Abstract-This

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As the Internet of Things (IoT) continues to gain traction in telecommunication networks, a very large number of devices are expected to be connected and used in the near future. In order to appropriately plan and dimension the network, as well as the back-end cloud systems and the resulting signaling load, traffic models are employed. These models are designed to accurately capture and predict the properties of IoT traffic in a concise manner. To achieve this, Poisson process approximations, based on the Palm-Khintchine theorem, have often been used in the past. Due to the scale (and the difference in scales in various IoT networks) of the modeled systems, the fidelity of this approximation is crucial, as in practice, it is very challenging to accurately measure or simulate large-scale IoT deployments. The main goal of this paper is to understand the level of accuracy of the Poisson approximation model. To this end, we first survey both common IoT network properties and network scales as well as traffic types. Second, we explain and discuss the Palm-Khintiche theorem, how it is applied to the problem, and which inaccuracies can occur when using it. Based on this, we derive guidelines as to when a Poisson process can be assumed for aggregated periodic IoT traffic. Finally, we evaluate our approach in the context of an IoT cloud scaler use case.

Abstract-User-centric service and application management focuses on the Quality of Experience (QoE) as perceived by the end user. Thereby, the goal is to maximize QoE while ensuring fairness among users, e.g., for resource allocation and scheduling in shared systems. Although the literature suggests to consider consequently QoE fairness, there is currently no accepted definition of QoE fairness. The contribution of this paper is the definition of a generic QoE fairness index F which has desirable key properties as well as the rationale behind it. By using examples and a measurement study involving multiple users downloading web content over a bottleneck link, we differentiate the proposed index from QoS fairness and the widely used Jain's fairness index. Based on results, we argue that neither QoS fairness nor Jain's fairness index meet all of the desirable QoE-relevant properties which are met by F . Consequently, the proposed index F may be used to compare QoE fairness across systems and applications, thus serving as a benchmark for QoE management mechanisms and system optimization.

The introduction of Network Function Virtualisation (NFV) represents a significant change in networking technology, which may create new opportunities in terms of cost efficiency, operations, and service provisioning. Although not explicitly stated as an objective, the dependability of the services provided using this technology should be at least as good as conventional solutions. Logical centralisation, off-the-shelf computing platforms, and increased system complexity represent new dependability challenges relative to the state of the art. The core function of the network, with respect to failure and service management, is orchestration. The failure and misoperation of the NFV Orchestrator (NFVO) will have huge network-wide consequences. At the same time, NFVO is vulnerable to overload and design faults.Thus, the objective of this paper is to give a tutorial on the dependability challenges of the NFVO, and to give insight into the required future research. This paper provides necessary background information, reviews the available literature, outlines the proposed solutions, and identifies some design and research problems that must be addressed.