Junaid Shaikh

Quality of Experience from user and network perspectives

The impact of network performance on user experience is important to know, as it determines the success or failure of a service. Unfortunately, it is very difficult to assess it in real-time on an operational network. Monitoring of network-level performance criteria is easier and more usual. But the problem is then to correlate these network-level Quality of Service (QoS) to the Quality of Experience (QoE) perceived by the users. Efforts have been done in the previous years to map user behaviour to traffic characteristics on the network to QoS. However, being able to successfully relate these traffic characteristics to user satisfaction is not a simple task and still requires further investigations. In this work, we try to associate on one side the correlations between various traffic characteristics measured on an operational network and on the other side the user experience tested on an experimental platform. Our aim is to observe some pronounced trends regarding relationships between both types of results. More precisely, we want to validate how and to what extent the volumes of user sessions represent the level of user satisfaction. Along this way, we need to revise classical relationships between some of the network performance indicators such as loss, download time and throughput in order to strengthen the understanding of this impact on each other and on user satisfaction. This preliminary study is based on the application web.

On user perception of web login — A study on QoE in the context of security

As there is a lack of such studies, this paper investigates user perception (Quality of Experience, QoE) of the response times (Quality of Service, QoS) of a web authentication procedure, in particular a login to a community web page. Comparing the results to well-known user perception of web performance, we show that the users perceive logins in a similar way as standard web pages, which means that similar limits on user patience apply. The derived QoE-QoS relationship, an exponential function, serves then as the basis for assessing the performance of authentication algorithms in the domain of user acceptability.

Evaluation of delay performance of traffic shapers

In network emulation, traffic shapers are used to shape the performance of the network. They are provided with certain inputs in a test environment to vary the network performance accordingly in order to investigate the effects of different network conditions on applications in real yet emulated scenarios. However, it is very important for the shapers to work as supposed in order to successfully realize the desired network conditions. They may make the results of network emulations unrealistic and unreliable if their functioning is not according to the desired specification. In this work, we evaluate the delay shaping of three traffic shapers, NIST Net, Netem and KauNet through the results obtained from a number of experiments. A comparison of the output of their delay shaping is presented. This comparison can enable us to select the most suitable shaper based on the required shaping. Effects of hardware platforms on the shaping are also filtered out by performing the experiments with shapers installed on Advance Micro Devices (AMD) and Intel platforms separately. Different Protocol Data Unit (PDU) sizes are used in the experiments to test the influence of packet sizes on the shaping. These delay evaluation results are then complemented by the Coefficient of Throughput Variation (CoTV) results.

In small chunks or all at once? User preferences of network delays in web browsing sessions

The time-critical tasks on the Internet are increasing. The delays in these tasks can have severe implications on the Quality of Experience (QoE) of a service. Therefore, networks require smart user-centric resource management mechanisms to reduce the impact of these delays on QoE. For this, a better understanding of the user preferences with regards to service performance is a prerequisite. In this paper, we present user responses to the three different distributions of delays, occurring during shopping sessions on the Web. By keeping the overall waiting time of the sessions same, we show how the users respond differently to the different set of delays. We analyzed the user responses and found that, the users prefer small frequently occurring delays as compared to the long rarely occurring delays within a task-based session.

Inferring User-Perceived Performance of Network by Monitoring TCP Interruptions

The fluctuating performance of wireless and mobile networks has triggered the need for smart algorithms to assess the user perception, resulting from the quality of network services. While efforts have been done to model the user experience resulting from the network performance, there is still the need for practical methods to assess the user-perceived performance, in the real environment. In this work, we present a set of criteria to observe the user behavior on the Web, passively from the network-level. The criteria are based on the monitoring of TCP control flags and HTTP requests. Thus, information about user actions performed in the web browser can be inferred by monitoring the TCP termination flags and by keeping track of the HTTP requests. Along the way, we also present some anomalies observed in the TCP connection termination process, which may result in performance degradation of Web transfers.

Evaluation of throughput performance of traffic shapers

Traffic shapers are used by researchers to emulate the behavior of networks and applications in test environments, typically with user-defined traffic shaping parameters such as throughput and loss. Also, traffic shapers are used for the enforcement of SLA, so they are of interest for Internet Service Providers. However, output given by traffic shapers may not be as accurate as desired. Therefore, it is important to assess the accuracy of the implementation of traffic shapers. In this paper, we evaluate two traffic shapers with regard to the performance of their throughput shaping. For this evaluation, traces were collected. The properties of the resulting throughput at the outlet of the shaper are compared to the properties of the throughput at the inlet in combination with the preset shaper parameters. In this sense, we also compare shapers installed on Advance Micro Devices (AMD) and Intel platforms, and we use different PDU sizes and load levels to test the influence of those parameters on the shaping. We are furthermore able to deduct internal shaper parameters such as packet rate and buffer size from our measurements, and we analyse the statistical properties of the packet departure process. The extensive measurement results in this paper allow for a detailed assessment of the question whether the shaper performance is up to mark for a desired timescale. In general, the performance of both shapers and hardware platforms can be considered satisfactory on the investigated time scales between 1 ms and 1 s, with a slight advantage for NetEm on AMD.

Back to normal? Impact of temporally increasing network disturbances on QoE

Brief episodes of network faults and performance issues adversely affect the user Quality of Experience (QoE). Besides damaging the current opinions of users, these events may also shape users future perception of the service. Therefore, it is important to quantify the impact of such events on QoE over time. In this paper, we present our findings on the temporal aspects of user feedback to disturbances on networks. These findings are based on subjective user tests performed in the context of web browsing on an e-commerce website. The results of this study suggest that the QoE drops significantly every time the page load time grows. The after-effects of network disturbances on user QoE remain visible even when the network problems are over, i.e., users do not immediately return to the same level of opinion scores as compared to the corresponding pre-disturbance phase. They tend to remember their recent experiences. Our results also show that there are four segments of users that exist with regards to their feedback to page load times. Network operators may customize their services according to each segment of users to raise the overall QoE. Finally, we show that the exponential relationship provides best fits of QoE and page load times for all segments of users.

Classification of TCP connection termination behaviors for mobile Web

Many emerging smart applications and services employ Web technology, and users nowadays surf the Web from any device via any kind of access network. Typically, high page latencies trigger users to abort ongoing transfers, resulting in the abrupt terminations of the TCP connections. This paper presents a systematic study of the termination process of the TCP connections and identifies the reasons behind the observed sequences of termination flags. Monitoring and classification of the termination behavior of the TCP connections can provide indications about the user-perceived performance of Web transfers. From the results, it is observed that the TCP termination behavior is heavily-dependent on the client-side application. Therefore, a set of criteria is required to identify the abortions made by the user.

Exponential On-Off Traffic Models for Quality of Experience and Quality of Service Assessment

Mobile connectivity typically exhibits on-off behavior, i.e. phases of undisturbed data transmission are interrupted by outages. Recent matching efforts have shown that the durations of the onand off-phases can be matched by exponential distributions. The resulting exponential on-off models allow for elegant close-form solutions for performance metrics such as freeze probabilities in face of buffering, which amongst others allows for analysis-based interpretationsof the impacts of various keyparameters. Centering around exponential on-off behavior of mobile channels, this work provides a bridge between traffic measurements in mobile environments, closed-form traffic analysis based on Markov-modulated fluid flow models, and user perception of those kinds of disturbances that are typical for mobile environments. It also shows the need to focusQuality of Experience (QoE) studies on impact factors close to the source of the performance degradation, rather than on generic Quality of Service (QoS) parameters onpacket level.