Sending Rate Control Research Articles

Intelligent Forwarding Strategy for Congestion Control Using Q-Learning and LSTM in Named Data Networking

Named data networking (NDN) is a future network architecture that replaces IP-oriented communication with content-oriented communication and has new features such as cache, multiple paths, and multiple sources. Services such as video streaming, to which NDN can be applied in the future, can cause congestion if data is concentrated on one of the nodes during high demand. To solve this problem, sending rate control methods such as TCP congestion control have been proposed, but they do not adequately reflect the characteristics of NDN. Therefore, we use reinforcement learning and deep learning to propose a congestion control method that takes advantage of multipath features. The intelligent forwarding strategy for congestion control using Q-learning and long short-term memory in NDN proposed in this paper is divided into two phases. The first phase uses an LSTM model to train a pending interest table (PIT) entry rate that can be used as an indicator to detect congestion by knowing the amount of data returned. In the second phase, it is forwarded to an alternative path that is not congestive via Q-learning based on the PIT entry rate predicted by the trained LSTM model. The simulation results show that the proposed method increases the data reception rate by 6.5% and 19.5% and decreases the packet drop rate by 7.3% and 17.2% compared to an adaptive SRTT-based forwarding strategy (ASF) and BestRoute.

Fuzzy and Parallel Enhanced Congestion Detection and Avoidance for Multiple Class of Traffic in Wireless Network

Background/Objectives: Congestion in wireless network causes loss of packets, longer delay and throughput degradation. So it is always a major concern to network. Many sources send data of different priorities in different applications like Multimedia. So weighted fairness for these applications is also required. Methods: To address these issues we propose a protocol named FPECODA for Mobile Ad hoc Network. Findings: It uses the GPU for parallel version of algorithm for finding the Dynamic Priority of packets, sorting algorithm etc. It also applies the fuzzy logic for rate control of node. Subsequently handling the congestion will become more effective by: a) Bottleneck node based source data sending rate control. b) Flexible Queue Scheduler for packets transfer. Improvements: Experimental results demonstrate that processing of packets becomes significantly faster than CPU only based algorithm. Also it will be very efficient in terms of throughput, end to end delay and fairness in network.

An End-To-End Rate Control Protocol for Intermittently Connected Networks

In this study, we propose a rate control algorithm for intermittently connected networks (ICNs), a type of delay/disruption-tolerant network. The different characteristics of ICNs compared with traditional TCP/IP networks lead to problems that do not occur in legacy networks, such as network partitioning, long and varying delays, high loss probability, and asymmetric data transmission rates. To overcome these issues, much recent research has been carried out. However, while the results have helped achieve smooth communication between nodes, there are still certain shortcomings. For example, if many messages are input by the nodes in a network, these schemes do not work well. To resolve this problem, we propose a novel sending rate control algorithm in an additive increase/multiplicative decrease manner. To detect network congestion, we measure the receiving rate, one-way delay, and average number of copies. When the measured receiving rate increases, the congestion window (cwnd) also increases; otherwise, cwnd decreases. The simulations show that the proposed algorithm can adjust the sending rate of nodes to avoid network congestion and provide a fair share of the network for the nodes.

ECODA: enhanced congestion detection and avoidance for multiple class of traffic in sensor networks

Congestion in a Wireless Sensor Network (WSN) can cause missing packets, low energy efficiency, and long delay. Moreover, some applications, e.g. multimedia and image, need to transmit large volumes of data concurrently from several sensors. These applications have different delay and QoS requirements. Congestion problem is more urgent in such applications. To address this challenge, we propose a novel energy efficient congestion control scheme for sensor networks, called ECODA (Enhanced congestion detection and avoidance) which comprises three mechanisms: (1) Use dual buffer thresholds and weighted buffer difference for congestion detection; (2) Flexible Queue Scheduler for packets scheduling; (3) A bottleneck-node-based source sending rate control scheme. Simulations show that ECODA achieves efficient congestion control and flexible weighted fairness for different class of traffic. Therefore it leads to higher energy efficiency and better QoS in terms of throughput, fairness, and delay.

Prototype Implementation of a Proxy Caching System for Streaming Media Objects

Existing techniques for caching Web objects are not appropriate for the multimedia streaming service. In this paper, the authors focus on the proxy caching problem specifically for multimedia streaming objects. A prototype design and implementation of a proxy caching system – HUSTProxy is proposed. The main contribution of HUSTProxy is its ability of partial video caching, sending rate control and providing a more rational prefix caching size determination. These techniques are implemented in our design and are discussed in detail in this paper.

Adaptive Control Algorithms for Decentralized Optimal Traffic Engineering in the Internet

In this paper, we address the problem of optimal decentralized traffic engineering when multiple paths are available for each call. More precisely, given a set of possible paths for each call, we aim at distributing the traffic among the available paths in order to maximize a given utility function. To solve this problem, we propose a large family of decentralized sending rate control laws having the property that each of the members of this family "steers" the traffic allocation to an optimal operation point. The approach taken relies on the control theory concept of Sliding Modes. These control laws allow each ingress node to independently adjust its traffic sending rates and/or redistribute its sending rates among multiple paths. The only nonlocal information needed is binary feedback from each congested node in the path. The control laws presented are applicable to a large class of utility functions, namely, utility functions that can be expressed as the sum of concave functions of the sending rates. We show that the technique can be applied not only to usual rate adaptive traffic with multiple paths, but also to rate adaptive traffic with minimum service requirements and/or maximum allowed sending rate and to assured service with targeted rate guarantee, all allowing for multiple paths. It is also shown that these control laws are robust with respect to failures; i.e., they automatically reroute traffic if a link failure occurs. Finally, we provide some insight on how to choose the "right" control law. In particular, we provide a way of choosing a member of the family of control laws that reduces the sending rate oscillation caused by implementation constraints like delays and quantization. An example of application of the approach delineated in this paper is also presented. This example provides some insights on the implementation aspects and illustrates the robustness of the control laws developed in this paper.