Determination of bit-rate adaptation thresholds for the opus codec for VoIP services

In this paper, we present an experimental evaluation of the recently standardized Opus codec used in a VoIP context. Opus operates in both narrow and wideband modes, similar to Adaptive Multi-Rate (AMR). Through the use of the Wideband Perceptual Evaluation of Speech Quality (WB-PESQ) metric, we have conducted an extensive set of experiments using multiple audio samples encoded at different bit-rates, to investigate the impact of packet loss on resulting speech quality. Using these results, fitting functions for each bit-rate were computed to provide a straightforward manner of evaluating speech quality when given a specified packet loss rate. Using ns-2, a simulation analysis was conducted to evaluate the effect of background traffic on transmitted Opus streams. We observed that, when using different levels of background traffic, the observed packet loss rates varied heavily depending on the stream bit-rate. By correlating this information with the fitting functions derived previously, we were able to define switching thresholds. These are points where the speech quality of a lower bit-rate stream is greater than that of a higher bit-rate stream for the same levels of link bandwidth saturation

Automated WAIT for cloud-based application testing

Cloud computing is causing a paradigm shift in the provision and use of software. This has changed the way of obtaining, managing and delivering computing services and solutions. Similarly, it has brought new challenges to software testing. A particular area of concern is the performance of cloud- based applications. This is because the increased complex- ity of the applications has exposed new areas of potential failure points, complicating all performance-related activi- ties. This situation makes the performance testing of cloud applications very challenging. Similarly, the identi cation of performance issues and the diagnosis of their root causes are time-consuming and complex, usually require multiple tools and heavily rely on expertise. To simplify these tasks, hence increasing the productivity and reducing the depen- dency on human experts, this paper presents a lightweight approach to automate the usage of expert tools in the per- formance testing of cloud-based applications. In this paper, we use a tool named Whole-system Analysis of Idle Time to demonstrate how our research work solves this problem. The validation involved two experiments, which assessed the overhead of the approach and the time savings that it can bring to the analysis of performance issues. The results proved the bene ts of the approach by achieving a signif- icant decrease in the time invested in performance analysis while introducing a low overhead in the tested system

Arbres de diffusion pour sessions MVoIP avec flux hétérogènes

Réaliser des audio-conférences de qualité sur Internet est une tâche complexe. En effet, l'hétérogénéité des terminaux mobiles et la dynamique du réseau doivent être prises en compte par les systèmes MVoIP (Multiparty VoIP) afin d'assurer une qualité d'expérience suffisante aux utilisateurs. Dans cette contribution, nous présentons un nouveau système MVoIP tirant partie de la technologie SDN. Notre système effectue une distribution multipoint ainsi qu'une adaptation des débits des différents flux audio afin d'optimiser la qualité d'appel pour chaque participant. Notre proposition repose sur des arbres couvrant les participants dont la construction peut être réalisée selon deux stratégies distinctes : chemins minimisés ou plus courts chemins. Les simulations que nous avons menées montrent qu'un compromis existe entre la latence générée et la bande passante consommée par ces deux approches. Ainsi, lorsque le nombre de participants augmente, notre système utilise beaucoup moins de bande passante qu'un système MVoIP classique au prix d'une très légère augmentation de la latenc

Load balancing of Java applications by forecasting garbage collections

Modern computer applications, especially at enterprise-level, are commonly deployed with a big number of clustered instances to achieve a higher system performance, in which case single machine based solutions are less cost-effective. However, how to effectively manage these clustered applications has become a new challenge. A common approach is to deploy a front-end load balancer to optimise the workload distribution between each clustered application. Since then, many research efforts have been carried out to study effective load balancing algorithms which can control the workload based on various resource usages such as CPU and memory. The aim of this paper is to propose a new load balancing approach to improve the overall distributed system performance by avoiding potential performance impacts caused by Major Java Garbage Collection. The experimental results have shown that the proposed load balancing algorithm can achieve a significant higher throughput and lower response time compared to the round-robin approach. In addition, the proposed solution only has a small overhead introduced to the distributed system, where unused resources are available to enable other load balancing algorithms together to achieve a better system performance

Towards an automated approach to use expert systems in the performance testing of distributed systems

Performance testing in distributed environments is challenging. Specifically, the identification of performance issues and their root causes are time-consuming and complex tasks which heavily rely on expertise. To simplify these tasks, many researchers have been developing tools with built-in expertise. However limitations exist in these tools, such as managing huge volumes of distributed data, that prevent their e cient usage for performance testing of highly dis- tributed environments. To address these limitations, this paper presents an adaptive framework to automate the us- age of expert systems in performance testing. Our validation assessed the accuracy of the framework and the time savings that it brings to testers. The results proved the bene ts of the framework by achieving a significant decrease in the time invested in performance analysis and testing