Robust Congestion Control Research Articles

ACC-RL: Adaptive Congestion Control Based on Reinforcement Learning in Power Distribution Networks with Data Centers

Modern data center power distribution networks place greater demands on the stability and reliability of power supply. Growing network computing demands and complex network environments can cause network congestion, which in turn leads to network traffic overload and power supply equipment overload. Therefore, network congestion is one of the most important problems faced by data center power distribution networks. In this paper, we propose an approach called ACC-RL based on reinforcement learning (RL), which can effectively avoid network congestion and improve energy performance. ACC-RL models the congestion control task as a Partially Observable Markov Decision Process (POMDP). It is independent of the estimated value function and supports deterministic policies. It also sets the reward value function using real-time network information such as the transmission rate, RTT, and switch queue length, with the target transmission rate as the target equilibrium point. ACC-RL is highly general, can be trained on datasets running in different network environments, and generates a robust congestion control policy. The experimental results show that ACC-RL can solve the congestion problem without any predefined scenarios in different network environments. It can control the network traffic well, thus ensuring the stability and reliability of the power supply in the distribution network. We conduct network simulation experiments through NS-3. We set up different scenarios for experiments and data analysis in many-to-one, all-to-all, and long–short network environments. Compared with the popular rule-based congestion control algorithms such as TIMELY, DCQCN, and HPCC, ACC-RL shows different degrees of energy performance advantages in network metrics such as fairness, link utilization, and throughput.

WOA-Based Robust Congestion Control Scheme with Two Kinds of Propagation Latencies and External Disturbance in Software-Defined Wireless Networks

This paper proposes a novel WOA-based robust control scheme with two kinds of propagation latencies and external disturbance implemented in Software-Defined Wireless Networks (SDWNs) to maximize overall throughput and enhance the stability of the global network. Firstly, an adjustment model developed using the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme with propagation latency in device-to-device paths and a closed-loop congestion control model with propagation latency in device–controller pairs are proposed, and the effect of channel competition from neighboring forwarding devices is analyzed. Subsequently, a robust congestion control model with two kinds of propagation latencies and external disturbance is established. Then, a new WOA-based scheduling strategy that considers each individual whale as a specific scheduling plan to allocate appropriate sending rates at the source side is presented to maximize the global network throughput. Afterward, the sufficient conditions are derived using Lyapunov–Krasovskii functionals and formulated using Linear Matrix Inequalities (LMIs). Finally, a numerical simulation is conducted to verify the effectiveness of this proposed scheme.

Robust Congestion Control for Demand-Based Optimization in Precoded Multi-Beam High Throughput Satellite Communications

High-throughput satellite communication systems are growing in strategic importance thanks to their role in delivering broadband services to mobile platforms and residences and/or businesses in rural and remote regions globally. Although precoding has emerged as a prominent technique to meet ever-increasing user demands, there is a lack of studies dealing with congestion control. This paper enhances the performance of multi-beam high throughput geostationary satellite systems under congestion, where the users’ quality of service (QoS) demands cannot be fully satisfied with limited resources. In particular, we propose congestion control strategies, relying on simple power control schemes. We formulate a multi-objective optimization framework balancing the system sum-rate and the number of users satisfying their QoS requirements. Next, we propose two novel approaches that effectively handle the proposed multi-objective optimization problem. The former is a model-based approach that relies on the weighted sum method to enrich the number of satisfied users by solving a series of the sum-rate optimization problems in an iterative manner. The latter is a data-driven approach that offers a low-cost solution by utilizing supervised learning and exploiting the optimization structures as continuous mappings. The proposed general framework is evaluated for different linear precoding techniques, for which the low computational complexity algorithms are designed. Numerical results manifest that our proposed framework effectively handles the congestion issue and brings superior improvements of rate satisfaction to many users than previous works. Furthermore, the proposed algorithms show low run-time and make them realistic for practical systems.

Robust finite-time $$H_{\infty }$$ congestion control for a class of AQM network systems

This work investigates a finite-time $$H_{\infty }$$ robust congestion control issue for transmission control protocol network systems. By means of the backstepping technique, finite-time control method and $$H_{\infty }$$ control theory, a novel robust $$H_{\infty }$$ finite-time controller design approach is presented to guarantee the finite-time convergence of the queue tracking error. Furthermore, the closed-loop network system has an $$L_{2}$$ gain less than or equal to a given positive real number. Comparative simulation results show that the proposed control method performs better than the existing control approach.

Robust congestion control in cognitive radio network using event-triggered sliding mode based on reaching laws

In a band-limited spectrum, an increase in requests for various services of wireless applications is a matter of apprehension. To fulfill the expectations of immense wireless users, effective management and efficient use of the band-limited spectrum is highly desired. Cognitive Radio emerges as a solution to the problem of spectrum scarcity by opportunistically sharing the spectrum among licensed and unlicensed users. A careful balance of spectrum usage among different priority class users must be ensured to avoid network congestion. A robust congestion controller with three different reaching laws has been studied in this work for optimal spectrum utilization in Cognitive Radio Network. The sliding mode controller, which is eminent for its robustness and disturbance rejection capabilities has been combined with a novel dynamic event-triggering scheme to reduce energy expenses and computational complexities. The controller adequacy has been supported by simulations.

Robust congestion control in cognitive radio network using dynamic event‐triggered sliding mode

SummaryThe ever‐increasing service demand from wireless users under restricted bandwidth necessitates the development of suitable congestion control scheme for throughput maximization. A robust congestion controller design has been addressed in this work to sustain necessary Quality of Service in Cognitive Radio Network. The goal is to reduce packet loss while ensuring optimum utilization of available resources. Sliding mode controller proclaimed for its disturbance rejection capabilities and robustness has been used to design the proposed controller. Using the benefits of sliding mode control, dynamic event‐triggering strategies have been combined for better utilization in resource‐constrained environments. Control execution requires minimal updates while guaranteeing the desired closed‐loop system behavior. The controller efficacy has been validated in simulations.

Congestion control in cognitive radio networks with event-triggered sliding mode

The increasing demand for assorted services from extensive wireline and wireless users place a significant burden on the band-limited radio spectrum. To settle the demand, smart reuse and management of the spectrum are necessary. In this contribution, Cognitive Radio being an emerging technology provides a platform to share the same spectrum between Primary Users (licensed) and Secondary Users (unlicensed) for significant improvement in the spectrum efficiency. The coexistence of users for data communications in a band-limited channel calls for a robust congestion controller to maximize throughput. This work presents the design of a robust nonlinear congestion controller based on event-triggered sliding mode for Cognitive Radio Networks. The goal is to maintain desired Quality of Service of the network with optimum bandwidth and resource utilization. The controller has been designed on the notions of sliding mode, better known for its inherent robustness and disturbance rejection capabilities. An event-triggering scheme has been incorporated with the sliding mode for optimum utilization of the available resources. The signal is sampled and control is updated only when a predefined condition gets violated while ensuring acceptable closed-loop behavior of the system. The efficiency of the proposed controllers has been validated using simulations.

A SECURE AND ROBUST CONGESTION CONTROL MODEL WITH FRAGMENTATION FOR CLUSTERED DISTRIBUTED DATABASE SYSTEM

The purpose of this paper is to present a secure and robust way to adopt Distributed Database System (DDBS). Distributed database is becoming very popular now a day. Today’s business environment has an increasingly need for distributed database and client/server applications. We know that the aim of current technologies is to fulfil the desire for consistent, scalable, reliable and accessible information i.e. steadily growing information. In Distributed Database System, the data fragmentation and distributed database site’s clustering problems are NP-Hard in nature and difficult to solve. One of the points considered that may cause loss of data means, while upgrading from Relational Database System, data distribution over different data-sites uses fragmentation. The database queries to access the applications on the data-sites and should be performed effectively. Therefore, the fragments that accessed by queries are needed to be allocated to the distributed database sites. Thus, fragmentation should be as secure as data be lossless during query execution. And, the second one may cause expensiveness of technology changes. Thus, we present a method for grouping the sites (clustering) of Distributed Database Systems in order to achieve not only secure fragmentation but also robust and cost effective congestion control.

MAQ: A Multiple Model Predictive Congestion Control Scheme for Cognitive Radio Networks

In this paper, we investigate the problem of robust congestion control in infrastructure-based cognitive radio networks (CRN). We develop an active queue management algorithm, termed MAQ, which is based on multiple model predictive control. The goal is to stabilize the TCP queue at the base station under disturbances from the time-varying service capacity for secondary users. The proposed MAQ scheme is validated with extensive simulation studies under various types of background traffic and system/network configurations. It outperforms two benchmark schemes with considerable gains in all the scenarios considered.

A robust congestion control scheme for cluster wireless multimedia sensor networks with propagation delay and external interference

Congestion control in cluster wireless multimedia sensor networks is capable of delivering and gathering multimedia information, while propagation delay and external interference may cause it unable to achieve the maximal network throughput without congestion control. To solve the problem, based on the additive-increase multiplicative-decrease adjustment strategy, a robust congestion control model is proposed with a scheduling scheme in cluster wireless multimedia sensor networks considering the neighbor feedback. The robust congestion control model is a Lur’e-type system. The cluster wireless multimedia sensor network is capable of achieving the desired cluster synchronization with network parameters provided by Lyapunov–Krasovskii functionals. Sufficient conditions of the robust congestion control are formulated by linear matrix inequalities. Numerical results show the effectiveness of the proposed scheme.

Global Asymptotic Stability Analysis of FAST TCP Using Nonlinear Small Gain Theorem

We investigate the global asymptotic stability of FAST TCP based on a continuous-time model for FAST TCP network in which all of cross traffics, time-varying network feedback delays, and queuing delay dynamics at link are taken into consideration. We recognize the dynamic interaction between FAST TCP source and link as a feedback interconnection of two input-to-state stable(ISS) subsystems. Then, applying the Razumikhin-type nonlinear small gain theorem, we prove that FAST TCP network is inherently ISS and globally asymptotically stable without any specific conditions on the tuning parameter α or update gain γ for FAST TCP operation. The ISS property of FAST TCP guarantees the robustness to disturbances, and the approach using the small gain theorem provides a guideline to design a globally stable and robust congestion control algorithm. The simulation results demonstrate the validity of the global asymptotic stability of FAST TCP.

Active Queue Management in Wireless Networks by Using Nonlinear Extended Network Disturbance

This paper studies congestion control in local wireless networks supporting transmission control protocol (TCP) under random early detection (RED). In general, congestion delays may cause instability of the routers running active queue management (AQM) protocols such as RED. Moreover, its management efficiency could be reduced by serious queue oscillation. To improve these two drawbacks, we propose a robust congestion controller focusing on the queue oscillation suppression and time-delay compensation based on an idea of nonlinear extended network disturbance (NEND). The time-delay compensation is further performed via the NEND rejection. Furthermore, the proposed strategy has been validated on NS2 showing their applicability.

Adaptive Congestion Controller for ABR Traffic in ATM Network

One of the imperatives for improving the quality-of-service (QoS) of high-speed networks of next generation is to reduce congestion. Congestion occurs when the resource demand exceeds the capacity of the network. The purpose of this paper is to analyze the performance of Robust Adaptive Congestion Control algorithm for available bit rate traffic in asynchronous transfer mode networks and suggest improvements of the algorithm for better performance and fairness. The results of simulation presented here corroborates the fact that the improved algorithm reduces settling time and cell loss ratio and thus maximizes utilization of the network.

A robust adaptive congestion control strategy for large scale networks with differentiated services traffic

In this paper, a robust decentralized congestion control strategy is developed for a large scale network with Differentiated Services (Diff-Serv) traffic. The network is modeled by a nonlinear fluid flow model corresponding to two classes of traffic, namely the premium traffic and the ordinary traffic. The proposed congestion controller does take into account the associated physical network resource limitations and is shown to be robust to the unknown and time-varying delays. Our proposed decentralized congestion control strategy is developed on the basis of Diff-Serv architecture by utilizing a robust adaptive technique. A Linear Matrix Inequality (LMI) condition is obtained to guarantee the ultimate boundedness of the closed-loop system. Numerical simulation implementations are presented by utilizing the QualNet and Matlab software tools to illustrate the effectiveness and capabilities of our proposed decentralized congestion control strategy.

Design of a stabilizing AQM controller for large-delay networks based on internal model control

We focus on the problem of the stability of congestion control for networks with large round-trip communication delays. Nearly all the existing AQM schemes neglect the impact large communication delay has on system behavior, such as stability, robustness and convergence. The drastic queue oscillations in large delay networks of PI, REM and DC-AQM decrease link utilization and introduce avoidable delay jitter. To address this problem, we propose a robust IMC-PID congestion controller based on the internal model control principle to restrict the negative impact of the stability caused by large delay. Simulation results demonstrate that the integrated performance of our proposed scheme outperforms others as communication delay increases, and achieves high link utilization and small delay jitter.

An Efficient Designed Robust Adaptive Congestion Controller for ABR Traffic in ATM Network

In the computer networks, congestion occurs when the resource demands exceeds the capacity, which the network can provide. The presence of too many packets in the network, which the network is not able to cope with due to its limited resources and it begins losing packets, gives birth to the situation called congestion.The aims of this paper is to analyze the performance of Robust Adaptive Congestion Control algorithm for ABR traffic in ATM networks and modify this algorithm for better performance. The main goal of ABR traffic control is to provide fairness among all the VCs with a minimal cell loss ratio and maximum utilization of the network.

Performance of TCP applications over ATM networks with ABR and UBR services—a simulation analysis

This paper aims to present a comparative simulation study of the performance of TCP traffic over ATM networks with UBR and ABR services; to identify weaknesses of currently most promising ATM congestion control schemes; and to indicate the requirements for a fairer, simpler and more robust congestion control mechanism. Variety of congestion control schemes under various network configurations and traffic patterns are simulated for a comprehensive analysis. Simulation results show that Early Packet Discard (EPD) wastes buffer capacity and has no provision for fairness, Intelligent Marking [K.Y. Siu, H.Y. Tzeng, Computer Communication Review 24 (5) (1995) 81–106] does not provide adequate fairness among competitive connections, and ERICA+ [R. Jain, S. Kalyanaraman, Y. Goyal, S. Fahmy, R. Viswanathan, ERICA switch algorithm: a complete description, ATM Forum Contribution 96-1172, 1996] is too restrictive and unnecessarily complicated. The paper also indicates that TCP flow control and ATM congestion control do not cooperate well when buffer overflows and retransmission occurs, necessitating further control mechanisms for improving performance.