Titlebook: Autonomous Control for a Reliable Internet of Services; Methods, Models, App Ivan Ganchev,R. D. van der Mei,Hans van den Berg Book‘‘‘‘‘‘‘‘

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2,-Structure Drawings of Similar Moleculesdia 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

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https://doi.org/10.1007/BFb0021783 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 S

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Ulrich Fößmeier,Carsten Heß,Michael Kaufmannease 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 Shoot

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Rodolfo Castelló,Rym Mili,Ioannis G. TollisQuality 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 analy

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Moritz Klammler,Tamara Mchedlidze,Alexey Pakring 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

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Moritz Klammler,Tamara Mchedlidze,Alexey Pakmization 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

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