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The explosive growth of the Internet has fundamentally changed the global society. The emergence of concepts like service-oriented architecture (SOA), Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Network as a Service (NaaS) and Cloud Computing in general has catalyzed the migration from the information-oriented Internet into an Internet of Services (IoS). This has opened up virtually unbounded possibilities for the creation of new and innovative services that facilitate business processes and improve the quality of life. However, this also calls for new approaches to ensuring quality and reliability of these services. The goal of this book chapter is to first analyze the state-of-the-art in the area of autonomous control for a reliable IoS and then to identify the main research challenges within it. A general background and high-level description of the current state of knowledge is presented. Then, for each of the three subareas, namely the autonomous management and real-time control, methods and tools for monitoring and service prediction, and smart pricing and competition in multi-domain systems, a brief general introduction and background are presented, and a list of key research challenges is formulated.

Whereas some application domains show a certain consensus on the role of system factors, human factors, and context factors, QoE management of multimedia systems and services is still faced with the challenge of identifying the key QoE influence factors. In this chapter, we focus on the potential of enhancing QoE management mechanisms by exploiting valuable context information.

To get a good grip on the basics we first discuss a general framework for context monitoring and define context information, including technical, usage, social, economic, temporal, and physical factors. We then iterate the opportunities and challenges in involving context in QoE monitoring solutions, as context may be, e.g., hard to ascertain or very situational.

The benefits of including context in QoE monitoring and management are demonstrated through use cases involving video flash crowds as well as online and cloud gaming.

Finally, we discuss potential technical realizations of context-aware QoE monitoring and management derived based on the SDN paradigm.

This chapter discusses prospects of QoE management for future networks and applications. After motivating QoE management, it first provides an introduction to the concept by discussing its origins, key terms and giving an overview of the most relevant existing theoretical frameworks. Then, recent research on promising technical approaches to QoE-driven management that operate across different layers of the networking stack is discussed. Finally, the chapter provides conclusions and an outlook on the future of QoE management with a focus on those key enablers (including cooperation, business models and key technologies) that are essential for ultimately turning QoE-aware network and application management into reality.

Conceptual and analytical models of an overall telecommunication system are utilized in this chapter for the definition of scalable indicators towards Quality of Service (QoS) monitoring, prediction, and management. The telecommunication system is considered on different levels – service phase, service stage, network, and overall system. The network itself is presented in seven service stages – A-user, A-terminal, Dialing, Switching, B-terminal Seizure, B-terminal, and B-user, each having its own characteristics and specifics. Traffic quality indicators are proposed on each level. Two network cost/quality ratios are proposed – mean and instantaneous – along with illustrative numerical predictions of the latter, which could be useful for dynamic pricing policy execution, depending on the network load. All defined indicators could be considered as sources for Quality of Experience (QoE) prediction.

Cloud gaming is an emerging technology that combines cloud computing with computer games. Compared to traditional gaming, its core advantages include ease of development/deployment for developers, and lower technology costs for users given the potential to play on thin client devices. In this chapter, we firstly describe the approach, and then focus on the impact of latency, known as lag, on Quality of Experience, for so-called First Person Shooter games. We outline our approach to lag compensation whereby we equalize within reason the up and downlink delays in real-time for all players. We describe the testbed in detail, the open source Gaming Anywhere platform, the use of NTP to synchronise time, the network emulator and the role of the centralized log server. We then present results that firstly validate the mechanism and also use small scale and preliminary subjective tests to assess and prove its performance. We conclude the chapter by outlining ongoing and future work.

Video streaming has become an indispensable technology in people’s lives, while its usage keeps constantly increasing. The variability, instability and unpredictability of network conditions pose one of the biggest challenges to video streaming. In this chapter, we analyze HTTP Adaptive Streaming, a technology that relieves these issues by adapting the video reproduction to the current network conditions. Particularly, we study how context awareness can be combined with the adaptive streaming logic to design a proactive client-based video streaming strategy. Our results show that such a context-aware strategy manages to successfully mitigate stallings in light of network connectivity problems, such as an outage. Moreover, we analyze the performance of this strategy by comparing it to the optimal case, as well as by considering situations where the awareness of the context lacks reliability.

This chapter presents scalable conceptual and analytical performance models of overall telecommunication systems, allowing the prediction of multiple Quality of Service (QoS) indicators as functions of the user- and network behavior. Two structures of the conceptual presentation are considered and an analytical method for converting the presentations, along with corresponding additive and multiplicative metrics, is proposed. A corresponding analytical model is elaborated, which allows the prediction of flow-, time-, and traffic characteristics of terminals and users, as well as the overall network performance. In accordance with recommendations of the International Telecommunications Union’s Telecommunication Standardization Sector (ITU-T), analytical expressions are proposed for predicting four QoS indicators. Differentiated QoS indicators for each subservice, as well as analytical expressions for their prediction, are proposed. Overall pie characteristics and their causal aggregations are proposed as causal-oriented QoS indicators. The results demonstrate the ability of the model to facilitate a more precise dynamic QoS management as well as to serve as a source for predicting some Quality of Experience (QoE) indicators.

With the emerging IoT and Cloud-based networked systems that rely heavily on virtualization technologies, elasticity becomes a dominant system engineering attribute for providing QoS-aware services to their users. Although the concept of elasticity can introduce significant QoS and cost benefits, its implementation in real systems is full of challenges. Indeed, nowadays systems are mainly distributed, built upon several layers of abstraction, and with centralized control mechanisms. In such a complex environment, controlling elasticity in a centralized manner might strongly penalize scalability. To overcome this issue, we can conveniently split the system in autonomous subsystems that implement elasticity mechanisms and run control policies in a decentralized manner. To efficiently and effectively cooperate with each other, the subsystems need to communicate among themselves to determine elasticity decisions that collectively improve the overall system performance. This new architecture calls for the development of new mechanisms and efficient policies. In this chapter, we focus on elasticity in IoT and Cloud-based systems, which can be geo-distributed also at the edge of the networks, and discuss its engineering perspectives along with various coordination mechanisms. We focus on the design choices that may affect the elasticity properties and provide an overview of some decentralized design patterns related to the coordination of elasticity decisions.

Traditional networks are transformed to enable full integration of heterogeneous hardware and software functions, that are configured at runtime, with minimal time to market, and are provided to their end users on “as a service” principle. Therefore, a countless number of possibilities for further innovation and exploitation opens up. Network Function Virtualization (NFV) and Software-Defined Networking (SDN) are two key enablers for such a new flexible, scalable, and service-oriented network architecture. This chapter provides an overview of QoS-aware strategies that can be used over the levels of the network abstraction aiming to fully exploit the new network opportunities. Specifically, we present three use cases of integrating SDN and NFV with QoS-aware service composition, ranging from the energy efficient placement of virtual network functions inside modern data centers, to the deployment of data stream processing applications using SDN to control the network paths, to exploiting SDN for context-aware service compositions.

Network Performance (NP)- and more recently Quality of Service/Experience/anything (QoS/QoE/QoX)-based network management techniques focus on the maximization of associated Key Performance Indicators (KPIs). Such mechanisms are usually constrained by certain thresholds of other system design parameters. e.g., typically, cost. When applied to the current competitive heterogeneous Cloud Services scenario, this approach may have become obsolete due to its static nature. In fact, energy awareness and the capability of modern technologies to deliver multimedia content at different possible combinations of quality (and prize) demand a complex optimization framework.

It is therefore necessary to define more flexible paradigms that make it possible to consider cost, energy and even other currently unforeseen design parameters not as simple constraints, but as tunable variables that play a role in the adaptation mechanisms.

In this chapter we will briefly introduce most commonly used frameworks for multi-criteria optimization and evaluate them in different Energy vs. QoX sample scenarios. Finally, the current status of related network management tools will be described, so as to identify possible application areas.