Heavy ion ranges from first-principles electron dynamics

The effects of incident energetic particles, and the modification of materials under irradiation, are governed by the mechanisms of energy losses of ions in matter. The complex processes affecting projectiles spanning many orders of magnitude in energy depend on both ion and electron interactions. Developing multi-scale modeling methods that correctly capture the relevant processes is crucial for predicting radiation effects in diverse conditions. In this work, we obtain channeling ion ranges for tungsten, a prototypical heavy ion, by explicitly simulating ion trajectories with a method that takes into account both the nuclear and the electronic stopping power. The electronic stopping power of self-ion irradiated tungsten is obtained from first-principles time-dependent density functional theory (TDDFT). Although the TDDFT calculations predict a lower stopping power than SRIM by a factor of three, our result shows very good agreement in a direct comparison with ion range experiments. These results demonstrate the validity of the TDDFT method for determining electronic energy losses of heavy projectiles, and in turn its viability for the study of radiation damage.Peer reviewe

Personalized Travel Itineraries with Multi-access Edge Computing Touristic Services

International audienceThe 5G networks enable new touristic services with challenging communication requirements, such as augmented reality (AR) applications, and allow the visitors to enjoy a touristic experience that involves both the physical and virtual space. Here, we propose a novel multiuser travel itinerary planning framework based on an optimal problem formulation that considers both individual trip itinerary (e.g., tourist's preferences, time or cost) and touristic service constraints (e.g., nearby edge cloud resources and application requirements). The main idea is to maximize the itinerary score of individual visitors, while also optimizing the resource allocation at the edge. We consider two services, video streaming and AR, and evaluate our framework using data from Flickr. Results demonstrate gains up to 100% in the resource allocation and user experience in comparison with a state-of-the-art solution adapted to this scenario

Data-Driven Characterization and Modeling of Web Map System Workload

International audienceEvery month, billions of users access Web Map Systems (WMSs), such as Google Maps, to visualize geospatial data. A large number of users and the huge amount of data demanded by these applications make the design and development of WMSs a challenging task, especially in terms of performance and scalability. In this context, workload generators become crucial tools, as they help system administrators to plan the capacity of WMSs and design provisioning strategies for peak load scenarios. However, little is known about the workload patterns generated by WMS users. In this work, we use data anonymously collected from sessions of a client application of Google Maps to devise a model that describes how users of desktop terminals navigate in a Web map. Based on this model, we implement a workload generator called MUSeGen. We compare the workload patterns generated by MUSeGen against the workload patterns found in real data. Results show that MUSeGen generates synthetic traces whose navigation patterns closely match those found in real data. We also compare MUSeGen against HELP, a workload generator built upon previous findings on empirical knowledge on the usage of WMSs. Results show that the number of issued operations per session in HELP is, on average, four times lower than that in MUSeGen and the number of tiles requested is, on average, twice lower than that in our tool. In addition, navigation patterns in HELP are much simpler than in MUSeGen. These findings support the conclusion that MUSeGen produces more realistic workloads than HELP. To illustrate how such differences affect performance evaluation in practice, we carry out a performance evaluation of a real WMS under workloads generated by HELP and MUSeGen. Our evaluation shows that the system capacity under HELP is three times less than that obtained under MUSeGen, highlighting the value of MUSeGen

Itinerary Recommendation Algorithm in the Age of MEC

To provide fully immersive mobile experiences, next-generation touristic services will rely on the high bandwidth and low latency provided by the 5G networks and the Multi-access Edge Computing (MEC) paradigm. Recommendation algorithms, being integral part of travel planning systems, devise personalized tour itineraries for a user considering the popularity of the Points of Interest (POIs) of a city as well as the tourist preferences and constraints. However, in the context of next-generation touristic services, recommendation algorithms should also consider the applications (e.g., augmented reality) the tourist will consume in the POIs and the quality in which such applications will be delivered by the MEC infrastructure. In this paper, we address the joint problem of recommending personalized tour itineraries for tourists and efficiently allocating MEC resources for advanced touristic applications. We formulate an optimization problem that maximizes the itinerary score of individual tourists, while optimizing the resource allocation at the network edge. We then propose an exact algorithm that quickly solves the problem optimally considering instances of realistic size. Finally, we evaluate our algorithm using a real dataset extracted from Flickr. Results demonstrate gains up to 100% in the resource allocation and user experience in comparison with a state-of-the-art solution

Optimizing Content Caching and Recommendations with Context Information in Multi-Access Edge Computing

Recently, the coupling between content caching at the wireless network edge and video recommendation systems has shown promising results to optimize the cache hit and improve the user experience. However, the quality of the UE wireless link and the resource capabilities of the UE are aspects that impact user experience and that have been neglected in the literature. In this work, we present a resource-aware optimization model for the joint task of caching and recommending videos to mobile users that maximizes the cache hit ratio and the user QoE (concerning content preferences and video representations) under the constraints of UE capabilities and the availability of network resources by the time of the recommendation. We evaluate our proposed model using a video catalog derived from a real-world video content dataset and real-world video representations and compare the performance with a state-of-the-art caching and recommendation method unaware of computing and network resources. Results show that our approach increases user QoE by at least 68% and effective cache hit ratio by at least 14% in comparison with the other method

Molecular dynamics simulations of non-equilibrium systems

Peer reviewe

Personalized Travel Itineraries with Multi-access Edge Computing Touristic Services

International audienceThe 5G networks enable new touristic services with challenging communication requirements, such as augmented reality (AR) applications, and allow the visitors to enjoy a touristic experience that involves both the physical and virtual space. Here, we propose a novel multiuser travel itinerary planning framework based on an optimal problem formulation that considers both individual trip itinerary (e.g., tourist's preferences, time or cost) and touristic service constraints (e.g., nearby edge cloud resources and application requirements). The main idea is to maximize the itinerary score of individual visitors, while also optimizing the resource allocation at the edge. We consider two services, video streaming and AR, and evaluate our framework using data from Flickr. Results demonstrate gains up to 100% in the resource allocation and user experience in comparison with a state-of-the-art solution adapted to this scenario

Combining Resource-Aware Recommendation and Caching in the Era of MEC for Improving the Experience of Video Streaming Users

International audienceThe coupling between content caching at the wireless network edge and video recommendation systems has shown promising results to optimize the cache hit and improve the user quality of experience (QoE). However, the quality of the UE wireless link and the resource capabilities of the UE are aspects that impact user QoE and that have been neglected in the literature. In this work, we present a resource-aware optimization model for the joint task of caching and recommending videos to mobile users that maximizes the cache hit ratio and the user QoE under the constraints of UE capabilities and the availability of network resources. In order to make the problem manageable, we assume that the regular user consumes video content keeping some time interval between them, and this user moves slowly inside the coverage of a base station. We evaluate our proposal using a video catalog derived from a real-world video content dataset and real-world video representations and compare the performance with a state-of-the-art caching and recommendation method unaware of computing and network resources. Results show that our approach increases user QoE by at least 68% and cache hit ratio by at least 14% in comparison with the other method