Assessing readiness of IP networks to support desktop videoconferencing using OPNET

OPNET is a powerful network design and simulation tool that has gained popularity in industry and academia. However, there exists no known simulation approach on how to deploy a popular real-time network service such as videoconferencing. This paper demonstrates how OPNET can be leveraged to assess the readiness of existing IP networks to support desktop videoconference. To date, OPNET does not have built-in features to support videoconferencing or its deployment. The paper offers remarkable details on how to model and configure OPNET for such a purpose. The paper considers two types of video traffic (viz. fixed and empirical video packet sizes). Empirical video packet sizes are collected from well-known Internet traffic traces. The paper presents in-depth analysis and interpretation of simulation results and shows how to draw proper engineering conclusions.     

Human-Centric Composite-Quality Modeling and Assessment for Virtual Desktop Clouds

There are several motivations, such as mobility, cost, and security, that are behind the trend of traditional desktop users transitioning to thin-client-based virtual desktop clouds (VDCs). Such a trend has led to the rising importance of human-centric performance modeling and assessment within user communities that are increasingly making use of desktop virtualization. In this paper, we present a novel reference architecture and its easily deployable implementation for modeling and assessing objective user quality of experience (QoE) in VDCs. This architecture eliminates the need for expensive, time-consuming subjective testing and incorporates finite-state machine representations for user workload generation. It also incorporates slow-motion benchmarking with deep-packet inspection of application task performance affected by QoS variations. In this way, a "composite-quality" metric model of user QoE can be derived. We show how this metric can be customized to a particular user group profile with different application sets and can be used to a) identify dominant performance indicators and troubleshoot bottlenecks and b) obtain both absolute and relative objective user QoE measurements needed for pertinent selection of thin-client encoding configurations in VDCs. We validate our composite-quality modeling and assessment methodology by using subjective and objective user QoE measurements in a real-world VDC called VDPilot, which uses RDP and PCoIP thin-client protocols. In our case study, actual users are present in virtual classrooms within a regional federated university system.     

Ontology-Based Semantic Priority Scheduling for Multi-domain Active Measurements

Network control and management techniques (e.g., dynamic path switching and on-demand bandwidth provisioning) rely on active measurements of the end-to-end network status. The measurements are needed to meet network monitoring objectives such as network weather forecasting, anomaly detection, and fault-diagnosis. Recent widespread deployment of openly accessible multi-domain active measurement frameworks, such as perfSONAR, has resulted in users competing for system and network measurement resources. Hence, there is a need to prioritize measurement requests of users before they are scheduled on measurement resources. In this paper, we present a novel ontology-based semantic priority scheduling algorithm (SPS) that handles resource contention while servicing measurement requests for meeting network monitoring objectives. We adopt ontologies to formalize semantic definitions and develop an inference engine to dynamically prioritize measurement requests. The prioritization is based upon user roles, user sampling preferences, resource policies, and oversampling mitigation factors. Performance evaluation results demonstrate that our SPS algorithm outperforms existing deterministic and heuristic algorithms in terms of user ‘satisfaction ratio’ and ‘average stretch’ among serviced measurement requests. Further, by sampling experiments on real-network perfSONAR measurement data sets, we show that our SPS algorithm successfully mitigates oversampling and further improves the satisfaction ratio. Our SPS scheme and evaluation results are vital to manage large-scale measurement infrastructures used for meeting monitoring objectives in the next-generation applications and networks.     

vSocial: a cloud-based system for social virtual reality learning environment applications in special education

Virtual Learning Environments (VLEs) are spaces designed to educate student groups remotely via online platforms. Although traditional VLEs have shown promise in educating students, they offer limited immersion that overall diminishes learning effectiveness. In this paper, we describe vSocial, a cloud-based virtual reality learning environment (VRLE) system that can be deployed over high-speed networks using the High Fidelity “social VR” platform. vSocial provides flexible control of group learning content and compliance with established VLE standards with improved immersive user experience for both instructor(s) and students. For our vSocial development, we build upon the use case of an existing special education VLE viz., iSocial that trains youth with Autism Spectrum Disorder by implementing the Social Competence Intervention (SCI) curriculum. The vSocial can be used to: (a) implement multiple learning modules using wearable VR technologies, (b) integrate cognitive state sensing devices, and (c) organize learning session data securely using web applications hosted on cloud resources. Our experiment results show that the VR mode of content delivery in vSocial better stimulates the generalization of lessons to the real world than non-VR lessons, and provides improved immersion when compared to an equivalent desktop version. Further, usability study results show that users can successfully use the web application features in vSocial for group learning activities with ease-of-use and consistency.

     

Topology-Aware Correlated Network Anomaly Event Detection and Diagnosis

For purposes such as end-to-end monitoring, capacity planning, and performance bottleneck troubleshooting across multi-domain networks, there is an increasing trend to deploy interoperable measurement frameworks such as perfSONAR. These deployments expose vast data archives of current and historic measurements, which can be queried using web services. Analysis of these measurements using effective schemes to detect and diagnose anomaly events is vital since it allows for verifying if network behavior meets expectations. In addition, it allows for proactive notification of bottlenecks that may be affecting a large number of users. In this paper, we describe our novel topology-aware scheme that can be integrated into perfSONAR deployments for detection and diagnosis of network-wide correlated anomaly events. Our scheme involves spatial and temporal analyses on combined topology and uncorrelated anomaly events information for detection of correlated anomaly events. Subsequently, a set of ‘filters’ are applied on the detected events to prioritize them based on potential severity, and to drill-down upon the events “nature” (e.g., event burstiness) and “root-location(s)” (e.g., edge or core location affinity). To validate our scheme, we use traceroute information and one-way delay measurements collected over 3 months between the various U.S. Department of Energy national lab network locations, published via perfSONAR web services. Further, using real-world case studies, we show how our scheme can provide helpful insights for detection, visualization and diagnosis of correlated network anomaly events, and can ultimately save time, effort, and costs spent on network management.