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The necessary adjustments to prominent measures of the neutral rate of interest following the COVID pandemic sparked a wide-ranging debate on the measurement and usefulness of r-star. Due to high uncertainty about relevant determinants, trend patterns and the correct estimation method, we propose in this paper a simple alternative approach derived from a standard macro model. Starting from a loss function, neutral periods can be determined in which a neutral real interest rate is observable. Using these values, a medium-term trend for a neutral interest rate can be determined. An application to the USA shows that our simple calculation of a neutral interest rate delivers comparable results to existing studies. A Taylor rule based on our neutral interest rate also does a fairly good job of explaining US monetary policy over the past 60 years.
Government support for green technologies and renewable energy in particular has become an integral cornerstone of economic policy for most industrialized economies. Due to competitive price determination and supposedly higher efficiency, auctions have in recent years widely succeeded feed-in-tariffs as the primary support instrument (del Rio & Linares, 2014; REN21, 2021). However, literature still struggles to produce causal evidence to validate mostly descriptive findings for efficiency gains. Yet, this evidence is needed as a foundation to provide robust recommendations to policy makers (Grashof et al., 2020). By utilizing a difference-in-differences approach, this paper provides such evidence for a German photovoltaic (PV) auctioning program which came into effect in 2015. Results for this natural experiment confirm that cost-effectiveness improved significantly while previous literature shows that capacity expansion remained high. Results additionally show that falling prices for PV panels were the primary driver of cost reductions and wages also exert high influence on support price. Input cost development therefore indeed strongly influences support level which was the aim with introducing competitive auctions. Interest rate development cannot be linked to support level development, most probably due to the low interest environment in considered period.
We propose that false beliefs about own current economic status are an important factor for explaining populist attitudes. Eliciting subjects’ receptiveness to rightwing populism and their perceived relative income positions in a representative survey of German households, we find that people with pessimistic beliefs about their income position are more attuned to populist statements. Key to understanding the misperception-populism relationship are strong gender differences in the mechanism: men are much more likely to channel their discontent into affection for populist ideas. A simple information provision does neither sustainably reduce misperception nor curb populism.
Service orchestration requires enormous attention and is a struggle nowadays. Of course, virtualization provides a base level of abstraction for services to be deployable on a lot of infrastructures. With container virtualization, the trend to migrate applications to a micro-services level in order to be executable in Fog and Edge Computing environments increases manageability and maintenance efforts rapidly. Similarly, network virtualization adds effort to calibrate IP flows for Software-Defined Networks and eventually route it by means of Network Function Virtualization. Nevertheless, there are concepts like MAPE-K to support micro-service distribution in next-generation cloud and network environments. We want to explore, how a service distribution can be improved by adopting machine learning concepts for infrastructure or service changes. Therefore, we show how federated machine learning is integrated into a cloud-to-fog-continuum without burdening single nodes.
In network research, reproducibility of experiments is not always easy to achieve. Infrastructures are cumbersome to set up or are not available due to vendor-specific devices. Emulators try to overcome those issues to a given extent and are available in different service models. Unfortunately, the usability of emulators requires time-consuming efforts and a deep understanding of their functionality. At first, we analyze to which extent currently available open-source emulators support network configurations and how user-friendly they are. With these insights, we describe, how an ease-to-use emulator is implemented and may run as a Network Emulator as a Service (NEaaS). Therefore, virtualization plays a major role in order to deploy a NEaaS based on Kathará.
This paper discusses the problem of finding multiple shortest disjoint paths in modern communication networks, which is essential for ultra-reliable and time-sensitive applications. Dijkstra’s algorithm has been a popular solution for the shortest path problem, but repetitive use of it to find multiple paths is not scalable. The Multiple Disjoint Path Algorithm (MDPAlg), published in 2021, proposes the use of a single full graph to construct multiple disjoint paths. This paper proposes modifications to the algorithm to include a delay constraint, which is important in time-sensitive applications. Different delay constraint least-cost routing algorithms are compared in a comprehensive manner to evaluate the benefits of the adapted MDPAlg algorithm. Fault tolerance, and thereby reliability, is ensured by generating multiple link-disjoint paths from source to destination.
In this work, we describe the network from data collection to data processing and storage as a system based on different layers. We outline the different layers and highlight major tasks and dependencies with regard to energy consumption and energy efficiency. With this view, we can outwork challenges and questions a future system architect must answer to provide a more sustainable, green, resource friendly, and energy efficient application or system. Therefore, all system layers must be considered individually but also altogether for future IoT solutions. This requires, in particular, novel sustainability metrics in addition to current Quality of Service and Quality of Experience metrics to provide a high power, user satisfying, and sustainable network.
How to Model and Predict the Scalability of a Hardware-In-The-Loop Test Bench for Data Re-Injection?
(2023)
This paper describes a novel application of an empirical network calculus model based on measurements of a hardware-in-the-loop (HIL) test system. The aim is to predict the performance of a HIL test bench for open-loop re-injection in the context of scalability. HIL test benches are distributed computer systems including software, hardware, and networking devices. They are used to validate complex technical systems, but have not yet been system under study themselves. Our approach is to use measurements from the HIL system to create an empirical model for arrival and service curves. We predict the performance and design the previously unknown parameters of the HIL simulator with network calculus (NC), namely the buffer sizes and the minimum needed pre-buffer time for the playback buffer. We furthermore show, that it is possible to estimate the CPU load from arrival and service-curves based on the utilization theorem, and hence estimate the scalability of the HIL system in the context of the number of sensor streams.
In recent years, satellite communication has been expanding its field of application in the world of computer networks. This paper aims to provide an overview of how a typical scenario involving 5G Non-Terrestrial Networks (NTNs) for vehicle to everything (V2X) applications is characterized. In particular, a first implementation of a system that integrates them together will be described. Such a framework will later be used to evaluate the performance of applications such as Vehicle Monitoring (VM), Remote Driving (RD), Voice Over IP (VoIP), and others. Different configuration scenarios such as Low Earth Orbit and Geostationary Orbit will be considered.
The introduction of new types of frequency spectrum in 6G technology facilitates the convergence of conventional mobile communications and radar functions. Thus, the mobile network itself becomes a versatile sensor system. This enables mobile network operators to offer a sensing service in addition to conventional data and telephony services. The potential benefits are expected to accrue to various stakeholders, including individuals, the environment, and society in general. The paper discusses technological development, possible integration, and use cases, as well as future development areas.