Abstract-ITU-T recommendation G.107 introduced the Emodel, a repeatable way to assess if a network is prepared to carry a VoIP call or not. Various studies show that the Emodel is complex with many factors to be used in monitoring purposes. Consequently, simplified versions of the E-model have been proposed to simplify the calculations and focus on the most important factors required for monitoring the call quality. In this paper, we propose simple correction to a simplified E-model; we show how to calculate the correction coefficients for 4 common codecs (G.711, G.723.1, G.726 and G.729A) and then we show that its predictions better match PESQ scores by implementing it in a monitoring application.

As Small Medium Enterprises (SMEs) adopt Cloud technologies to provide high value customer offerings, uptime is considered important. Cloud outages represent a challenge to SMEs and micro teams to maintain a services platform. If a Cloud platform suffers from downtime this can have a negative effect on business revenue. Additionally, outages can divert resources from product development/delivery tasks to reactive remediation. These challenges are immediate for SMEs or micro teams with a small levels of resources. In this paper we present a framework that can model the arrival of Cloud outage events. This framework can be used by DevOps teams to manage their scarce pool of resources to resolve outages, thereby minimising impact to service delivery. We analysed over 300 Cloud outage events from an enterprise data set. We modelled the inter-arrival and service times of each outage event and found a Pareto and a lognormal distribution to be a suitable fit. We used this result to produce a special case of the G/G/1 queue system to predict busy times of DevOps personnel. We also investigated dependence between overlapping outage events. Our predictive queuing model compared favourably with observed data, 72% precision was achieved using one million simulations.

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We propose a generalised testing framework to evaluate the end-user perceptual quality of video over IP solutions in enterprise networks. The framework automates testing of communications applications to produce Quality-of-Experience (QoE) results in terms of industry defined standard metrics like PSNR, SSIM and video MOS. Furthermore, it aids in network planning by relating performance indices to network characteristics. Experiments are carried out in an emulated network environment to realise and test the application under varied network impairments. We describe a sample realization of framework using a suite of open source tools and application client extensions. We present a comparative analysis involving performance of two different popular enterprise VVoIP applications which explains the usage of framework as a quality verification and analysis tool. We also present a brief accuracy analysis of Opinion Model using Video Quality Metric (VQM).Index Terms-Video over IP, VQM, call quality, testing framework, Quality of Experience I. INTRODUCTIONDue to vast deployment of broadband networks, video streaming services over packet switched networks have become popular. Enterprise have embraced video telephony due to its flexible, integrated IP-based deployment and requiring low cost of maintenance. However, popularity of this service depends on the quality as perceived by the end user. Thus, understanding the perceptual quality issues is important for persistence of video phone service and its revenue model. Typically, the Quality-of-Experience (QoE) as perceived by application end users is estimated using the Mean Opinion Score (MOS) metric. Whilst MOS is by its nature highly subjective, being derived from subjective studies involving real users there has been considerable research on the mapping of objective network metrics like delay and jitter to estimated MOS values.Being an all IP based service quality of video over IP is highly dependent on network fluctuations and parameters like bandwidth, end-to-end delay, jitter and packet loss ratio. Apart from these fixed network issues, other application level parameters like codec, codec implementation parameters for a given network and service, non-optimized jitter buffer also inevitably collaborate to degrade the desired end-user video quality. High susceptibility to fluctuations in network and application level parameter makes quality of video over IP more vulnerable to degradation.