A service-oriented admission control strategy for class-based IP networks

The clear trend toward the integration of current and emerging applications and services in the Internet launches new demands on service deployment and management. Distributed service-oriented traffic control mechanisms, operating with minimum impact on network performance, assume a crucial role as regards controlling services quality and network resources transparently and efficiently. In this paper, we describe and specify a lightweight distributed admission control (AC) model based on per-class monitoring feedback for ensuring the quality of distinct service levels in multiclass and multidomain environments. The model design, covering explicit and implicit AC, exhibits relevant properties that allow managing quality of service (QoS) and service-level specifications (SLSs) in multiservice IP networks in a flexible and scalable manner. These properties, stemming from the way service-dependent AC and on-line service performance monitoring are proposed and articulated in the model’s architecture and operation, allow a self-adaptive service and resource management, while abstracting from network core complexity and heterogeneity. A proof of concept is provided to illustrate the AC criteria ability in satisfying multiple service class commitments efficiently. The obtained results show that the self-adaptive behavior inherent to on-line measurement-based service management, combined with the established AC rules, is effective in controlling each class QoS and SLS commitments consistently

A multiadaptive sampling technique for cost-effective network measurements

The deployment of efficient measurement solutions to assist network management tasks without interfering with normal network operation assumes a prominent role in today’s high-speed networks attending to the huge amounts of traffic involved. From a myriad of proposals for traffic measurement, sampling techniques are particularly relevant contributing effectively for this purpose as only a subset of the overall traffic volume is handled for processing, preserving ideally the correct estimation of network statistical behavior. In this context, this paper proposes MuST – a multiadaptive sampling technique based on linear prediction, aiming at reducing significantly the measurement overhead and still assuring that traffic samples reflect the statistical characteristics of the global network traffic under analysis. Conversely to current sampling techniques, MuST is a multi and self-adaptive technique as both the sample size and interval between samples are self-adjustable parameters according to the ongoing network activity and the accuracy of prediction achieved. The tests carried out demonstrate that the proposed sampling technique is able to achieve accurate network estimations with reduced overhead, using throughput as reference parameter. The evaluation results, obtained resorting to real traffic traces representing wired and wireless aggregated traffic scenarios and actual network services, prove that the simplicity, flexibility and self-adaptability of the proposed technique can be successfully explored to improve network measurements efficiency over distinct traffic conditions. For optimization purposes, this paper also includes a study of the impact of varying the order of prediction, i.e., of considering different degrees of past memory in the self-adaptive estimation mechanism. The significance of the obtained results is demonstrated through statistical benchmarking.Fundação para a Ciência e a Tecnologia (FCT

Handling Concurrent Admission Control in Multiservice Ip Networks

This paper debates the problem of handling concurrent admission control decisions in multiservice networks, putting forward solutions to mitigate the negative impact that distributed admission of flows might have on the service level guarantees provided to network customers. Keeping in mind that simplicity is a key factor for deployable solutions, we suggest and discuss the use of (i) a service-dependent concurrency index; (ii) a tokenbased system and (iii) a rate-based credit system, as alternative or complementary proposals to minimize or solve QoS degradation resulting from AC false acceptance

Measuring QoS in Class-based IP . . .

Multiclass IP networks open new dimensions and challenges on active monitoring as efficient strategies of in-band probing are required to sense each class performance without causing noticeable side-effects on real traffic. In our study, we provide new insights on how to perform efficiently active monitoring in these networks, suggesting the use of light and multipurpose probing streams able to capture simultaneously the behavior of multiple QoS metrics of each class. Considering oneway -delay, jitter and loss metrics, we explore different spatial-temporal characteristics of probing, focusing on finding patterns adjusted to each class measurement requirements. We demonstrate that commonly used probing streams fail to capture these metrics simultaneously and we propose novel colored probing patterns able to increase multipurpose active monitoring efficiency. As test environment, we consider a diffserv domain where admission control resorts to feedback from edge-to-edge active monitoring to dynamically control hard real-time, soft real-time and elastic traffic classes. Comparing graphically and statistically the probing and passive measurement outcome of each class, the obtained results show that despite being difficult to match the scale and shape of multiple metrics, a single and properly colored probing stream can capture close and simultaneously the behavior of one-way-delay, jitter and loss, for low in-band probing rates

A modular traffic sampling architecture: bringing versatility and efficiency to massive traffic analysis

The massive traffic volumes and heterogeneity of services in today's networks urge for flexible, yet simple measurement solutions to assist network management tasks, without impairing network performance. To turn treatable tasks requiring traffic analysis, sampling the traffic has become mandatory, triggering substantial research in the area. Despite that, there is still a lack of an encompassing solution able to support the flexible deployment of sampling techniques in production networks, adequate to diverse traffic scenarios and measurement activities. In this context, this article proposes a modular traffic sampling architecture able to foster the flexible design and deployment of efficient measurement strategies. The architecture is composed of three layers-management plane, control plane and data plane-covering key components to achieve versatile and lightweight measurements in diverse traffic scenarios and measurement activities. Each component of the architecture is described considering the different strategies, technologies and protocols that compose the several stages of a measurement process. Following the proposed architecture, a sampling framework prototype has been developed, providing a fair environment to assess and compare sampling techniques under distinct measurement scenarios, evaluating their performance in balancing computational burden and accuracy. The results have demonstrated the relevance and applicability of the proposed architecture, revealing that a modular and configurable approach to sampling is a step forward for improving sampling scope and efficiency.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio