Performance evaluation of multi-service UMTS core networks with clustering and neural modelling

The paper is devoted to neural modeling of link occupancy distribution. Multi-service (i.e., bandwidth sharing between different traffic classes) models of a single, possibly wireless transmission link for rigid, adaptive and elastic traffic are developed based on Markov reward models. Link occupancy distribution is introduced as embedded, discrete time Markov chain researched with vector quantization. Link occupancy performance is simulated as a combination of single queues with random distributions of arrival processes and holding times in service phases. Link occupancy probability density is determined using learning vector quantization in a two-layered neural structure. Simulation and numerical results are shown.

Optical networks are expected to provide a unified platform for a diverse set of emerging applications (three-dimensional TV, digital cinema, e-health, grid computing, etc). The service differentiation will be an essential feature of these networks. Considering the fact that users have different levels of patience for different network applications, referred to as set-up delay tolerance, it will be one of the key parameters for service differentiation. Service differentiation based on set-up delay tolerance will not only enable network users to select an appropriate service class (SC) in compliance with their requirements, but will also provide an opportunity to optimize the network resource provisioning by exploiting this information, resulting in an improvement in the overall performance. Improvement in network performance can be further enhanced by exploiting the connection holding-time awareness. However, when multiple classes of service with different set-up delay tolerances are competing for network resources, the connection requests belonging to SCs with higher set-up delay tolerance have better chances to grab the resources and leave less room for the others, resulting in degradation in the blocking performance of less patient customers. This study proposes different scheduling strategies for promoting the requests belonging to smaller set-up delay tolerance SCs, such as giving priority, reserving some fraction of available resources, and augmenting the research space by providing some extra paths. Extensive simulation results show that 1) priority in the rescheduling queue is not always sufficient for eradicating the degradation effect of high delay tolerant SCs on the provisioning rate of the most stringent SC, and 2) by utilizing the proposed strategies, resource efficiency and overall network blocking performance improve significantly in all SCs.

B-ISDN is expected to support diverse applications ranging from low bit rate communications between terminals and host computers to the broadcasting high resolution TV signals. One of the most promising approaches for the B-ISDN is the asynchronous transfer mode (ATM) where all information (such as voice, data, image and video) is divided into fixed length data format, called cells, and these cells are asynchronously transmitted through the network. ATM networks must provide multimedia services with diversified traffic flow characteristics and quality requirements, thus priorities are given to different classes of services. To achieve the requirement of each class, a suitable priority management scheme is needed. The authors propose a priority management scheme to achieve the delay requirement and the packet loss rate requirement of each class. The analytical process uses a Markov chain to analyse the average delay time and the packet loss rate of each class. A computing algorithm is proposed to generate the states of the Markov chain and the corresponding transition probability matrix automatically. The results indicate that the proposed priority management scheme provides a real-time service for the time constrained class, and the required buffer size in the switches can be greatly reduced when the control parameter is appropriately chosen.

A new traffic engineering manager for DiffServ/MPLS networks: design and implementation on an IP QoS Testbed

In multiservice networks, QoS monitoring needs to be carried out in a per-class basis so that each service class measuring requirements and behavior are met and sensed properly. Facing the shortage of off-the-shelf class-based monitoring solutions, this work is focused on the development of a flexibly QoS monitoring tool oriented to multiservice networks. In this context, after discussing main QoS monitoring issues, we propose a flexible QoS monitoring Java application, totally user parameterized and supporting service differentiation. Benefiting from an edge-to-edge design perspective, this service-oriented tool is able to make a periodic evaluation of relevant QoS metrics for each service class, on an intra-domain or end-to-end path basis. Monitoring results, stored in a MySQL database, are useful to drive both online and offline traffic engineering and service management tasks.

Network resources dimensioning and traffic engineering influence the quality in provisioned services required by the Expedited Forwarding (EF) traffic in production networks established through DiffServ over MPLS‐enabled network. By modeling EF traffic flows and the excess of network resources reserved for it, we derive the range of delay values which are required to support these flows at DiffServ nodes. This enables us to develop an end‐to‐end (e2e) delay budget‐partitioning mechanism and traffic‐engineering techniques within a framework for supporting new premium QoS levels, which are differentiated based on e2e delay, jitter and loss. This framework enables ingress routers to control EF traffic flow admission and select appropriate routing paths, with the goal of EF traffic balancing, avoiding traffic congestion and getting the most use out of the available network resources through traffic engineering. As a result, this framework should enable Internet service providers to provide three performance levels of EF service class to their customers provided that their network is DiffServ MPLS TE aware. Copyright © 2008 John Wiley & Sons, Ltd.

The paper proposes two IP packet aggregation techniques, called DAT (Deterministic Aggregation Technique) and WATT (Work-conserving Aggregation Technique with Timer), to adapt IP traffic to a multi-service optical slotted network. To perform aggregation, IP packets belonging to different classes of service (CoS) are polled according to the strict priority (SP) or to an hybrid version of the probabilistic priority (PP) scheduling discipline originally proposed in [1, 2]. An approximate analytical model is given in the case of DAT under the SP discipline. In addition, extensive simulations are used to study the impact of self-similar traffic on the aggregation processes. Finally, performance comparisons between the aggregation techniques and the standard approach (where no aggregation is performed) are carried out in the context of a slotted dual bus optical ring network (SDBORN) which is a candidate viable solution for metropolitan area networks (MAN).

There is no network traffic classification in intermediate nodes in IEEE802.11e multi-hop wireless network, which results in delays for the traffic with higher levels of service. Also, there is no mechanism to support fairness in a shared environment with different classes of service, which leads to inappropriate quality of service. Previous studies were focused on improving network throughput and also the fairness among the traffic that belongs to one class of service and there is no solution for providing fairness among different classes of service in each individual node. In this paper, a new method is proposed for scheduling the traffic in each intermediate node which is based on Transmission Opportunity (TXOP). The new method ensures differentiated quality of service for service classes as well as improvement of throughput and end to end delay. Using dynamic TXOP along with the proposed method can provide desired quality of service as well as fairness in the bandwidth shared between different classes of services. Simulation results illustrate network throughput and end to end delay improvement for each class of service, in the case of using the proposed method, as well as fairness in using the shared bandwidth which leads to more efficient use of network resources.

This work presents a novel resource allocation scheme for a multi-service (e.g., voice, video and data) wireless ad-hoc networks. The technique is established via clustered multicarrier code division multiple access (MC-CDMA) system. In clustered MC-CDMA, the available bandwidth is divided into the clusters of carriers, their MC-CDMA is applied within each cluster and frequency division multiple access (FDMA) is applied across clusters. Here, a master node allocates carrier clusters and their associated power to the nodes in order to minimize the interference temperature, with constraints on bit-error-rate and transmission rates for different classes of services. Sub-optimum algorithms are proposed for this constrained optimization problem and their impact on interference temperature in multi-service wireless networks is investigated. Simulation results confirm the potential of the proposed techniques to improve the interference temperature, which is mainly due to the flexibility in channel allocation via clustered MC-CDMA.

In this paper we study the performance of IEEE 802.11e enhanced distributed channel access (EDCA) priority schemes under finite load and error-prone channel. We introduce a multi-dimensional Markov Chain model that includes all the mandatory differentiation mechanisms of the standard: QoS parameters, CW <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MIN</sub> , CW <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAX</sub> arbitration inter-frame space (AIFS), and the virtual collision handler. The model faithfully represents the functionality of the EDCA access mechanisms, including lesser known details of the standard such as the management of the backoff counter which is technically different from the one used in the legacy DCF. We study the priority schemes under both finite load and saturation conditions. Our analysis also takes into consideration channel conditions.

ANFIS networks are neural models particularly suited to the solution of time series forecasting problems, which can be considered as function approximation problems whose inputs are determined by using past samples of the sequence to be predicted. In this context, clustering procedures represent a straightforward approach to the synthesis of ANFIS networks. The use of a clustering procedure, working in the conjunct input-output space of data, is proposed in the paper. Simulation tests and comparisons with other prediction techniques are discussed for validating the proposed synthesis approach. In particular, we consider the prediction of environmental data sequences, which are often characterized by a chaotic behavior. Consequently, well-known embedding techniques are used for solving the forecasting problems by means of ANFIS networks.

In traditional cloud computing research, it is often considered that the network resources between edge devices and cloud platform are sufficient, and the symmetry between the upward link from edge devices to the cloud platform and the downward link from cloud platform to edge devices is definite. However, in many application scenarios, the network resources between edge devices and cloud platform might be limited, and the link symmetry may not be guaranteed. To solve this problem, network relay nodes are introduced to realize the data transmission between edge devices and the cloud platform. The environment in which network relay nodes that can cooperate with the cloud platform is called cloud network collaborative environment (CNCE). In CNCE, how to optimize data transmission from edge devices to cloud platform through relay nodes has become one of the most important research topics. In this paper, we focus on the following two influencing factors that previous studies ignored: (1) the multi-link and multi-constraint transmission process; and (2) the timely resource state of the relay node. Taking these factors into consideration, we design a novel data transmission scheduling algorithm, called ant colony based transmission scheduling approach (ACTS). First, we propose a multi-link optimization mechanism to optimize the constraint limits. This mechanism divides the transmission into two links called the downlink relay link and uplink relay link. For the downlink relay link, we use the store-and-forward method for the optimization. For the uplink relay link, we use the min–min method for the optimization. We use the ant colony algorithm for the overall optimization of the two links. Finally, we improve the pheromone update rule of the ant colony algorithm to avoid the algorithm from falling into a local optimum. Extensive experiments demonstrate that our proposed approach has better results in transmission efficiency than other advanced algorithms.

The article is devoted to finding the optimal network resources distribution in the network represented by an undirected complete connected planar uplifted graph. It is assumed that the weight of the edge of the graph is determined by the minimum bandwidth(traffic) of the router at its nodes. The functional is the sum of the bandwidth (maximal flow) of the routers on all nodes under the restrictions on their total cost.The complexity of the problem lies in the fact that there are two types of uncertainty in it: the first - the type of router in each network node is not initially determined, the second - the path between the nodes on which the maximum traffic is realized is not defined. The first uncertainty is solved by using the Genetic Algorithm and the second uncertainty is solved by using the Ford Fulkerson algorithm. An example of calculation for a fragment of a network of a real Internet Service Provider (ISP) is presented in this article..

Performance-analysis of a switching system using priority-based dynamic capacity allocation is presented. The system provides connectivity between a number of point-to-multipoint communication access clusters. The scheduling of traffic transmission at each access cluster is based on a priority scheme with priority given to real-time (rt) traffic over nonreal-time (nrt) traffic. An analytical model is developed to evaluate the covariance functions of both rt and nrt traffic arriving at the output ports. Aggregate traffic arrival streams are approximated to 2-state Markov-modulated Poisson processes (MMPPs) by matching their statistical characteristics. Analytical and simulation results on performance of an example 4 /spl times/ 4 switch for different traffic loads are discussed.

The most sustainable source of energy with unlimited reserves is the solar energy, which is the main source of all types of energy on earth. Accurate knowledge of solar radiation is considered to be the first step in solar energy availability assessment. It is also the primary input for various solar energy applications. The unavailability of the solar radiation measurements for several sites around the world leads to proposing different models for predicting the global solar radiation. Artificial neural network technique is considered to be an effective tool for modelling nonlinear systems and requires fewer input parameters. This work aims to investigate the performance of artificial neural network-based models in estimating global solar radiation. To achieve this goal, measured data set of global solar radiation for the case study location (Lat. 30˚ 51 ̀ N and long. 29˚ 34 ̀ E) are utilized for model establishment and validation. Mostly, common statistical indicators are employed for evaluating the performance of these models and recognizing the best model. The obtained results show that the artificial neural network models demonstrate promising performance in the prediction of global solar radiation. In addition, the proposed models provide superior consistency between the measured and estimated values.