A Worst-Case Approximate Analysis of Peak Age-of-Information Via Robust Queueing Approach

A new timeliness metric, called Age-of-Information (AoI), has recently attracted a lot of research interests for real-time applications with information updates. It has been extensively studied for various queueing models based on the probabilistic approaches, where the analyses heavily depend on the properties of specific distributions (e.g., the memoryless property of the exponential distribution or the i.i.d. assumption). In this work, we take an alternative new approach, the robust queueing approach, to analyze the Peak Age-of-Information (PAoI). Specifically, we first model the uncertainty in the stochastic arrival and service processes using uncertainty sets. This enables us to approximate the expected PAoI performance for very general arrival and service processes, including those exhibiting heavy-tailed behaviors or correlations, where traditional probabilistic approaches cannot be applied. We then derive a new bound on the PAoI in the single-source single-server setting. Furthermore, we generalize our analysis to two-source single-server systems with symmetric arrivals, which involves new challenges (e.g., the service times of the updates from two sources are coupled in one single uncertainty set). Finally, through numerical experiments, we show that our new bounds provide a good approximation for the expected PAoI. Compared to some well-known bounds in the literature (e.g., one based on Kingman's bound under the i.i.d. assumption) that tends to be inaccurate under light load, our new approximation is accurate under both light and high loads, both of which are critical scenarios for the AoI performance.Comment: Published in IEEE INFOCOM 202

Waiting but not Aging: Optimizing Information Freshness Under the Pull Model

The Age-of-Information is an important metric for investigating the timeliness performance in information-update systems. In this paper, we study the AoI minimization problem under a new Pull model with replication schemes, where a user proactively sends a replicated request to multiple servers to "pull" the information of interest. Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different values of the AoI across the servers (due to the random updating processes) and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side. Specifically, assuming Poisson updating process for the servers and exponentially distributed response time, we derive a closed-form formula for computing the expected AoI and obtain the optimal number of responses to wait for to minimize the expected AoI. Then, we extend our analysis to the setting where the user aims to maximize the AoI-based utility, which represents the user's satisfaction level with respect to freshness of the received information. Furthermore, we consider a more realistic scenario where the user has no prior knowledge of the system. In this case, we reformulate the utility maximization problem as a stochastic Multi-Armed Bandit problem with side observations and leverage a special linear structure of side observations to design learning algorithms with improved performance guarantees. Finally, we conduct extensive simulations to elucidate our theoretical results and compare the performance of different algorithms. Our findings reveal that under the Pull model, waiting does not necessarily lead to aging; waiting for more than one response can often significantly reduce the AoI and improve the AoI-based utility in most scenarios.Comment: 15 pages. arXiv admin note: substantial text overlap with arXiv:1704.0484

The Effects of Electricity Production on Industrial Development and Sustainable Economic Growth: A VAR Analysis for BRICS Countries

This study aims to evaluate the effect of electricity production on industrial development and sustainable economic growth. In this context, Brazil, Russia, India, China, and South Africa (BRICS), countries which have the highest increase in electricity production in the period of 2000–2018, are included in the scope of this study. Annual data of these variables in the period of 1991–2018 are used and three different models are created by using Vector Auto Regression (VAR) methodology. The findings state that electricity production in BRICS countries has a positive effect on both industrial production and sustainable economic growth. Hence, electricity production needs to be increased for them. For this purpose, it is important to encourage investors with tax advantages, location orientation and financing. Moreover, BRICS countries should give importance to renewable energy investments in order to increase electricity production. These issues have a contributing effect to sustainable economic growth

The effects of electricity production on industrial development and sustainable economic growth: a VAR analysis for BRICS countries

This study aims to evaluate the effect of electricity production on industrial development and sustainable economic growth. In this context, Brazil, Russia, India, China, and South Africa (BRICS), countries which have the highest increase in electricity production in the period of 2000-2018, are included in the scope of this study. Annual data of these variables in the period of 1991-2018 are used and three different models are created by using Vector Auto Regression (VAR) methodology. The findings state that electricity production in BRICS countries has a positive effect on both industrial production and sustainable economic growth. Hence, electricity production needs to be increased for them. For this purpose, it is important to encourage investors with tax advantages, location orientation and financing. Moreover, BRICS countries should give importance to renewable energy investments in order to increase electricity production. These issues have a contributing effect to sustainable economic growth.Philosophy and Social Science Plan Project of Henan Province, Chin