RENDERING-ON-DEMAND SERVICE ACROSS HETEROGENEOUS GRID ENVIRONMENT

2005

This paper describes an e-Commerce system which adds the possibility of creating 3D realistic virtual environments. As component of the system, a Grid rendering service has been developed in order to provide the render engine to the e-Commerce application. The service has been designed in a generic way, so it can be used for any application with special quality requirements in the image generation process. A parallel version of the hierarchical radiosity algorithm has been developed inside the rendering service in order to obtain 3D navigable scenes in a short period of time.

2015

The PGPGrid project aims to apply Grid technologies to the production of computer-generated animation. This involves undertaking the compute-intensive processes of modelling and rendering by employing Grids in a Virtual Organisation setting. The project will attempt to implement a Wide-Area Rendering Environment (WARE) that will allow the exploitation of remote rendering farms. This involves the design and implementation of a Remote Rendering System (RRS) based on Java and Web Services. This paper presents the high level designs of the WARE and RRS and the experience gained from the implementation of a prototype based on these designs.

The movies like the “Avathar” are a good example of the stunning visual effects that the animation could bring into a movie. The 3D wireframe models are converted to 3D photorealistic images using a process called the rendering. This rendering process is offered as a service in the cloud, where the animation files to be rendered are split into frames and rendered in the cloud resources and are popularly known as Rendering-as-a-Service (RaaS). As this is gaining high popularity among the animators community, this work intends to enable the animators to: (a) Gain basic knowledge about Rendering-as-a-Service (RaaS). (b) Understand the variety in the RaaS service models through the taxonomy (c) Explore, compare and classify the RaaS services quickly using the tree-structured taxonomy of services. In this paper, the various characteristics of the RaaS services are organized in the form of a tree to enable quick classification and comparison of the RaaS services. To enhance the understandability, three popular RaaS services have been classified and verified according to the proposed tree-structured taxonomy.

Proceedings of the ACM/IEEE SC2004 Conference, 2004

This paper presents a distributed, collaborative grid enabled visualization environment that supports automated resource discovery across heterogeneous machines. Our Resource-Aware Visualization Environment (RAVE) runs as a background process using Grid/Web services, enabling us to share resources with other users rather than commandeering an entire machine. RAVE supports a wide range of machines, from hand-held PDAs to high-end servers with large-scale stereo, tracked displays. The local display device may render all, some or none of the data set remotely, depending on its available resources. This enables scientists and engineers to collaborate from their desks, in the field or in front of specialised immersive displays. We present initial results of our implementation, showing how we distribute complete datasets across multiple machines as required, using a central data service to distribute data updates from collaborating users. We will demonstrate RAVE at SC2004, utilising available heterogeneous resources.

Cloud computing is gaining popularity in the 3D Animation industry for rendering the 3D images. Rendering is an inevitable task in creating the 3d animated scenes. It is a process where the scene files to be animated is read and converted into 3D photorealistic images automatically. Since it is a computationally intensive task, this process consumes the majority of the time taken for 3D images production. As the scene files could be processed in parallel, clusters of computers called render farms can be used to speed up the rendering process. The advantage of using Cloud based render farms is that it is scalable and can be availed on demand. One of the important challenges faced by the 3D studios is the comparison and selection of the cloud based render farm service provider who could satisfy their functional and the non functional Quality of Service (QoS) requirements. In this paper we propose, a frame work for Cloud Service Broker (CSB) responsible for the selection and provision of the cloud based render farm. The Cloud Service Broker matches the functional and the non functional Quality of Service requirements (QoS) of the user with the service offerings of the render farm service providers and helps the user in selecting the right service provider using an aggregate utility function. The CSB also facilitates the process of Service Level Agreement (SLA) negotiation and monitoring by the third party monitoring services.

2012

2006

The Scalable Adaptive Graphics Environment (SAGE) is specialized middleware for enabling data, high-definition video and extremely high-resolution graphics to be streamed in real-time from remotely distributed rendering and storage clusters to scalable display walls over ultrahigh-speed networks. In this paper, we present the SAGE architecture, focusing on its dynamic graphics streaming capability. In the SAGE framework, multiple visualization applications can be streamed to large tiled displays and viewed at the same time. The application windows can be moved, resized and overlapped like any standard desktop window manager. Every window movement or resize operation requires dynamic and non-trivial reconfiguration of the involved graphics streams. This approach has been successfully shown to scale to support streaming on the LambdaVision 100 Megapixel display wall. SAGE is now being extended to support distance collaboration with multiple endpoints by streaming visualization to all the participants.

2015

Systems now days are requiring huge database and massive power of computation. This cannot be gained using the available computation technology or computers. Execution time and large data processing are problems which are usually encountered in highly demanded applications such as multimedia processing. Grid computing technology offers a possible solution for computational intensive applications. Canny edge detection algorithm is known as the optimum algorithm for edge detection that performs edge detection in five different and computationally extensive stages, which consume large processing time. In this work, grid computing is used to perform Canny edge detection as an application of grid computing in multimedia processing. Univa grid engine is used the middleware for the proposed grid system. It is demonstrated here that the proposed grid based solution that integrates grid computing technology into Canny edge detection reduces the processing time while preserving the expected p...

— Hollywood movies like the ―Flight‖, ―Star Trek: Into Darkness‖ showcase the magical photorealistic effects produced using the 3D animation techniques. In the 3D studios the animation scene files undergo a process called as rendering, where the 3D wire frame models are converted into 3D photorealistic images. As the rendering process is both a computationally intensive and a time consuming task, the cloud based rendering in cloud render farms is gaining popularity among the animators. The advantages of using the cloud based render farms are that it enables the on demand scalability of the render nodes on the Pay-as-you-go model. The animators could choose from either an IaaS cloud service that provides only the render nodes or a PaaS Render farm service that provides the complete rendering environment which includes the render nodes, software licensing, render job management software etc. Though cloud renderfarm services are beneficial, the animators and 3D studios hesitate to move from their traditional offline renderfarms to cloud renderfarms as they lack the knowledge, expertise in using the cloud technology for rendering. They also lack the time to compare the render farm service providers based on the Quality of Service offered by them, negotiate the QoS and monitor whether the agreed upon QoS is actually offered by the renderfarm service providers. In this paper we propose a Cloud Service Broker (CSB) framework called the RenderSelect that helps in the dynamic selection, negotiation and monitoring of the cloud based render farm services. The method of selecting the render farm services based on the Service Measurement Index (SMI) suggested by the CSMIC (Cloud Service Measurement Index Consortium) and ranking the services using the AHP Multi Criteria Decision Making (MCDM) Method is illustrated in detail with an example.

2004

Grid Computing clusters a wide variety of geographically distributed resources. As a result it can be considered as a promising platform for solving large scale intensive problems. For this reason, it can be considered as one of the hottest issues in the computer society. A computational intensive application which can be gained from such a Grid infrastructure is rendering, a process dealing with creating realistic computer-generated image and with many applications ranging from simulation to design and entertainment. To implement, however, a rendering process in a Grid infrastructure is to perform prediction of its computational complexity. This is addressed, in this paper, by using several neural network modules, each of which is appropriate for a given rendering process. For this reason, initially, a feature vector is constructed to describe with high efficiency the parameters affected the complexity of a rendering algorithm. The feature vector is estimated by parsing a file on a RIB format. Then, prediction is performed using a neural network model. Prediction of three types of rendering algorithms is examined; the Ray Tracing, the Radiosity and the Monte Carlo irradiance analysis.