scholarly journals Protocol-agnostic method for monitoring interactivity time in remote desktop services

Author(s):  
Jesus Arellano-Uson ◽  
Eduardo Magaña ◽  
Daniel Morató ◽  
Mikel Izal

AbstractThe growing trend of desktop virtualisation has facilitated the reduction of management costs associated with traditional systems and access to services from devices with different capabilities. However, desktop virtualisation requires controlling the interactivity provided by an infrastructure and the quality of experience perceived by users. This paper proposes a methodology for the quantification of interactivity based on the measurement of the time elapsed between user interactions and the associated responses. Measurement error is controlled using a novel mechanism for the detection of screen changes, which can lead to erroneous measurements. Finally, a campus virtual desktop infrastructure and the Amazon WorkSpaces solution are analysed using this proposed methodology. The results demonstrate the importance of the location of virtualisation infrastructure and the types of protocols used by remote desktop services.

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicholas Milef ◽  
Adam Ryason ◽  
Di Qi ◽  
Samuel O. Alfred ◽  
Cullen D. Jackson ◽  
...  

AbstractCollaborative virtual environments are being used in various applications ranging from online games to complex team training scenarios. The key to the success of such environments is the ability of the participants to form a shared mental model of the collaborative task being performed. Poor quality of service can deteriorate user performance and quality of experience, leading to a disruption of this mental model. While the effects of quality of service have been analyzed for traditional desktop environments, these effects remain unclear in collaborative virtual environments during user-to-user interactions. Here, we analyze the role of latency and packet bursts, two common problems in collaborative applications, on both simulator perception and actual task performance in a collaborative fire-fighting simulator. This exploratory study indicates that large latencies have a significant (p < 0.05) impact on the quality of experience, but not task performance. In contrast, packet bursts have a much larger impact on both the quality of experience and performance. Additionally, the network role, such as whether a user is a client or server, showed a significant (p < 0.05) impact on task performance in conditions impaired by packet bursts.


2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sajeeb Saha ◽  
Md. Ahsan Habib ◽  
Tamal Adhikary ◽  
Md. Abdur Razzaque ◽  
Md. Mustafizur Rahman ◽  
...  

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.


2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.


Author(s):  
Gosala Kulupana ◽  
Dumidu S. Talagala ◽  
Hemantha Kodikara Arachchi ◽  
Mobolaji Akinola ◽  
Anil Fernando

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