End-to-end QoS with Budget-Based Management

Computer Networks, 2004

Network service providers purchase large end-to-end connections from network owners, then offer individual users network access at a price. Appropriately provisioning (purchasing) and allocating (pricing) connections remains a difficult problem due to increasing demands and network dynamics. However, connection management is more complex with the deployment of end-to-end Quality of Service (QoS). This paper describes a scalable connection management strategy for QoS enabled networks. The management technique maximizes profit, while maintaining a low blocking probability for each connection. Important issues regarding demand estimation, connection duration, and pricing intervals, are addressed and analyzed. Analytical results are then validated using simulation under realistic conditions.

2004

The traditional approaches to optimal design and planning of packet networks focus on the network-layer infrastructure, thus neglecting end-to-end QoS (Quality of Service) issues, and SLA (Service Level Agreement) guarantees. This is quite inappropriate, since the Internet today carries a wide range of critical telecommunication services, and design approaches based on end-to-end QoS are a must.

Telecommunication Systems, 2006

This paper addresses the problem of resource fragmentation (RF) in QoS controlled networks. Resources are said to be fragmented when they are available in non-contiguous blocks and hence cannot be utilized by incoming calls with high resource demands. This paper shows the effect of resource fragmentation on QoS controlled networks and presents the Dynamic Resource Redistribution (DRR) algorithm to counteract RF. The DRR algorithm reduces the effects of RF by attempting to redistribute resources in different paths to make resources to incoming calls. A variety of simulation experiments were conducted to study the performance of the DRR algorithm on different network topologies with varying traffic characteristics. The DRR algorithm, when used, increased the number of calls accommodated in the network as well as the overall resource allocation in the network.

2007

Efficient dynamic resource provisioning algorithms are necessary to the development and automation of Quality of Service (QoS) networks. The main goal of these algorithms is to offer services that satisfy the QoS requirements of individual users while guaranteeing at the same time an efficient utilization of network resources.

2008

Abstract Efficient dynamic resource provisioning algorithms are necessary to the development and automation of Quality of Service (QoS) networks. The main goal of these algorithms is to offer services that satisfy the QoS requirements of individual users while guaranteeing at the same time an efficient utilization of network resources.

Lecture Notes in Computer Science, 2006

Efficient dynamic resource provisioning algorithms are necessary to the development and automation of Quality of Service (QoS) networks. The main goal of these algorithms is to offer services that satisfy the QoS requirements of individual users while guaranteeing at the same time an efficient utilization of network resources. In this paper we introduce a new service model that provides quantitative per-flow bandwidth guarantees, where users subscribe for a guaranteed rate; moreover, the network periodically individuates unused bandwidth and proposes short-term contracts where extra-bandwidth is allocated and guaranteed exclusively to users who can exploit it to transmit at a rate higher than their subscribed rate. To implement this service model we propose a dynamic provisioning architecture for intra-domain Quality of Service networks. We develop an efficient bandwidth allocation algorithm that takes explicitly into account traffic statistics to increase the users' benefit and the network revenue simultaneously. We demonstrate through simulation in realistic network scenarios that the proposed dynamic provisioning model is superior to static provisioning in providing resource allocation both in terms of total accepted load and network revenue.

GLOBECOM-NEW YORK-, 2001

… Journal Of Social …, 2010

Firms acquire network capacity from multiple suppliers which offer different Quality of Service (QoS) levels. After acquisition, day-to-day operations such as video conferencing, voice over IP and data applications are allocated between these acquired capacities by considering QoS requirement of each operation. In optimal allocation scheme, it is generally assumed each operation has to be placed into resource that provides equal or higher QoS Level. Conversely, in this study it is showed that former allocation strategy may lead to suboptimal solutions depending upon penalty cost policy to charge degradation in QoS requirements. We model a cost minimization problem which includes three cost components namely capacity acquisition, opportunity and penalty due to loss in QoS.

2011

Abstract Modern data-intensive applications move vast amounts of data between multiple locations around the world. To enable predictable and reliable data transfers, next generation networks allow such applications to reserve network resources for exclusive use. In this paper, we solve an important problem (called SMR 3) to accommodate multiple and concurrent network reservation requests between a pair of end sites.

IEEE Journal on Selected Areas in Communications, 2005

We present a general framework for the problem of quality-of-service (QoS) routing with resource allocation for data networks. The framework represents the QoS parameters as functions rather than static metrics. The formulation incorporates the hardware/software implementation and its relation to the allocated resources into a single framework. The proposed formulation allows intelligent adaptation of QoS parameters and allocated resources during a path search, rather than decoupling the path search process from resource allocation. We present a dynamic programming algorithm that, under certain conditions, finds an optimal path between a source and destination node and computes the amount of resources needed at each node so that the end-to-end QoS requirements are satisfied. We present jitter and data droppage analyzes of various rate-based service disciplines and use the dynamic programming algorithm to solve the problem of QoS routing with resource allocation for networks that employ these service disciplines.