2,938 research outputs found
Semantic multimedia remote display for mobile thin clients
Current remote display technologies for mobile thin clients convert practically all types of graphical content into sequences of images rendered by the client. Consequently, important information concerning the content semantics is lost. The present paper goes beyond this bottleneck by developing a semantic multimedia remote display. The principle consists of representing the graphical content as a real-time interactive multimedia scene graph. The underlying architecture features novel components for scene-graph creation and management, as well as for user interactivity handling. The experimental setup considers the Linux X windows system and BiFS/LASeR multimedia scene technologies on the server and client sides, respectively. The implemented solution was benchmarked against currently deployed solutions (VNC and Microsoft-RDP), by considering text editing and WWW browsing applications. The quantitative assessments demonstrate: (1) visual quality expressed by seven objective metrics, e.g., PSNR values between 30 and 42 dB or SSIM values larger than 0.9999; (2) downlink bandwidth gain factors ranging from 2 to 60; (3) real-time user event management expressed by network round-trip time reduction by factors of 4-6 and by uplink bandwidth gain factors from 3 to 10; (4) feasible CPU activity, larger than in the RDP case but reduced by a factor of 1.5 with respect to the VNC-HEXTILE
6G Mobile-Edge Empowered Metaverse: Requirements, Technologies, Challenges and Research Directions
The Metaverse has emerged as the successor of the conventional mobile
internet to change people's lifestyles. It has strict visual and physical
requirements to ensure an immersive experience (i.e., high visual quality, low
motion-to-photon latency, and real-time tactile and control experience).
However, the current communication systems fall short to satisfy these
requirements. Mobile edge computing (MEC) has been indispensable to enable low
latency and powerful computing. Moreover, the sixth generation (6G) networks
promise to provide end users with high-capacity communications to MEC servers.
In this paper, we bring together the primary components into a 6G mobile-edge
framework to empower the Metaverse. This includes the usage of heterogeneous
radios, intelligent reflecting surfaces (IRS), non-orthogonal multiple access
(NOMA), and digital twins (DTs). We also discuss novel communication paradigms
(i.e., semantic communication, holographic-type communication, and haptic
communication) to further satisfy the demand for human-type communications and
fulfil user preferences and immersive experiences in the Metaverse
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Multimedia delivery in the future internet
The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio
and video are produced, distributed, shared, managed and consumed on-line through various networks,
like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white
paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked
challenges of the Networked Media in the transition to the Future of the Internet.
Internet has evolved and changed the way we work and live. End users of the Internet have been confronted
with a bewildering range of media, services and applications and of technological innovations concerning
media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace
of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more
than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so
regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected
to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising
to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged
that in a near- to mid-term future, the Internet will provide the means to share and distribute (new)
multimedia content and services with superior quality and striking flexibility, in a trusted and personalized
way, improving citizens’ quality of life, working conditions, edutainment and safety.
In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe
network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as
community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of
interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and
innovative applications “on the move”, like virtual collaboration environments, personalised services/
media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content
combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P
networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to
contribute towards such a vision.
Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6)
and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily
contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way
ahead in the area of Content Aware media delivery platforms
Mobile graphics: SIGGRAPH Asia 2017 course
Peer ReviewedPostprint (published version
Crowd simulation and visualization
Large-scale simulation and visualization are essential topics in areas as different as sociology, physics, urbanism, training, entertainment among others.
This kind of systems requires a vast computational power and memory resources commonly available in High Performance Computing HPC platforms. Currently, the most potent clusters have heterogeneous architectures with hundreds of thousands and even millions of cores. The industry trends inferred that exascale clusters would have thousands of millions.
The technical challenges for simulation and visualization process in the exascale era are intertwined with difficulties in other areas of research, including storage, communication, programming models and hardware. For this reason, it is necessary prototyping, testing, and deployment a variety of approaches to address the technical challenges identified and evaluate the advantages and disadvantages of each proposed solution.
The focus of this research is interactive large-scale crowd simulation and visualization. To exploit to the maximum the capacity of the current HPC infrastructure and be prepared to take advantage of the next generation. The project develops a new approach to scale crowd simulation and visualization on heterogeneous computing cluster using a task-based technique. Its main characteristic is hardware agnostic. It abstracts the difficulties that imply the use of heterogeneous architectures like memory management, scheduling, communications, and synchronization — facilitating development, maintenance, and scalability.
With the goal of flexibility and take advantage of computing resources as best as possible, the project explores different configurations to connect the simulation with the visualization engine. This kind of system has an essential use in emergencies. Therefore, urban scenes were implemented as realistic as possible; in this way, users will be ready to face real events.
Path planning for large-scale crowds is a challenge to solve, due to the inherent dynamism in the scenes and vast search space. A new path-finding algorithm was developed. It has a hierarchical approach which offers different advantages: it divides the search space reducing the problem complexity, it can obtain a partial path instead of wait for the complete one, which allows a character to start moving and compute the rest asynchronously. It can reprocess only a part if necessary with different levels of abstraction.
A case study is presented for a crowd simulation in urban scenarios. Geolocated data are used, they were produced by mobile devices to predict individual and crowd behavior and detect abnormal situations in the presence of specific events. It was also address the challenge of combining all these individual’s location with a 3D rendering of the urban environment. The data processing and simulation approach are computationally expensive and time-critical, it relies thus on a hybrid Cloud-HPC architecture to produce an efficient solution.
Within the project, new models of behavior based on data analytics were developed. It was developed the infrastructure to be able to consult various data sources such as social networks, government agencies or transport companies such as Uber. Every time there is more geolocation data available and better computation resources which allow performing analysis of greater depth, this lays the foundations to improve the simulation models of current crowds.
The use of simulations and their visualization allows to observe and organize the crowds in real time. The analysis before, during and after daily mass events can reduce the risks and associated logistics costs.La simulaciĂłn y visualizaciĂłn a gran escala son temas esenciales en áreas tan diferentes como la sociologĂa, la fĂsica, el urbanismo, la capacitaciĂłn, el entretenimiento, entre otros. Este tipo de sistemas requiere una gran capacidad de cĂłmputo y recursos de memoria comĂşnmente disponibles en las plataformas de computo de alto rendimiento. Actualmente, los equipos más potentes tienen arquitecturas heterogĂ©neas con cientos de miles e incluso millones de nĂşcleos. Las tendencias de la industria infieren que los equipos en la era exascale tendran miles de millones. Los desafĂos tĂ©cnicos en el proceso de simulaciĂłn y visualizaciĂłn en la era exascale se entrelazan con dificultades en otras áreas de investigaciĂłn, incluidos almacenamiento, comunicaciĂłn, modelos de programaciĂłn y hardware. Por esta razĂłn, es necesario crear prototipos, probar y desplegar una variedad de enfoques para abordar los desafĂos tĂ©cnicos identificados y evaluar las ventajas y desventajas de cada soluciĂłn propuesta. El foco de esta investigaciĂłn es la visualizaciĂłn y simulaciĂłn interactiva de multitudes a gran escala. Aprovechar al máximo la capacidad de la infraestructura actual y estar preparado para aprovechar la prĂłxima generaciĂłn. El proyecto desarrolla un nuevo enfoque para escalar la simulaciĂłn y visualizaciĂłn de multitudes en un clĂşster de computo heterogĂ©neo utilizando una tĂ©cnica basada en tareas. Su principal caracterĂstica es que es hardware agnĂłstico. Abstrae las dificultades que implican el uso de arquitecturas heterogĂ©neas como la administraciĂłn de memoria, las comunicaciones y la sincronizaciĂłn, lo que facilita el desarrollo, el mantenimiento y la escalabilidad. Con el objetivo de flexibilizar y aprovechar los recursos informáticos lo mejor posible, el proyecto explora diferentes configuraciones para conectar la simulaciĂłn con el motor de visualizaciĂłn. Este tipo de sistemas tienen un uso esencial en emergencias. Por lo tanto, se implementaron escenas urbanas lo más realistas posible, de esta manera los usuarios estarán listos para enfrentar eventos reales. La planificaciĂłn de caminos para multitudes a gran escala es un desafĂo a resolver, debido al dinamismo inherente en las escenas y el vasto espacio de bĂşsqueda. Se desarrollĂł un nuevo algoritmo de bĂşsqueda de caminos. Tiene un enfoque jerárquico que ofrece diferentes ventajas: divide el espacio de bĂşsqueda reduciendo la complejidad del problema, puede obtener una ruta parcial en lugar de esperar a la completa, lo que permite que un personaje comience a moverse y calcule el resto de forma asĂncrona, puede reprocesar solo una parte si es necesario con diferentes niveles de abstracciĂłn. Se presenta un caso de estudio para una simulaciĂłn de multitud en escenarios urbanos. Se utilizan datos geolocalizados producidos por dispositivos mĂłviles para predecir el comportamiento individual y pĂşblico y detectar situaciones anormales en presencia de eventos especĂficos. TambiĂ©n se aborda el desafĂo de combinar la ubicaciĂłn de todos estos individuos con una representaciĂłn 3D del entorno urbano. Dentro del proyecto, se desarrollaron nuevos modelos de comportamiento basados ¿¿en el análisis de datos. Se creo la infraestructura para poder consultar varias fuentes de datos como redes sociales, agencias gubernamentales o empresas de transporte como Uber. Cada vez hay más datos de geolocalizaciĂłn disponibles y mejores recursos de cĂłmputo que permiten realizar un análisis de mayor profundidad, esto sienta las bases para mejorar los modelos de simulaciĂłn de las multitudes actuales. El uso de simulaciones y su visualizaciĂłn permite observar y organizar las multitudes en tiempo real. El análisis antes, durante y despuĂ©s de eventos multitudinarios diarios puede reducir los riesgos y los costos logĂsticos asociadosPostprint (published version
Service Abstractions for Scalable Deep Learning Inference at the Edge
Deep learning driven intelligent edge has already become a reality, where millions of mobile, wearable, and IoT devices analyze real-time data and transform those into actionable insights on-device. Typical approaches for optimizing deep learning inference mostly focus on accelerating the execution of individual inference tasks, without considering the contextual correlation unique to edge environments and the statistical nature of learning-based computation. Specifically, they treat inference workloads as individual black boxes and apply canonical system optimization techniques, developed over the last few decades, to handle them as yet another type of computation-intensive applications. As a result, deep learning inference on edge devices still face the ever increasing challenges of customization to edge device heterogeneity, fuzzy computation redundancy between inference tasks, and end-to-end deployment at scale. In this thesis, we propose the first framework that automates and scales the end-to-end process of deploying efficient deep learning inference from the cloud to heterogeneous edge devices. The framework consists of a series of service abstractions that handle DNN model tailoring, model indexing and query, and computation reuse for runtime inference respectively. Together, these services bridge the gap between deep learning training and inference, eliminate computation redundancy during inference execution, and further lower the barrier for deep learning algorithm and system co-optimization. To build efficient and scalable services, we take a unique algorithmic approach of harnessing the semantic correlation between the learning-based computation. Rather than viewing individual tasks as isolated black boxes, we optimize them collectively in a white box approach, proposing primitives to formulate the semantics of the deep learning workloads, algorithms to assess their hidden correlation (in terms of the input data, the neural network models, and the deployment trials) and merge common processing steps to minimize redundancy
Enhancing Mobile Capacity through Generic and Efficient Resource Sharing
Mobile computing devices are becoming indispensable in every aspect of human life, but diverse hardware limits make current mobile devices far from ideal for satisfying the performance requirements of modern mobile applications and being used anytime, anywhere. Mobile Cloud Computing (MCC) could be a viable solution to bypass these limits which enhances the mobile capacity through cooperative resource sharing, but is challenging due to the heterogeneity of mobile devices in both hardware and software aspects. Traditional schemes either restrict to share a specific type of hardware resource within individual applications, which requires tremendous reprogramming efforts; or disregard the runtime execution pattern and transmit too much unnecessary data, resulting in bandwidth and energy waste.To address the aforementioned challenges, we present three novel designs of resource sharing frameworks which utilize the various system resources from a remote or personal cloud to enhance the mobile capacity in a generic and efficient manner. First, we propose a novel method-level offloading methodology to run the mobile computational workload on the remote cloud CPU. Minimized data transmission is achieved during such offloading by identifying and selectively migrating the memory contexts which are necessary to the method execution. Second, we present a systematic framework to maximize the mobile performance of graphics rendering with the remote cloud GPU, during which the redundant pixels across consecutive frames are reused to reduce the transmitted frame data. Last, we propose to exploit the unified mobile OS services and generically interconnect heterogeneous mobile devices towards a personal mobile cloud, which complement and flexibly share mobile peripherals (e.g., sensors, camera) with each other
Design and management of image processing pipelines within CPS: Acquired experience towards the end of the FitOptiVis ECSEL Project
Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints
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