of the critic to adjust the sub-flows congestion windows size adaptively according to the dynamic condition of a network. Simulation results show that the proposed scheme outperforms the state of the art schemes in terms of the goodput under highly-dynamic IoT environments.

Traditional congestion control algorithms rely on various model-based methods to improve the end-to-end (E2E) performance of packet transmission. The resulting decisions quickly become less effective amid the dynamics of network conditions. In order to perform congestion control adaptively, reinforcement learning (RL) can be adopted to continuously learn the optimal strategy from the network environment. Oftentimes, the reward of such a learning problem is a weighted sum of multiple E2E performance metrics, such as throughput, delay, and fairness. Unfortunately, those weights can be only manually tuned based on extensive experiments. To address this issue, in this paper, we design a constrained RL algorithm for congestion control named CRL-CC to adaptively tune those weights, with the objective of effectively improving the overall E2E packet transmission performance. In particular, the multi-objective optimization problem is firstly formulated as a constrained optimization problem. Then, the Lagrangian relaxation method is leveraged to transform the constrained optimization problem into a single-objective optimization problem, which is solved by designing a multi-objective reward function with Lagrangian multipliers. Extensive experiments based on OpenAI-Gym show that the proposed CRL-CC algorithm can achieve higher overall performance in various network conditions. In particular, the CRL-CC algorithm outperforms the benchmark algorithm on Pantheon by 21.7%, 27.4%, and 5.3% in throughput, delay, and fairness, respectively.

A network rate management protocol with TCP congestion control and fairness for all

Congestion control in wireless links based on selective delivery of erroneous packets

Most of the existing congestion control works are suitable for the best effort flows, but not for the flows with quality of service (QoS) requirements. In this paper, by adopting equation-based congestion control (EBRC) for differentiated services instead of traditional TCP congestion control for best-effort service, a new congestion control mechanism supporting QoS requirements can be achieved. Three parts which the mechanism consists of are sender functionality, packet loss control mechanism in routers and receiver functionality. The details of design and implementation within this mechanism are investigated.

In 2016, Google proposed the bottleneck bandwidth round-trip propagation time (BBR) as a new congestion control algorithm for improving network speed. As BBR has different behavioral characteristics from loss-based congestion control, it must ensure the coexistence between congestion control algorithms. However, throughput imbalance has been reported in a competition between BBR and loss-based congestion control. In this paper, we propose an opponent congestion control identification model of BBR (OI-BBR) with machine learning to improving inter-protocol fairness. In the emulation experiments, we confirmed that OI-BBR based on the proposed model improved the inter-protocol fairness by operating differently according to the opponent congestion control algorithms.

The explosion of Internet of things (IoT) devices has led to pretty much saturated network infrastructures, thus congestion often becomes severe, especially in applications where users require low latency, high throughput and real-time responses. Hence, traditional congestion control mechanisms like static routing protocols, Active Queue Management (AQM) and TCP variants are not suitable for this dynamic and heterogeneous IoT environment as they are reactive, rigid and cannot adapt to dynamic changes. The contribution is to investigate the potential of AI paradigms—such as ML, DL, RL and hybrid models- to provide Intelligent and proactive congestion Control in IoT systems. The paper compares the strengths and weaknesses of each method, such as RL’s generalizability and DL’s ability to capture patterns, as well as limitations in terms of scalability, interpretability, and computational resource requirements. Moreover, it emphasizes important research gaps in model generalization process, evaluation criteria and cross-platform fusion. Next steps in research discussions on the future of research take into account lightweight AI architectures, Explainable AI (XAI) frameworks, scalability of FL, and standard benchmarking data sets. The hybrid-AI congestion prediction model built in this work is validated across simulation tools and real-data sets and is observed to result in a significant decrease in latency, packet loss and energy consumption. This paper paves the way for scalable, intelligent and secure AI-based congestion management solutions for various IoT networks.

Chapter 5 - Congestion Control in High-Speed Networks

In recent years, hybrid congestion control (CC) algorithms that combine rule-based CC and learning-based CC have gained significant attention. They incorporate the fast adaption ability of learning-based CC and the stability of rule-based CC, tending to select the better-performing rate based on the network feedback. However, the practical implementation of such algorithms has revealed primary issues. Specifically, they require both CCs to run alternately, which results in a poorly performing CC continuing to run in the network. Moreover, hybrid CCs cannot converge to the optimal rate when both CCs perform poorly. This paper proposes Anole to address these issues. Anole has three main algorithmic contributions: 1) Anole always selects the better-performing CC, 2) Anole temporarily deprecates the consistently underperforming CC, 3) when both CCs pervform poorly, Anole infers the optimal sending rate based on the network feedback. We carry out comprehensive experiments in both emulated and real-world wired networks, as well as in real-world WiFi networks, to assess the performance of Anole. The experiment results demonstrate that Anole achieves approximately 6% higher throughput in real-world links and 34% lower delay in the 48Mbps link compared to the state-of-the-art CC. Anole also exhibits superior performance in adaptability and fair convergence.

In this paper, we study the performance of utility maximization congestion control over multihop CSMA-based networks. We consider decoupled vs. joint design of congestion control and medium access and consider unmodified MAC protocols such as IEEE 802.11. Networks employing such MAC protocols incur flow starvation both without congestion control and with existing TCP-based congestion control. We develop a framework to study key issues in such networks that are not incorporated by prior models, yet are critical to the performance of congestion control algorithms. We study the role of data transmission capacity that is location dependent and, even more, unknown. We show that for the case of consistent channel state, a single globally optimal data transmission capacity does not exist. Moreover, for the case of inconsistent channel state that arises due to the carrier sense mechanism itself, a data transmission capacity that provides convergence to perfectly fair rates does not exist, i.e., the congestion control algorithm converges to incorrect rates. We study the impact of inter-node collaboration within a contention region, and show that collaboration can alleviate these problems and ensure convergence to fair rates. Finally, we compare the performance of congestion control in a collaborative network with the performance of TCP, and show that TCP starves some flows, whereas congestion control with collaboration removes starvation, provides significantly better fairness, and achieves 17% higher aggregate throughput.

Channel congestion is one of the most critical issues in IEEE 802.11p-based vehicular communications as it leads to the unreliability of safety applications. As a countermeasure, many Decentralized Congestion Control (DCC) algorithms have been proposed. One of the most prominent DCC algorithms is the message-rate based LIMERIC. Recently, algorithms have also been proposed to support higher vehicle density (better scalability). One of such algorithms is the combined message-rate and data-rate based congestion control algorithm (MD-DCC). MD-DCC can support around 2.7 times higher vehicular density than LIMERIC. However, if LIMERIC has been deployed, can MD-DCC be introduced and coexist well with LIMERIC. The objective of this paper is to investigate how MD-DCC coexists with LIMERIC. Given a scenario where vehicles may use either LIMERIC or MD-DCC, we study the impact of coexistence on channel load, fairness and reliability of vehicles at different densities. Simulation studies show that there is no significant degradation of reliability both for LIMERIC and MD-DCC at different densities. On the contrary, coexistence can improve the reliability of LIMERIC vehicles. Furthermore, MD-DCC can support vehicles at large densities even when it coexists with LIMERIC retaining its scalability. However, fair allocation of resources is not guaranteed when LIMERIC and MD-DCC coexist.

Researches have shown that self similar traffic has essential effects on the network parameters such as loss rate, length of the queue in the routers and throughput. Since classical congestion control algorithms work in short time scales and do not consider self similarity and long time dependency phenomenon of data, development of congestion control algorithms which be able to distinguish and manage of such traffic have been very important to researchers. Multiple time-scale congestion control is a method which has high performance reliance co-efficiency. In this research congestion control in large time scales has been implemented in fuzzy form. Simulation results show that fuzzy congestion controller will have better performance in comparison with a non-fuzzy two time-scale controller, if fuzzy rules are selected correctly.

Congestion control (CC) is an indispensable component of transport protocols to prevent congestion collapse as it distributes the available bandwidth among all competing flows, ideally in a fair manner. It thus has a large impact on performance and there exists a constantly evolving set of CC algorithms, each addressing different performance needs. While the algorithms are commonly tested regarding the problems underlying their implementation, the interaction with existing algorithms is often not considered. Additionally considering the fact that content providers (CPs) such as content distribution networks (CDNs) are known to tune TCP stacks for performance gains, the large assortment of algorithms opens the door for custom parametrization and potentially unfair bandwidth sharing. In this paper, we thus empirically investigate if current Internet traffic generated by CPs still adheres to the conventional understanding of fairness. For this, we compare fairness properties of testbed hosts to actual traffic of six major CPs subject to different queue sizes and queueing disciplines in a home-user setting. Additionally, we investigate how mice and elephant flows from the different CPs interact. We find that some employed CC algorithms lead to significantly asymmetric bandwidth shares and very poor flow completion times for mice flows. Fortunately, AQMs such as FQ_CoDel are able to alleviate such unfairness.

In this chapter, layer-2 hop-by-hop congestion control mechanisms for wireless mesh networks are studied. First, background on development of congestion control as a research area is presented. Then, congestion is defined and the causes of congestion in a mesh network are identified. The steps and challenges for development of a congestion control protocol are explored by dividing congestion control mechanism into three main functions: congestion detection, signaling, and resolution. Stability and fairness in the context of rate control are studied, and global stability in the presence of delay is identified as the main challenge faced by hop-by-bop congestion control algorithms in multihop networks. The congestion control framework in the IEEE 802.11 draft standard on mesh networking is then presented. The chapter concludes with thoughts for practitioners and areas for future research in this field.