A New CAC Method Using Queuing Theory

The CAC (Connection Admission Control) method plays an important role in the ATM (Asynchronous Transfer Mode) network environment. The CAC is the first step in the prevention of congested states in the network topology, and conducts to the optimal network resources utilization. The paper is aimed to propose an enhancement for a convolution method that is one of the statistical CAC methods used in ATM. The convolution method uses a buffer-less assumption in the estimation of the cell loss. Using formulas for the G/M/1 queuing system, the cell loss can be estimated as the buffer overflow probability. In this paper, the proposed CAC method is compared with other three statistical CAC methods, and conclusions regarding the exploitation of the CAC method are presente

Statistical CAC Methods in ATM

Admission control is a very useful tool for a network operator. It enables effective link utilization with QoS guaranty. Without doubts, CAC function will be important part in evolution of next generation networks. The question, how to choose suitable CAC method as admission control, is crucial for effective exploitation of CAC function. In this paper, we compare three statistical CAC methods providing their suitability as control for specific traffic: Method of Effective Bandwidth, Diffusion Approximation Method and Gaussian Approximation Method

B&W Call Admission Control for Multimedia Communication Networks

In the multimedia communication networks providing quality of service (QoS), an interface between the signal processing systems and the communication systems is the call admission control (CAC) mechanism. Owing to the heterogeneous traffic produced by diverse signal processing systems in a multimedia communication network, the traditional CAC mechanism that used only one CAC algorithm can no longer satisfy the aim of QoS CAC: Utilize the network resource to the most best and still satisfy the QoS requirements of all connections. For satisfying the aim of QoS CAC in the multimedia communication networks, this study proposed an innovative CAC mechanism called black and white CAC (B&W CAC), which uses two CAC algorithms. One of them is called black CAC controller and is used for the traffic with specifications more uncertain, which is called black traffic here. The other is call white CAC controller and is for the traffic with clearer specifications, which is call white traffic. Because white traffic is simple, an equivalent bandwidth CAC is taken as the white CAC. On the other hand, a neural network CAC (NNCAC) is employed to be the black CAC to overcome the uncertainty of black traffic. Furthermore, owing to more parameters needed in a QoS CAC mechanism, a hierarchical NNCAC is proposed instead of the common used NNCAC. Besides to accommodate more parameters, a hierarchical NNCAC can keep the complexity low. The simulation results show the B&W CAC can obtain higher utilization and still meet the QoS requirements of traffic sources

CAM04-1: Admission control in self aware networks

The worldwide growth in broadband access and multimedia traffic has led to an increasing need for Quality- of-Service (QoS) in networks. Real time network applications require a stable, reliable, and predictable network that will guarantee packet delivery under QoS constraints. Network self- awareness through on-line measurement and adaptivity in response to user needs is one way to advance user QoS when overall network conditions can change, while admission control (AC) is an approach that has been commonly used to reduce traffic congestion and to satisfy users' QoS requests. The purpose of this paper is to describe a novel measurement-based admission control algorithm which bases its decision on different QoS metrics that users can specify. The self-observation and self- awareness capabilities of the network are exploited to collect data that allows an AC algorithm to decide whether to admit users based on their QoS needs, and the QoS impact they will have on other users. The approach we propose finds whether feasible paths exist for the projected incoming traffic, and estimates the impact that the newly accepted traffic will have on the QoS of pre-existing connections. The AC decision is then taken based on the outcome of this analysis

CAM04-1: Admission control in self aware networks

The worldwide growth in broadband access and multimedia traffic has led to an increasing need for Quality- of-Service (QoS) in networks. Real time network applications require a stable, reliable, and predictable network that will guarantee packet delivery under QoS constraints. Network self- awareness through on-line measurement and adaptivity in response to user needs is one way to advance user QoS when overall network conditions can change, while admission control (AC) is an approach that has been commonly used to reduce traffic congestion and to satisfy users' QoS requests. The purpose of this paper is to describe a novel measurement-based admission control algorithm which bases its decision on different QoS metrics that users can specify. The self-observation and self- awareness capabilities of the network are exploited to collect data that allows an AC algorithm to decide whether to admit users based on their QoS needs, and the QoS impact they will have on other users. The approach we propose finds whether feasible paths exist for the projected incoming traffic, and estimates the impact that the newly accepted traffic will have on the QoS of pre-existing connections. The AC decision is then taken based on the outcome of this analysis

Statistical multiplexing and connection admission control in ATM networks

Asynchronous Transfer Mode (ATM) technology is widely employed for the transport of network traffic, and has the potential to be the base technology for the next generation of global communications. Connection Admission Control (CAC) is the effective traffic control mechanism which is necessary in ATM networks in order to avoid possible congestion at each network node and to achieve the Quality-of-Service (QoS) requested by each connection. CAC determines whether or not the network should accept a new connection. A new connection will only be accepted if the network has sufficient resources to meet its QoS requirements without affecting the QoS commitments already made by the network for existing connections. The design of a high-performance CAC is based on an in-depth understanding of the statistical characteristics of the traffic sources

Delay Bound: Fractal Traffic Passes through Network Servers

Delay analysis plays a role in real-time systems in computer communication networks. This paper gives our results in the aspect of delay analysis of fractal traffic passing through servers. There are three contributions presented in this paper. First, we will explain the reasons why conventional theory of queuing systems ceases in the general sense when arrival traffic is fractal. Then, we will propose a concise method of delay computation for hard real-time systems as shown in this paper. Finally, the delay computation of fractal traffic passing through severs is presented

Self-* Features for Semantic Networking

http://www.fitramen.eu/program.htmInternational audienceWe propose the Semantic Networking concept as a candidate for the Internet of the Future. Re-thinking of the architectural and functional paradigms is needed to face scalability and complexity issues in the current Internet developments. A fundamental of our proposal is to reconsider all the networking and service operations based on the flow granularity, thus beyond packet or circuit paradigms. This is enabled by the awareness of the transported traffic, thanks to a combined Deep Packet Inspection and Behavioral Analysis approach. Together with the flow-based and traffic-aware features, Autonomic Networking is considered as a pillar of this concept which leads in turn to specific requirements. This paper is an introduction to autonomic features which should be instantiated as per the Semantic Networking goals, within the traffic-aware data plane ("Semantic Analysis", "Elastic Fluid Switching"), the flow-based control plane ("Flow Admission Control", "Flow Policing", "Traffic Aware Routing"), and the self-management plane ("Network Mining", "Knowledge Plane"). We describe each of these functional building blocks, their interactions, the requirements for their autonomic (or self-*) features, and their localization in transport network nodes to transform them into "semantic network nodes"