NexGen D-TCP: Next generation dynamic TCP congestion control algorithm

With the advancement of wireless access networks and mmWave New Radio (NR), new applications emerged, which requires a high data rate. The random packet loss due to mobility and channel conditions in a wireless network is not negligible, which degrades the significant performance of the Transmission Control Protocol (TCP). The TCP has been extensively deployed for congestion control in the communication network during the last two decades. Different variants are proposed to improve the performance of TCP in various scenarios, specifically in lossy and high bandwidth-delay product (high- BDP) networks. Implementing a new TCP congestion control algorithm whose performance is applicable over a broad range of network conditions is still a challenge. In this article, we introduce and analyze a Dynamic TCP (D-TCP) congestion control algorithm overmmWave NR and LTE-A networks. The proposed D-TCP algorithm copes up with the mmWave channel fluctuations by estimating the available channel bandwidth. The estimated bandwidth is used to derive the congestion control factor N. The congestion window is increased/decreased adaptively based on the calculated congestion control factor. We evaluated the performance of D-TCP in terms of congestion window growth, goodput, fairness and compared it with legacy and existing TCP algorithms. We performed simulations of mmWave NR during LOS \u3c-\u3e NLOS transitions and showed that D-TCP curtails the impact of under-utilization during mobility. The simulation results and live air experiment points out that D-TCP achieves 32:9% gain in goodput as compared to TCPReno and attains 118:9% gain in throughput as compared to TCP-Cubic

Stream Control Transmission Protocol (SCTP): Robust and Efficient for Data Centre Applications

Due to rapid advancement in modern technology, as one of the major concerns is the stability of business. The organizations depend on their systems to provide robust and faster processing of information for their operations. Efficient data centers are key sources to handle these operations. If the organizational system is not fully functional, the performance of organization may be impaired or clogged completely. With the developments of real-time applications into data centers for data communications, there is a need to use an alternative of the standard TCP protocol to provide reliable data transfer. Stream Control Transmission Protocol (SCTP) consists of several well built-in characteristics that make it capable to work efficiently with real-time applications. In this paper, we evaluate an optimized version of STCP. The optimized version of SCTP is tested against a non optimized version of STCP and TCP in a data center environment. Simulations of the protocols are carried using NS2 simulator.http://arxiv.org/abs/1312.062

Reducing Transport Latency for Short Flows with Multipath TCP

Multipath TCP (MPTCP) has been an emerging transport protocol that provides network resilience to failures and improves throughput by splitting a data stream into multiple subflows across all the available multiple paths. While MPTCP is generally beneficial for throughput-sensitive large flows with large number of subflows, it may be harmful for latency-sensitive small flows. MPTCP assigns each subflow a congestion window, making short flows susceptible to timeout when a flow only contains a few packets. This condition becomes even worse when the paths have heterogeneous characteristics as packet reordering occurs and the slow paths can be used with MPTCP, causing the increased end-to-end delay and the lower application Goodput. Thus, it is important to choose the appropriate subflows for each MPTCP connection to achieve the good performance. However, the subflows in MPTCP are determined before a connection is established, and they usually remain unchanged during the lifetime of that connection. To address this issue, we propose DMPTCP, which dynamically adjusts the subflows according to application workloads. Specifically, DMPTCP first utilizes the idea of TCP modeling to estimate the latency on the path under scheduling and the data amount sent on the other paths simultaneously, and then decides the set of subflows to be used for certain application periodically with the goal of reducing completion time for short flows and achieving a higher throughput for long flows. We implement DMPTCP in a Linux server and conduct extensive experiments both in NS3 and in Linux testbed to validate its effectiveness. Our evaluation shows that DMPTCP decreases the completion time by over 46.55% compared to conventional MPTCP for short flows while increases the Goodput up to 21.3% for long-lived flows

Reducing Transport Latency for Short Flows with Multipath TCP

Multipath TCP (MPTCP) has been an emerging transport protocol that provides network resilience to failures and improves throughput by splitting a data stream into multiple subflows across all the available multiple paths. While MPTCP is generally beneficial for throughput-sensitive large flows with large number of subflows, it may be harmful for latency-sensitive small flows. MPTCP assigns each subflow a congestion window, making short flows susceptible to timeout when a flow only contains a few packets. This condition becomes even worse when the paths have heterogeneous characteristics as packet reordering occurs and the slow paths can be used with MPTCP, causing the increased end-to-end delay and the lower application Goodput. Thus, it is important to choose the appropriate subflows for each MPTCP connection to achieve the good performance. However, the subflows in MPTCP are determined before a connection is established, and they usually remain unchanged during the lifetime of that connection. To address this issue, we propose DMPTCP, which dynamically adjusts the subflows according to application workloads. Specifically, DMPTCP first utilizes the idea of TCP modeling to estimate the latency on the path under scheduling and the data amount sent on the other paths simultaneously, and then decides the set of subflows to be used for certain application periodically with the goal of reducing completion time for short flows and achieving a higher throughput for long flows. We implement DMPTCP in a Linux server and conduct extensive experiments both in NS3 and in Linux testbed to validate its effectiveness. Our evaluation shows that DMPTCP decreases the completion time by over 46.55% compared to conventional MPTCP for short flows while increases the Goodput up to 21.3% for long-lived flows

Experience-driven Control For Networking And Computing

Modern networking and computing systems have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this thesis, we aim to study system control problems from a whole new perspective by leveraging emerging Deep Reinforcement Learning (DRL), to develop experience-driven model-free approaches, which enable a network or a device to learn the best way to control itself from its own experience (e.g., runtime statistics data) rather than from accurate mathematical models, just as a human learns a new skill (e.g., driving, swimming, etc). To demonstrate the feasibility and superiority of this experience-driven control design philosophy, we present the design, implementation, and evaluation of multiple DRL-based control frameworks on two fundamental networking problems, Traffic Engineering (TE) and Multi-Path TCP (MPTCP) congestion control, as well as one cutting-edge application, resource co-scheduling for Deep Neural Network (DNN) models on mobile and edge devices with heterogeneous hardware. We first propose DRL-TE, a DRL-based framework that enables experience-driven networking for TE. DRL-TE maximizes a widely-used utility function by jointly learning network environment and its dynamics, and making decisions under the guidance of powerful DNNs. We propose two new techniques, TE-aware exploration and actor-critic-based prioritized experience replay, to optimize the general DRL framework particularly for TE. Furthermore, we propose an Actor-Critic-based Transfer learning framework for TE, ACT-TE, which solves a practical problem in experience-driven networking: when network configurations are changed, how to train a new DRL agent to effectively and quickly adapt to the new environment. In the new network environment, ACT-TE leverages policy distillation to rapidly learn a new control policy from both old knowledge (i.e., distilled from the existing agent) and new experience (i.e., newly collected samples). In addition, we propose DRL-CC to enable experience-driven congestion control for MPTCP. DRL-CC utilizes a single (instead of multiple independent) DRL agent to dynamically and jointly perform congestion control for all active MPTCP flows on an end host with the objective of maximizing the overall utility. The novelty of our design is to utilize a flexible recurrent neural network, LSTM, under a DRL framework for learning a representation for all active flows and dealing with their dynamics. Moreover, we integrate the above LSTM-based representation network into an actor-critic framework for continuous congestion control, which applies the deterministic policy gradient method to train actor, critic, and LSTM networks in an end-to-end manner. With the emergence of more and more powerful chipsets and hardware and the rise of Artificial Intelligence of Things (AIoT), there is a growing trend for bringing DNN models to empower mobile and edge devices with intelligence such that they can support attractive AI applications on the edge in a real-time or near real-time manner. To leverage heterogeneous computational resources (such as CPU, GPU, DSP, etc) to effectively and efficiently support concurrent inference of multiple DNN models on a mobile or edge device, in the last part of this thesis, we propose a novel experience-driven control framework for resource co-scheduling, which we call COSREL. COSREL has the following desirable features: 1) it achieves significant speedup over commonly-used methods by efficiently utilizing all the computational resources on heterogeneous hardware; 2) it leverages DRL to make dynamic and wise online scheduling decisions based on system runtime state; 3) it is capable of making a good tradeoff among inference latency, throughput and energy efficiency; and 4) it makes no changes to given DNN models, thus preserves their accuracies. To validate and evaluate the proposed frameworks, we conduct extensive experiments on packet-level simulation (for TE), testbed with modified Linux kernel (for MPTCP), and off-the-shelf Android devices (for resource co-scheduling). The results well justify the effectiveness of these frameworks, as well as their superiority over several baseline methods

Contributions to the routing of traffic flows in multi-hop IEEE 802.11 wireless networks

The IEEE 802.11 standard was not initially designed to provide multi-hop capabilities. Therefore, providing a proper traffic performance in Multi-Hop IEEE 802.11 Wireless Networks (MIWNs) becomes a significant challenge. The approach followed in this thesis has been focused on the routing layer in order to obtain applicable solutions not dependent on a specific hardware or driver. Nevertheless, as is the case of most of the research on this field, a cross-layer design has been adopted. Therefore, one of the first tasks of this work was devoted to the study of the phenomena which affect the performance of the flows in MIWNs. Different estimation methodologies and models are presented and analyzed. The first main contribution of this thesis is related to route creation procedures. First, FB-AODV is introduced, which creates routes and forwards packets according to the flows on the contrary to basic AODV which is destination-based. This enhancement permits to balance the load through the network and gives a finer granularity in the control and monitoring of the flows. Results showed that it clearly benefits the performance of the flows. Secondly, a novel routing metric called Weighted Contention and Interference routing Metric (WCIM) is presented. In all analyzed scenarios, WCIM outperformed the other analyzed state-of-the-art routing metrics due to a proper leveraging of the number of hops, the link quality and the suffered contention and interference. The second main contribution of this thesis is focused on route maintenance. Generally, route recovery procedures are devoted to the detection of link breaks due to mobility or fading. However, other phenomena like the arrival of new flows can degrade the performance of active flows. DEMON, which is designed as an enhancement of FB-AODV, allows the preemptive recovery of degraded routes by passively monitoring the performance of active flows. Results showed that DEMON obtains similar or better results than other published solutions in mobile scenarios, while it clearly outperforms the performance of default AODV under congestion Finally, the last chapter of this thesis deals with channel assignment in multi-radio solutions. The main challenge of this research area relies on the circular relationship between channel assignment and routing; channel assignment determines the routes that can be created, while the created routes decide the real channel diversity of the network and the level of interference between the links. Therefore, proposals which join routing and channel assignment are generally complex, centralized and based on traffic patterns, limiting their practical implementation. On the contrary, the mechanisms presented in this thesis are distributed and readily applicable. First, the Interference-based Dynamic Channel Assignment (IDCA) algorithm is introduced. IDCA is a distributed and dynamic channel assignment based on the interference caused by active flows which uses a common channel in order to assure connectivity. In general, IDCA leads to an interesting trade-off between connectivity preservation and channel diversity. Secondly, MR-DEMON is introduced as way of joining channel assignment and route maintenance. As DEMON, MR-DEMON monitors the performance of the active flows traversing the links, but, instead of alerting the source when noticing degradation, it permits reallocating the flows to less interfered channels. Joining route recovery instead of route creation simplifies its application, since traffic patterns are not needed and channel reassignments can be locally decided. The evaluation of MR-DEMON proved that it clearly benefits the performance of IDCA. Also, it improves DEMON functionality by decreasing the number of route recoveries from the source, leading to a lower overhead.El estándar IEEE 802.11 no fue diseñado inicialmente para soportar capacidades multi-salto. Debido a ello, proveer unas prestaciones adecuadas a los flujos de tráfico que atraviesan redes inalámbricas multi-salto IEEE 802.11 supone un reto significativo. La investigación desarrollada en esta tesis se ha centrado en la capa de encaminamiento con el objetivo de obtener soluciones aplicables y no dependientes de un hardware específico. Sin embargo, debido al gran impacto de fenómenos y parámetros relacionados con las capas físicas y de acceso al medio sobre las prestaciones de los tráficos de datos, se han adoptado soluciones de tipo cross-layer. Es por ello que las primeras tareas de la investigación, presentadas en los capítulos iniciales, se dedicaron al estudio y caracterización de estos fenómenos. La primera contribución principal de esta tesis se centra en mecanismos relacionados con la creación de las rutas. Primero, se introduce una mejora del protocolo AODV, que permite crear rutas y encaminar paquetes en base a los flujos de datos, en lugar de en base a los destinos como se da en el caso básico. Esto permite balacear la carga de la red y otorga un mayor control sobre los flujos activos y sus prestaciones, mejorando el rendimiento general de la red. Seguidamente, se presenta una métrica de encaminamiento sensible a la interferencia de la red y la calidad de los enlaces. Los resultados analizados, basados en la simulación de diferentes escenarios, demuestran que mejora significativamente las prestaciones de otras métricas del estado del arte. La segunda contribución está relacionada con el mantenimiento de las rutas activas. Generalmente, los mecanismos de mantenimiento se centran principalmente en la detección de enlaces rotos debido a la movilidad de los nodos o a la propagación inalámbrica. Sin embargo, otros fenómenos como la interferencia y congestión provocada por la llegada de nuevos flujos pueden degradar de forma significativa las prestaciones de los tráficos activos. En base a ello, se diseña un mecanismo de mantenimiento preventivo de rutas, que monitoriza las prestaciones de los flujos activos y permite su reencaminamiento en caso de detectar rutas degradadas. La evaluación de esta solución muestra una mejora significativa sobre el mantenimiento de rutas básico en escenarios congestionados, mientras que en escenarios con nodos móviles obtiene resultados similares o puntualmente mejores que otros mecanismos preventivos diseñados específicamente para casos con movilidad. Finalmente, el último capítulo de la tesis se centra en la asignación de canales en entornos multi-canal y multi-radio con el objetivo de minimizar la interferencia entre flujos activos. El reto principal en este campo es la dependencia circular que se da entre la asignación de canales y la creación de rutas: la asignación de canales determina los enlaces existentes la red y por ello las rutas que se podrán crear, pero son finalmente las rutas y los tráficos activos quienes determinan el nivel real de interferencia que se dará en la red. Es por ello que las soluciones que proponen unificar la asignación de canales y el encaminamiento de tráficos son generalmente complejas, centralizadas y basadas en patrones de tráfico, lo que limita su implementación en entornos reales. En cambio, en nuestro caso adoptamos una solución distribuida y con mayor aplicabilidad. Primero, se define un algoritmo de selección de canales dinámico basado en la interferencia de los flujos activos, que utiliza un canal común en todos los nodos para asegurar la conectividad de la red. A continuación, se introduce un mecanismo que unifica la asignación de canales con el mantenimiento preventivo de las rutas, permitiendo reasignar flujos degradados a otros canales disponibles en lugar de reencaminarlos completamente. Ambas soluciones demuestran ser beneficiosas en este tipo de entornos.Postprint (published version