Understanding CHOKe: throughput and spatial characteristics

A recently proposed active queue management, CHOKe, is stateless, simple to implement, yet surprisingly effective in protecting TCP from UDP flows. We present an equilibrium model of TCP/CHOKe. We prove that, provided the number of TCP flows is large, the UDP bandwidth share peaks at (e+1)/sup -1/=0.269 when UDP input rate is slightly larger than link capacity, and drops to zero as UDP input rate tends to infinity. We clarify the spatial characteristics of the leaky buffer under CHOKe that produce this throughput behavior. Specifically, we prove that, as UDP input rate increases, even though the total number of UDP packets in the queue increases, their spatial distribution becomes more and more concentrated near the tail of the queue, and drops rapidly to zero toward the head of the queue. In stark contrast to a nonleaky FIFO buffer where UDP bandwidth shares would approach 1 as its input rate increases without bound, under CHOKe, UDP simultaneously maintains a large number of packets in the queue and receives a vanishingly small bandwidth share, the mechanism through which CHOKe protects TCP flows

Drop counters are enough.

Small Flow Completion Time (FCT) of short-lived flows, and fair bandwidth allocation of long-lived flows have been two major, usually concurrent, goals in the design of resource allocation algorithms. In this paper we present a framework that naturally unifies these two objectives under a single umbrella; namely by proposing resource allocation algorithm Markov Active Yield (MAY). Based on a probabilistic strategy: "drop proportional to the amount of past drops", MAY achieves very small FCT among short-lived flows as well as max-min fair bandwidth allocation among long-lived flows, using only the information of short history of already dropped packets. It turns out that extremely small amount of on-chip SRAM (roughly 1 bit per flow in Pareto-like flow size distributions) is enough for storing this drop history. Analytical models are presented and analyzed and accuracy of results is verified experimentally using packet level ns2 simulations

FavorQueue: A parameterless active queue management to improve TCP traffic performance

This paper presents and analyzes the implementation of a novel active queue management (AQM) named FavorQueue that aims to improve delay transfer of short lived TCP flows over best-effort networks. The idea is to dequeue packets that do not belong to a flow previously enqueued first. The rationale is to mitigate the delay induced by long-lived TCP flows over the pace of short TCP data requests and to prevent dropped packets at the beginning of a connection and during recovery period. Although the main target of this AQM is to accelerate short TCP traffic, we show that FavorQueue does not only improve the performance of short TCP traffic but also improves the performance of all TCP traffic in terms of drop ratio and latency whatever the flow size. In particular, we demonstrate that FavorQueue reduces the loss of a retransmitted packet, decreases the number of dropped packets recovered by RTO and improves the latency up to 30% compared to DropTail. Finally, we show that this scheme remains compliant with recent TCP updates such as the increase of the initial slow-start value

Traffic classification and management based flow statistics netfpga

The internet bandwidth increased significantly over the past years but the problem of network bandwidth management remained a key issue. One of the major problems associated with bandwidth management is network bottleneck, which is the overcapacity of network traffic due to abnormal application bandwidth usage. With the release of new applications every year, especially P2P applications that require high bandwidth, effective network management has become even more important. Congestion can be caused inside a network by numerous flows and high bandwidth applications that may dominate the total bandwidth allocation, affecting normal users. This report presents an approach to detect and manage high bandwidth traffic flows in a congested network, providing fair bandwidth usage to normal users and restricting bandwidth-heavy applications. Flow statistics information is used for classification of network traffic by applying k-means clustering. An inline rate-limiter technique based on queue management is used for controlling high bandwidth flows. The proposed traffic shapping method queues the header packets of flows that are classified as high bandwidth flows. These modules are integrated into the NetFPGA platform, where decision making is carried out with minimal intervention of network administrators by only updating the classifier model when accuracy falls below a threshold line. It ensure zero intrusion of user privacy and at the same time it is able to reduce the high bandwidth rate, providing fair network usage for home users

Maximum Production Of Transmission Messages Rate For Service Discovery Protocols

Minimizing the number of dropped User Datagram Protocol (UDP) messages in a network is regarded as a challenge by researchers. This issue represents serious problems for many protocols particularly those that depend on sending messages as part of their strategy, such us service discovery protocols. This paper proposes and evaluates an algorithm to predict the minimum period of time required between two or more consecutive messages and suggests the minimum queue sizes for the routers, to manage the traffic and minimise the number of dropped messages that has been caused by either congestion or queue overflow or both together. The algorithm has been applied to the Universal Plug and Play (UPnP) protocol using ns2 simulator. It was tested when the routers were connected in two configurations; as a centralized and de centralized. The message length and bandwidth of the links among the routers were taken in the consideration. The result shows Better improvement in number of dropped messages `among the routers