Congestion Control Architecture Research Articles

A novel of congestion control architecture using edge computing and trustworthy blockchain system

Recently, Vehicular Ad-hoc Network (VANET) has been one of the emerging fields of research. Many researchers are doing their research on various challenges of VANET. Congestion or blockage has become a critical issue in intelligent transportation systems, and this problem may arise daily due to the usage of smart technology in VANET. So we need some mechanism which controlscongestion. This paper present the trustworthy, long-lasting and consistent block chain congestion control mechanism using the heterogeneity of Dullening Nural Network (DNN), Q-Learning, and Software Define Network (SDN) model for an accurate result, fixed infrastructure, together with a correct prediction of congestion when it occurs at the edge of the network and give the fast and correct decision of congestion w.r.t VANET trust, Quality of service (QOS) and other vehicles current request. The focus of our research is on distributed SDN Technology and block chain technology for the development of smart cities and linked vehicles. So we proposed an inexpensive mechanism with low latency and a low bandwidth block chain system. Based on the Simulation result, our proposed architecture gives 82% and 98% reliability and efficiency gain in a congestion environment compared to traditional approaches. This paper aims to increase throughput, Packet Delivery Ratio (PDR), energy consumption time, and less end-to-end delay and routing overhead during communication.

BCOOL: A Novel Blockchain Congestion Control Architecture Using Dynamic Service Function Chaining and Machine Learning for Next Generation Vehicular Networks

This paper presents the first, novel, dynamic, resilient, and consistent Blockchain COngestion ContrOL (BCOOL) system for vehicular networks that fills the gap of trustworthy Blockchain congestion prediction systems. BCOOL relies on the heterogeneity of Machine Learning, Software-Defined Networks and Network Function Virtualization that is customized in three hybrid cloud/edge-based On/Offchain smart contract modules and ruled by an efficient and reliable communication protocol. BCOOL’s first novel module aims at managing message and vehicle trustworthiness using a novel, dynamic and hybrid Blockchain Fog-based Distributed Trust Contract Strategy (FDTCS). The second novel module accurately and proactively predicts the occurrence of congestion, ahead of time, using a novel Hybrid On/Off-Chain Multiple Linear Regression Software-defined Contract Strategy (HOMLRCS). This module presents a virtualization facility layer to the third novel K-means/Random Forest-based On/Off-Chain Dynamic Service Function Chaining Contract Strategy (KRF-ODSFCS) that dynamically, securely and proactively predicts VNF placements and their chaining order in the context of SFCs w.r.t users’ dynamic QoS priority demands. BCOOL exhibits a linear complexity and a strong resilience to failures. Simulation results show that BCOOL outperforms the next best comparable strategies by 80% and 100% reliability and efficiency gains in challenging data congestion environments. This yields to fast, reliable and accurate congestion prediction decisions, ahead of time, and optimizes transaction validation processing time. Globally, the Byzantine resilience, complexity and attack mitigation strategies along with simulation results prove that BCOOL securely predicts the congestion and provides real-time monitoring, fast and accurate SFC deployment decisions while lowering both capital and operational expenditures (CAPEX/OPEX) costs.

Achieving Ultra-Low Latency in 5G Millimeter Wave Cellular Networks

The IMT 2020 requirements of 20 Gb/s peak data rate and 1 ms latency present significant engineering challenges for the design of 5G cellular systems. Systems that make use of the mmWave bands above 10 GHz ---where large regions of spectrum are available --- are a promising 5G candidate that may be able to rise to the occasion. However, although the mmWave bands can support massive peak data rates, delivering these data rates for end-to-end services while maintaining reliability and ultra-low-latency performance to support emerging applications and use cases will require rethinking all layers of the protocol stack. This article surveys some of the challenges and possible solutions for delivering end-to-end, reliable, ultra-low-latency services in mmWave cellular systems in terms of the MAC layer, congestion control, and core network architecture.

Rethinking congestion control architecture

After more than two decades of evolution, TCP and its end host based modifications can still suffer from severely degraded performance under real-world challenging network conditions. The reason, as we observe, is due to TCP family's fundamental architectural deficiency, which hardwires packet-level events to control responses and ignores emprical performance. Jumping out of TCP lineage's architectural deficiency, we propose Performance-oriented Congestion Control (PCC), a new congestion control architecture in which each sender controls its sending strategy based on empirically observed performance metrics. We show through preliminary experimental results that PCC achieves consistently high performance under various challenging network conditions.

MaxNet: a congestion control architecture for scalable networks

MaxNet is a distributed congestion control architecture. This paper analyzes the stability properties of MaxNet. We show that MaxNet is stable for networks with arbitrary delays, numbers of sources, capacities, and topologies. Unlike existing proposals, MaxNet does not need to estimate the number of bottleneck links on the end-to-end path to achieve these scaling properties.

A survey of congestion control schemes for multicast video applications

Congestion control for IP multicast on the Internet has been one of the main issues that challenge a rapid deployment of IP multicast. In this article, we survey and discuss the most important congestion control schemes for multicast video applications on the Internet. We start with a discussion of the different elements of a multicast congestion control architecture. A congestion control scheme for multicast video possesses specific requirements for these elements. These requirements are discussed, along with the evaluation criteria for the performance of multicast video. We categorize the schemes we present into end-to-end schemes and router-supported schemes. We start with the end-to-end category and discuss several examples of both single-rate multicast applications and layered multicast applications. For the router-supported category, we first present single-rate schemes that utilize filtering of multicast packets by the routers. Next we discuss receiver-based layered schemes that rely on routers group/flow control of multicast sessions. We evaluate a number of schemes that belong to each of the two categories.

MaxNet: a congestion control architecture

On the Internet all the bottlenecked links of a source to destination path contribute to the aggregate congestion signal that controls the source rate. We introduce an architecture, MaxNet, in which only one link, the most severely bottlenecked link on the end-to-end path, controls the source rate. We prove that MaxNet results in MaxMin fairness for sources with general homogenous utility functions and the current architecture may not. MaxNet is distributed and requires no global information or per-flow state in the link.

Loss differentiated multicast congestion control

A new layered multicast scheme known as loss differentiated multicast congestion control architecture is proposed to provide effective congestion control in heterogeneous multicast networks. It is comprised of two components, the random early detection assisted layered multicast (RALM) and the layer marking discovery protocol (LMDP). The RALM protocol utilises the packet marking and priority dropping mechanisms of the differentiated services architecture to differentiate losses in the layers of a layered multicast session. The LMDP protocol assists in the discovery of the optimal subscription levels to which packets of the layers should be marked. By marking the layers’ packets appropriately and dropping them differently during congestion, the RALM protocol guides receivers to their stable optimal subscription levels that satisfy their bandwidth requirements while providing multirate max–min fairness in the network.

Controlling congestion in B-ISDN/ATM: issues and strategies

The high-level goals of a congestion control architecture are outlined. The controls are discussed in detail, focusing on the set of controls exercised by the network and the functionality needed at the network access and in the internal network elements. The end system controls are discussed only to the extent that they can benefit from the available network controls. The motivation for the suggested controls is discussed by describing their use for selected traffic types. Example traffic types and the use of some of the controls to support these traffic types in an integrated environment are examined. >