THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Keynote David Rose The New Vanguard for Business: Connectivity, Design, and the Internet of Things The Internet of Things is the hottest topic of the moment a shift predicted to be as momentous as the impact of the internet itself. The internet has allowed us to share ideas and data largely input by humans. What about a world where data from objects as diverse as umbrellas, fridges, and gas tanks all flows through the internet?

The Internet of things.

End user quality perception in the context of Internet applications is often characterized by waiting times before service consumption as well as interruptions during service consumption. In particular in case of bad network conditions, network and service providers have to trade off between these two impairment types, i.e. between the devil and the deep blue sea. In this paper we investigate this tradeoff in order to guide the design and development of Internet applications and network management approaches. The contribution of this paper is twofold. Firstly, we quantify the impact of initial delays on the user perceived Quality of Experience (QoE) for different application scenarios by means of subjective laboratory and crowdsourcing studies. We show that QoE for a given waiting time strongly depends on the concrete application at hand but that rating diversity remains fairly application-invariant. Secondly, using the example of YouTube video streaming we compare the influence of initial delays and interruptions (stallings) during watching. Our results demonstrate that users are extremely sensitive to interruptions and that services should be designed accordingly e.g. by increasing initial delay for prebuffering to overcome lack of resources.

Initial delay vs. interruptions: Between the devil and the deep blue sea

Cloud computing is currently gaining enormous momentum due to a number of promised benefits: ease of use in terms of deployment, administration, and maintenance, along with high scalability and flexibility to create new services. However, as more personal and business applications migrate to the cloud, service quality will become an important differentiator between providers. In particular, quality of experience as perceived by users has the potential to become the guiding paradigm for managing quality in the cloud. In this article, we discuss technical challenges emerging from shifting services to the cloud, as well as how this shift impacts QoE and QoE management. Thereby, a particular focus is on multimedia cloud applications. Together with a novel QoE-based classification scheme of cloud applications, these challenges drive the research agenda on QoE management for cloud applications.

/pdf/challenges-of-qoe-management-for-cloud-applications-397l4o6god.pdf

Challenges of QoE management for cloud applications

With the move towards converged all-IP wireless network environments, managing end-user Quality of Experience (QoE) poses a challenging task, aimed at meeting high user expectations and requirements regarding reliable and cost-effective communication, access to any service, anytime and anywhere, and across multiple operator domains. In this paper, we give a survey of state-of-the-art research activities addressing the field of QoE management, focusing in particular on the domain of wireless networks and addressing three management aspects: QoE modeling, monitoring and measurement, and adaptation and optimization. Furthermore, we identify and discuss the key aspects and challenges that need to be considered when conducting research in this area.

/pdf/survey-and-challenges-of-qoe-management-issues-in-wireless-5gfqoab0k4.pdf

Survey and Challenges of QoE Management Issues in Wireless Networks

The paper is devoted to modeling wireless mesh networks (WMN) through mixed-integer programming (MIP) formulations that allow to precisely characterize the link data rate capacity and transmission scheduling using the notion of time slots. Such MIP models are formulated for several cases of the modulation and coding schemes (MCS) assignment. We present a general way of solving the max-min fairness (MMF) trac objective for WMN using the formulated capacity models. Thus the paper combines WMN radio link modeling with a non-standard way of dealing with uncertain trac, a combination that has not, to our knowledge, been treated so far by exact optimization models. We discuss several ways, including a method based on the so called compatible or independent sets, of solving the arising MIP problems. We also present an extensive numerical study that illustrates the running time eciency of dierent solution approaches, and the inuence of the MCS selection options and the number of time slots on trac performance of a WMN. Exact joint optimization modeling of the WMN capacity and the MMF trac objectives forms the main contribution of the paper.

/pdf/on-max-min-fair-ow-optimization-in-wireless-mesh-networks-2197bdnjzj.pdf

On max-min fair ow optimization in wireless mesh networks

The paper is devoted to modeling wireless mesh networks (WMN) through mixed-integer programming (MIP) formulations that allow to precisely characterize the link data rate capacity and transmission scheduling using the notion of time slots. Such MIP models are formulated for several cases of the modulation and coding schemes (MCS) assignment. We present a general way of solving the max-min fairness (MMF) traffic objective for WMN using the formulated capacity models. Thus the paper combines WMN radio link modeling with a non-standard way of dealing with uncertain traffic, a combination that has not, to our knowledge, been treated so far by exact optimization models. We discuss several ways, including a method based on the so called compatible or independent sets, of solving the arising MIP problems. We also present an extensive numerical study that illustrates the running time efficiency of different solution approaches, and the influence of the MCS selection options and the number of time slots on traffic performance of a WMN. Exact joint optimization modeling of the WMN capacity and the MMF traffic objectives forms the main contribution of the paper.

/pdf/on-max-min-fair-flow-optimization-in-wireless-mesh-networks-wtyfenbgnq.pdf

On max-min fair flow optimization in wireless mesh networks

YouTube is the most important online platform for streaming video clips. The popularity and the continuously increasing number of users pose new challenges for Internet service providers. In particular, in access networks where the transmission resources are limited and the providers are interested in reducing their operational expenditure, it is worth to efficiently optimise the network for popular services such as YouTube. In this paper, we propose different resource management mechanisms to improve the quality of experience (QoE) of YouTube users. In particular, we investigate the benefit of cross-layer resource management actions at the client and in the access network for YouTube video streaming. The proposed algorithms are evaluated in a wireless mesh testbed. The results show how to improve the YouTube QoE for the users with the help of client-based or network-based control actions. Copyright © 2013 John Wiley & Sons, Ltd.

Using buffered playtime for QoE‐oriented resource management of YouTube video streaming

Internet access networks in general and wireless mesh access networks in particular, are the bottleneck of today's communication networks and consequently most strongly responsible for determining the user satisfaction. The limited bandwidth and the fact that access network are most often used as mere bit pipes are however unfavorable for the users' quality of experience (QoE). The lack of application-specific service guarantees is especially inadequate in the face of an increasing degree of heterogeneity of Internet applications and their individual service requirements. Application and quality of experience resource management, Aquarema for short, addresses this challenge by enabling application specific network resource management and thereby improves the user QoE. This is achieved by the interaction of application comfort (AC) monitoring tools, running at the clients, and a network advisor which may trigger different resource management tools. AC quantifies how well an application is running and in particular, enables a prediction of the user experience, thereby allowing the network advisor to act upon an imminent QoE degradation. We demonstrate the appeal of Aquarema at the example of YouTube video streaming in a congested IEEE 802.11 based mesh testbed where AC-aware traffic shaping guarantees the successful video playback.

Aquarema in action: Improving the YouTube QoE in wireless mesh networks

Quality of Experience (QoE) is a measure for the satisfaction of a user with a given service. Despite the popularity of the term, there is a lack of understanding of how to capture or quantify QoE. In this paper we therefore take a step forward in this direction using the example of thin client architectures, where the client is only used as a dumb terminal while the applications run on a central server. The satisfaction of a thin client user is thus heavily influenced by the quality of the network connection between the client and the server. We study this influence by varying different network parameters like packet loss, jitter, and delay in a controlled testbed environment. The service quality is assessed using objective measurements on application level (oQoE) and verified by means of a survey among test persons capturing subjective user satisfactions (sQoE).

Barbara Staehle

Papers

On max-min fair ow optimization in wireless mesh networks

On max-min fair flow optimization in wireless mesh networks

Using buffered playtime for QoE‐oriented resource management of YouTube video streaming

Aquarema in action: Improving the YouTube QoE in wireless mesh networks

Quantifying the Influence of Network Conditions on the Service Quality Experienced by a Thin Client User