Congestion Control Mechanism Research Articles

Early Detection of LDoS Attack using SNMP MIBs

Early detection of Denial of Service (DoS) attacks are given more emphasizing due to its adverse effects on disrupting the services of legitimate users. LDoS attack is one among the DoS category which floods the target at ideal rate to keep the connections open for longer duration. Traditional defense measures are inadequate to filter due to its less traffic volume. The current works focus on either empirical studies or signal processing models to capture the behavioural characteristics of LDoS based on TCP’s congestion control and timeout mechanism but none carries out detection at a faster timestamp. Early detection solutions are the main focus as it could scale up the revenue losses in today’s online application issues. Hence our model is based on Simple Network Management Protocol (SNMP), through which the early detection of LDoS attacks is carried out. The relevant detection metrics are identified through theoretical validation of SNMP MIBs and existing dataset analysis. Experimental simulations illustrate the LDoS detection efficiency and the same has been validated for theoretically.

Fragment Forwarding in Lossy Networks

This paper evaluates four forwarding strategies for fragmented datagrams in the Internet of Things (IoT). We focus on classic end-to-end fragmentation, hop-wise reassembly, a minimal approach to direct forwarding of fragments, and direct forwarding utilizing selective fragment recovery. To fully analyze the potentials of selective fragment recovery, we include four common congestion control mechanisms. We compare all fragmentation strategies comprehensively in extensive experiments to assess reliability, end-to-end latency, and memory consumption on top of IEEE 802.15.4 and its common CSMA/CA MAC implementation. Our key findings include three takeaways. First, direct fragment forwarding should be deployed with care since higher packet transmission rates on the link layer can significantly reduce reliability, which can even further increase end-to-end latency because of highly increased link layer retransmissions. Second, selective fragment recovery can mitigate the problems underneath. Third, congestion control for selective fragment recovery should be chosen such that small congestion windows grow together with fragment pacing. In case of fewer fragments per datagram, pacing is less of a concern but the congestion window is limited by an upper bound.

Prioritizing Relevant Information: Decentralized V2X Resource Allocation for Cooperative Driving

Cooperative driving is a promising approach to increase traffic efficiency and safety but requires high Vehicle-to-Everything (V2X) communication performance for coordinating and executing a cooperative maneuvers, especially in scenarios with high vehicle densities under congested channel conditions. Recent studies identified that content-agnostic congestion control mechanisms deployed in decentralized V2X networks maintain the radio communication performance under congested channel conditions but severely degrade the communication performance from the application perspective, such as for cooperative driving, as focused in this paper. Thus, we propose a relevance-aware resource allocation mechanism for decentralized V2X networks that prioritizes vehicles with relevant information and counteracts channel conditions. Our evaluation results show that our proposed approach maintains the radio communication performance and significantly increases the communication performance from the application perspective compared to content-aware resource allocation and content-agnostic congestion control mechanisms.

ModQUIC protocol performance verification with CUBIC and BBR congestion control mechanisms

ModQUIC protocol performance verification with CUBIC and BBR congestion control mechanisms

Protocols for Transferring Bulk Data Over Internet: Current Solutions and Future Challenges

Big Data has gained interests in effectively capturing, storing, analysis and visualisation from wide range of scientific, economic and business communities and is frequently communicated over internet for various purposes among government and enterprise sectors sited at different locations. Several experiments and analyses have shown that currently employed applications and transport protocols in internet are not suitable for transferring such voluminous data because of not addressing requirements of low-access latency. This paper presents issues associated with the basic mechanism of legacy protocols in the context of high speed networks for transferring Big Data e.g. conservative TCP congestion control mechanism may result in minute utilisation of high bandwidth provisioning networks. We present state-of-art alternatives proposed in the literature to solve these problems in high speed networks. We compare several underlying emerging alternatives of TCP, UDP and multi-TCP-streams protocols over a number of comparison criteria e.g. protocol convergence, responsiveness etc., to handle communication of huge data. We note that these protocol alternatives have significant importance over fulfilling requirements of emerging data-intensive applications in high-speed networks. In addition, we discuss open research issues and challenges that can be explored as a source of motivation towards development and deployment of data-intensive applications in emerging networking technologies.

ABCSS: A novel approach for increasing the TCP congestion window in a network

Congestion control is a useful task to be taken into account in order to maintain a good traffic in a network. In the case of a TCP/IP (Transmission Control Protocol/Internet Protocol) network, congestion creates queues in routers and even packet rejection. It is the reason why research have been conducted to improve congestion control mechanisms. Nowadays, several TCP congestion algorithms have been implemented. However, with the technology evolution and the need in terms of traffic capacity, various mechanisms implemented by these algorithms have shown their limitations and still need to be improved. The aim of this paper is to develop a novel TCP congestion control approach based on two famous algorithms: Appropriate Byte Counting (ABC) and Slow Start (SS). The results obtained show that ABC increases the congestion window appropriately compared to Slow Start. However, the congestion window remains invariant during the first round trips, which causes the TCP burst phenomenon. The TCP splitting can cause buffer overflows and long queues. For theses shortcomings, we propose ABCSS as a novel approach that appropriately increase the TCP congestion window and minimize the TCP splitting problem. The simulations of ABCSS algorithm provide interesting results compared to some known algorithms.

ModQUIC protocol performance verification with CUBIC and BBR congestion control mechanisms

ModQUIC protocol is developed to compete for TCP's dominance. In this work, ModQUIC protocol performance is evaluated using the BBR protocol and suggested a new decrease factor (β) for CUBIC protoc...

EC-Elastic an Explicit Congestion Control Mechanism for Named Data Networking

EC-Elastic an Explicit Congestion Control Mechanism for Named Data Networking

High-Potency Models of LDoS Attack Against CUBIC + RED

A TCP-targeted Low-rate denial of service (LDoS) attack exploits the vulnerabilities of TCP congestion control mechanism. CUBIC is the most widely used TCP congestion control algorithm today. CUBIC TCP increases the resilience against LDoS over traditional TCP. This paper explores high-potency patterns of the LDoS attack against CUBIC TCP under RED queue management scenario, and accordingly develops two attack models referring to maximizing attack potency. Theoretical analyses and extensive experiments are conducted to validate the proper function of the two models and evaluate their performance. Test results show that the two attack models can effectively throttle CUBIC TCP throughput. Under standard-configured network parameters, the number of TCP units damaged by one attack unit are up to about 21 and 26 respectively for our proposed two models, which represents an increase in attack potency of about 20%. And, our proposed models outperform the traditional attack model in terms of attack potency by at least 250%. In addition, with variations in different network parameters, these two models are still efficient and alternatively maximize the attack potency. Finally, an outline for the attack countermeasure is discussed. The present study offers a basis to explore new attack manners which may be exploited by attackers and excites defenders to develop new measurements against such attack.

STCC: A SDN‐oriented TCP congestion control mechanism for datacenter network

Due to characteristics of high bandwidth and low latency, datacenter networks ensure tremendous data could be transmitted in an efficient way. However, in many-to-one transmission scenarios, high concurrency of TCP flows aggravates network congestion and causes overflows in switches, seriously impairing network performance. To solve the problem, a TCP congestion control mechanism based on software-defined networking (STCC) is proposed. Without any modification in TCP stack, STCC monitors network performance through centralized control and global network view of SDN, employs a routing algorithm based on the minimum path bandwidth utilization rate to forward packets and uses different methods to adjust congestion windows of senders so that network congestion can be greatly mitigated. An experiment platform is built to carry out simulation tests for evaluating STCC, and the results show that under the same network conditions, STCC effectively reduces the number of retransmission timeout of senders and noticeably raises network throughput, compared with other congestion control algorithms.

Multi-Path Selection and Congestion Control for NDN: An Online Learning Approach

In Named Data Networking (NDN) architecture, data can be obtained from multiple content sources (i.e., producers or caching nodes) with multiple paths, making the traditional end-to-end (i.e., TCP/IP) congestion control scheme invalid. In addition, the NDN multi-path discovery and management are still an open issue as the dynamic network topology changes. In this article, we propose a multi-path congestion control mechanism, named MPCC, which includes two major components, i.e., multi-path discovery and multi-path congestion control. Particularly, for multi-path discovery, we first devise a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path tag</i> to uniquely mark each sub-path in the forwarding process, and then propose a tag-aware forwarding strategy to discover and manage sub-paths. For multi-path selection and congestion control, we first integrate the metrics of packet loss, bandwidth, round trip time, and path centrality, for path assessment, based on which, we then leverage the Upper Confidence Bound (UCB) algorithm to select sub-paths in order to maximize the network throughput. In addition, for selected sub-paths, we have devised a sub-path window adaptation algorithm to avoid multi-path congestions. At last, we implement MPCC in ndnSIM and conduct extensive experiments for performance evaluation. Our results demonstrate that MPCC can discover all sub-paths in real-time for the multi-path scenario, and can effectively avoid multi-path congestions with improving throughput and reducing transmission time.

EnCoCo-RED: Enhanced congestion control mechanism for CoAP observe group communication

As Internet of Things (IoT) applications have gained increasing popularity, mechanisms to deal with congestion control owing to the large number of nodes have become a critical requirement. The Constrained Application Protocol (CoAP) is designed for low-power and lossy networks, using messages on the UDP, which relies on simple congestion control mechanisms. This method is suitable for IoT environments and can handle several nodes. In this study, we propose Enhanced Congestion Control – Random Early Detection (EnCoCo-RED), which is a more recent version that can help improve congestion control mechanism for CoAP observe group communication. The mechanism provides buffer management, along with the adjustment of backoff algorithms, according to the congestion levels in a network.To test its effectiveness, the mechanism was operated in two scenarios (burst traffic scenario and constant traffic scenario) that differ in terms of network traffic volumes. The experimental results demonstrate that EnCoCo-RED outperforms the CoAP and the previous CoCo-RED in terms of the settling time, throughput, packet loss, and response time.

Distance‐based congestion control mechanism for CoAP in IoT

Designing a scalable and efficient application layer protocols for the Internet of Things (IoT) environment is an ongoing problem. The constrained application protocol (CoAP) offering RESTful services for IoT devices implements a simple congestion control mechanism. This study proposes and analyses the distance-based congestion control CoAP (DCC-CoAP) for the current state of the network and accordingly calculate the future flow rate to handle congestion. The authors’ main idea is to use the combination of distance between nodes and round trip time (RTT) measurements which limits the losses of CoAP messages, which is an efficient way to predict the network congestion. The user-defined options, i.e. retransmission counter and timestamp of sent and received CON and ACK messages are introduced for mapping CON to ACK and measure RTT. The comparative analysis of the DCC-CoAP with other congestion control mechanism like default CoAP, CoCoA+ is also highlighted in this study. With low additional computations, future RTT can be measured for avoiding congestion, and retransmission timeout varies with distance to avoid transaction losses. An algorithm with varying data rate has been used in homogeneous and mixed traffic to calculate the parameters in terms of packet delivery ratios, delay and retransmission count.

GoT: Decreasing DCC Queuing for CAM Messages

Vehicular networks use Decentralized Congestion Control (DCC) mechanisms to operate effectively, but this mechanism may introduce queuing delays. Freshness of Cooperative Awareness Messages (CAMs) is critical for their usefulness. In this letter we explore how the presence of other types of traffic additional to CAMs, even with lower priorities, has an impact on the freshness of CAM messages due to DCC queuing. Finally, we propose Generate-on-Time (GoT), which is a simple mechanism that reduces DCC queuing delays for CAM messages without introducing any downside in other performance metrics.

Improving Congestion Control of TCP for Constrained IoT Networks.

For smooth integration with middleboxes on the Internet, TCP (Transmission Control Protocol) is favorably considered as a transport-layer protocol for IoT (Internet of Things) networks. In constrained networks, TCP tends to perform well with a small window size. For example, the uIP (micro IP) TCP/IP stack sets the TCP window size to one segment by default. In such a case, managing the retransmission timer is a primary approach to congestion control. In this paper, we examine the congestion control mechanism of TCP in the uIP stack using grid topology networks. In the preliminary test using the Cooja network simulator, the results show that the original uIP TCP causes lots of retransmissions when a radio duty cycling mechanism such as ContikiMAC is used. One main reason is that, once retransmission is deemed to be necessary, the original uIP TCP sets the retransmission timer based on the fixed RTO (retransmission timeout) before performing a retransmission. Since ContikiMAC may cause large RTT (round-trip time) variations due to the hidden terminal problem, the retransmission timer based on the fixed RTO value may cause lots of retransmissions. To address the problem, we propose a new scheme for managing the retransmission timer which adopts the notion of weak RTT estimation of CoCoA, exponential backoffs with variable limits, and dithering. Simulation results show that our proposed scheme reduces retransmissions while enhancing throughput and fairness when an RDC (radio duty cycling) mechanism is used.

MRGM: An Adaptive Mechanism for Congestion Control in Smart Vehicular Network

Traffic flow on roads has increased manifolds from past few decades due to increase in number of vehicles and rise in population. With fixed road infrastructure and more vehicles on traffic routes lead to traffic congestion conditions especially in urban areas of developing nations. Traffic jams are normal in major cities which ultimately cause delay in travel time, more fuel consumption and more pollution. This manuscript propose a Multi-metric road guidance mechanism(MRGM) which considers multiple metrics to analyze the traffic congestion conditions and based on the conditions effective optimal routes are suggested to the vehicles. The Simulation of the proposed mechanism is performed with the SUMO by using the python script and the results show that proposed mechanism i.e MRGM outperforms other mechanism in terms of traffic efficiency, travel time, fuel consumption and pollution levels in the smart vehicular network.

Congestion Control Mechanism Based on Dual Threshold DI-RED for WSNs

In wireless sensor networks (WSNs), traffic flow congestion can reduce network performance. We propose a congestion control method for WSNs that use a cache state with a dual threshold in the router buffer to control congestion. In addition, the congestion is controlled by the channel transmission state that is monitored by the queuing variation tendency and the transmission rate. Based on the active queue management mechanism, a dual threshold with the double index random early detection (DI-RED) model is established to improve network performance. We also present the congestion control algorithm of DI-RED model to ensure the perfect actualization. By solving the DI-RED model, a series of channel indicators is obtained, including packet loss rate, average queue length, delay and throughput. Simulation results show that the proposed DI-RED model is more stable and demonstrates better control, which can overcome the parameter sensitivity of the RED mechanism. The proposed model realises a reasonable tradeoff between network performance and packet dropping probability, which effectively avoids degraded network performance caused by pursuing a lower packet dropping probability. The results are important when selecting a congestion control mechanism and parameter settings for WSNs.

A solution to MPTCP’s inefficiencies under the incast problem for Data Center Networks

In recent years several multipath data transport mechanisms, such as MPTCP and XMP, have been introduced to effectively exploit the path diversity of data center networks (DCNs). However, these multipath schemes have not been widely deployed in DCNs. We argue that two key factors among others impeded their adoption: TCP incast and minimum window syndrome . First, these mechanisms are ill-suited for workloads with a many-to-one communication pattern, commonly found in DCNs, causing frequent TCP incast collapses. Second, the syndrome we discover for the first time, results in 2-5 times lower throughput for single-path flows than multipath flows, thus severely violating network fairness.To effectively tackle these problems, we propose AMP: an adaptive multipath congestion control mechanism that quickly detects the onset of these problems and transforms its multipath flow into a single-path flow. Once these problems disappear, AMP safely reverses this transformation and continues its data transmission via multiple paths. Our evaluation results under a diverse set of scenarios in a fat-tree topology with realistic workloads demonstrate that AMP is robust to the TCP incast problem and improves network fairness between multipath and single-path flows significantly with little performance loss.

Enhancement of CoAP based congestion control in IoT network - a novel approach

IoT technologies are an inclusive kind of application and provide insolent environments. IoT devices accomplish actuating and recognize tasks through unique addresses. Resources of the WSN control the memory utilization of the network, network bandwidth, and network power consumption. The traffic strategy of the WSN IoT network is different from the conservative network. CoAP is the best data protocol in handling consistency and congestion control in the IoT environment. It is very important in cluster communication in multicast with server and numerous clients. This work emphasizes CoAP due to its squat overhead to reduce consumption in memory utilization and reduced the overall network power consumption. The proposed work presents a new approach to predict congestion control with the best feasible technique. An advanced congestion control mechanism uses various margins implemented using CoAP to analyze. Network congestion information is transpiring via massive, constrained communication devices. The proposed work implementation results are shows that it is better to compare to the real terms like transmission of packets, reduction in the latency, and increase in the transmission response time and reduction in power consumption.

Congestion Control and Traffic Differentiation for Heterogeneous 6TiSCH Networks in IIoT.

The Routing Protocol for Low power and lossy networks (RPL) has been introduced as the de-facto routing protocol for the Industrial Internet of Things (IIoT). In heavy load scenarios, particular parent nodes are likely prone to congestion, which in turn degrades the network performance, in terms of packet delivery and delay. Moreover, there is no explicit strategy in RPL to prioritize the transmission of different traffic types in heterogeneous 6TiSCH networks, each according to its criticality. In this paper, we address the aforementioned issues by introducing a congestion control and service differentiation strategies to support heterogeneous 6TiSCH networks in IIoT applications. First, we introduce a congestion control mechanism to achieve load balancing under heavy traffic scenarios. The congestion is detected through monitoring and sharing the status of the queue backlog among neighbor nodes. We define a new routing metric that considers the queue occupancy when selecting the new parent node in congestion situations. In addition, we design a multi-queue model to provide prioritized data transmission for critical data over the non-critical ones. Each traffic type is placed in a separate queue and scheduled for transmission based on the assigned queue priority, where critical data are always transmitted first. The performance of the proposed work is evaluated through extensive simulations and compared with existing work to demonstrate its effectiveness. The results show that our proposal achieves improved packet delivery and low queue losses under heavy load scenarios, as well as improved delay performance of critical traffic.