Pre-Congestion Notification (PCN) Architecture

This document describes a general architecture for flow admission and termination based on pre-congestion information in order to protect the quality of service of established, inelastic flows within a single Diffserv domain.\u

Pre-Congestion Notification (PCN) Boundary-Node Behavior for the Single Marking (SM) Mode of Operation

Pre-Congestion Notification (PCN) is a means for protecting the quality of service for inelastic traffic admitted to a Diffserv domain. The overall PCN architecture is described in RFC 5559. This memo is one of a series describing possible boundary-node behaviors for a PCN-domain. The behavior described here is that for a form of measurement-based load control using two PCN marking states: not-marked and excess-traffic-marked. This behavior is known informally as the Single Marking (SM) PCN-boundary-node behavior

Requirements for Signaling of Pre-Congestion Information in a Diffserv Domain

Pre-Congestion Notification (PCN) is a means for protecting quality of service for inelastic traffic admitted to a Diffserv domain. The overall PCN architecture is described in RFC 5559. This memo describes the requirements for the signaling applied within the PCN- domain: (1) PCN-feedback-information is carried from the PCN-egress-node to the Decision Point; (2) the Decision Point may ask the PCN-ingress-node to measure, and report back, the rate of sent PCN-traffic between that PCN-ingress-node and PCN-egress-node. The Decision Point may be either collocated with the PCN-ingress-node or a centralized node (in the first case, (2) is not required). The signaling requirements pertain in particular to two edge behaviors, Controlled Load (CL) and Single Marking (SM)

Flow termination signaling in the centralized pre-congestion notification architecture

Pre-congestion notification (PCN) protects inelastic traffic by using feedback on network link loads on and acting upon this accordingly. These actions comprise to admission control and termination of flows. Two PCN architectures have been defined by IETF: the centralized and decentralized PCN architecture. The decentralized PCN architecture has received much attention in the literature whereas the centralized PCN architecture has not. In the decentralized architecture, feedback is sent from the egress nodes to ingress nodes, which then take and apply decisions regarding admission of new flows and/or termination of ongoing flows. Signaling occurs only between ingress and egress nodes. In the centralized architecture these decisions are made at a central node, which requires proper signaling for action and information exchange between the central node and the egress and ingress nodes. This signaling has been suggested by other authors, but is not fully defined yet. Our contribution is twofold. We define signaling in the centralized PCN architecture focussing on flow termination, which completes the definition of the signaling in the centralized PCN architecture. Secondly, we run extensive simulations showing that the proposed signaling works well and that the performances of the centralized PCN and the decentralized PCN architectures are similar. Hence, it is expected that results from existing research on the effectiveness of decentralized PCN are also valid when the centralized PCN architecture is used

Pre-Congestion Notification Encoding Comparison

DiffServ mechanisms have been developed to support Quality of Service (QoS). However, the level of assurance that can be provided with DiffServ without substantial over-provisioning is limited. Pre-Congestion Notification (PCN) investigates the use of per-flow admission control to provide the required service guarantees for the admitted traffic. While admission control will protect the QoS under\ud normal operating conditions, an additional flow termination mechanism is necessary in the times of heavy congestion (e.g. caused by route changes due to link or node failure).\ud Encoding and their transport are required to carry the congestion and pre-congestion information from the congestion and pre-congestion points to the decision points. This document provides a survey of\ud several encoding methods, using comparisons amongst them as a way to explain their strengths and weaknesses.\u

LC-PCN: The Load Control PCN Solution

There is an increased interest of simple and scalable resource provisioning solution for Diffserv network. The Load Control PCN (LC-PCN) addresses the following issues:\ud o Admission Control for real time data flows in stateless Diffserv Domains\ud o Flow Termination: Termination of flows in case of exceptional events, such as severe congestion after re-routing.\ud Admission control in a Diffserv stateless domain is a combination of:\ud o Probing, whereby a probe packet is sent along the forwarding path in a network to determine whether a flow can be admitted based upon the current congestion state of the network\ud o Admission Control based on data marking, whereby in congestion situations the data packets are marked to notify the PCN-egress-node that a congestion occurred on a particular PCN-ingress-node to PCN-egress-node path.\ud \ud The scheme provides the capability of controlling the traffic load in the network without requiring signaling or any per-flow processing in the PCN-interior-nodes. The complexity of Load Control is kept to a minimum to make implementation simple.\u

Joint in-network video rate adaptation and measurement-based admission control: algorithm design and evaluation

The important new revenue opportunities that multimedia services offer to network and service providers come with important management challenges. For providers, it is important to control the video quality that is offered and perceived by the user, typically known as the quality of experience (QoE). Both admission control and scalable video coding techniques can control the QoE by blocking connections or adapting the video rate but influence each other's performance. In this article, we propose an in-network video rate adaptation mechanism that enables a provider to define a policy on how the video rate adaptation should be performed to maximize the provider's objective (e.g., a maximization of revenue or QoE). We discuss the need for a close interaction of the video rate adaptation algorithm with a measurement based admission control system, allowing to effectively orchestrate both algorithms and timely switch from video rate adaptation to the blocking of connections. We propose two different rate adaptation decision algorithms that calculate which videos need to be adapted: an optimal one in terms of the provider's policy and a heuristic based on the utility of each connection. Through an extensive performance evaluation, we show the impact of both algorithms on the rate adaptation, network utilisation and the stability of the video rate adaptation. We show that both algorithms outperform other configurations with at least 10 %. Moreover, we show that the proposed heuristic is about 500 times faster than the optimal algorithm and experiences only a performance drop of approximately 2 %, given the investigated video delivery scenario

Analytical Study of Pre-Congestion Notification (PCN) Techniques

Maintaining the quality of service (QOS) and controlling the network congestion are quite complicated tasks. They cause degrading the performance of the network, and disturbing the continuous communication process. To overcome these issues, one step towards this dilemma has been taken in form of Pre-congestion notification (PCN) technique. PCN uses a packet marking technique within a PCN domain over IP networks. It is notified by egress node that works as guard at entry point of network. Egress node gives feedback to communicating servers whether rate on the link is exceeded than configured admissible threshold or within the limit. Based on this feedback, admission decisions are taken to determine whether to allow/block new coming flows or terminate already accepted. The actual question is about selection of right algorithm for PCN domain. In this paper, we investigate the analytical behavior of some known PCN algorithms. We make slide modifications in originality of PCN algorithms without disquieting working process in order to employ those within similar types of scenarios. Our goal is to simulate them either in highly congested or less congested realistic scenarios. On the basis of simulation done in ns2, we are able to recommend each PCN algorithm for specific conditions. Finally, we develop a benchmark that helps researchers and scientific communities to pick the right algorithm. Furthermore, the benchmark is designed to achieve specific objectives according to the users’ requirements without congesting the network