A Distributed Economics-based Infrastructure for Utility Computing

Existing attempts at utility computing revolve around two approaches. The first consists of proprietary solutions involving renting time on dedicated utility computing machines. The second requires the use of heavy, monolithic applications that are difficult to deploy, maintain, and use. We propose a distributed, community-oriented approach to utility computing. Our approach provides an infrastructure built on Web Services in which modular components are combined to create a seemingly simple, yet powerful system. The community-oriented nature generates an economic environment which results in fair transactions between consumers and providers of computing cycles while simultaneously encouraging improvements in the infrastructure of the computational grid itself.Comment: 8 pages, 1 figur

Maestro-VC: On-Demand Secure Cluster Computing Using

On-demand computing is the name given to technology which enables an infrastructure where computing cycles are treated as a commodity, and where such a commodity can be accessed upon request. In this way the goals of on-demand computing overlap with and are similar to those of Grid computing: both enable the pooling of global computing resources to solve complex computational problems

Maestro-VC: A Paravirtualized Execution Environment for Secure

Virtualization, a technology first developed for partitioning the resources of mainframe computers, has seen a resurgence in popularity in the realm of commodity workstation computers. This paper introduces MaestroVC, a system which explores a novel use of VMs as the building blocks of entire Virtual Clusters (VCs). Virtualization of entire clusters is beneficial because existing parallel code can run without modification in the virtual environment. At the same time, inserting a layer of software between a virtual cluster and native hardware allows for security enforcement and flexible resource management in a manner transparent to running parallel code. In this paper we describe the design and implementation of Maestro-VC, and give the results of some preliminary performance experiments

Cluster Security Research Challenges

In this paper, we share insights from our group experience building and experimenting on high performance computing clusters to support our research developing novel cluster security protection techniques and tools

Real-Time 3D Video Compression for Tele-Immersive Environments

Tele-immersive system can improve the productivity and aid communication by allowing distributed parties to exchange information via a shared immersive experience. The TEEVE research project at the University of Illinois (UIUC) and the University of California at Berkeley (UC Berkeley) seeks to promote the application of tele-immersive environments by a holistic integration of existing components that capture, transmit, and render three-dimensional (3D) scenes in real time to convey the sense of an immersive space. However, the transmission of 3D videos poses a great challenge. First, it is bandwidth-intensive as multiple large volume 3D video streams have to be transmitted. Second, the video stream contains not only color but also depth information, which requires different treatment. While color information may be compressed in a lossy manner, the depth information should be compressed losslessly. Therefore, a 3D real-time compression algorithm must be deployed to accommodate both the bandwidth requirement and the variety of data. In this paper, we present and evaluate two compression schemes for compressing 3D video streams containing color and depth information. For the first scheme, we use color reduction followed by background removal to compress the color information, which is then compressed along with the depth information using zlib. For the second scheme, we use motion JPEG to compress the color information and run length (RLE) coding followed by Huffman coding to compress the depth information. The experimental results of 3D videos captured from real tele-immersive environment show that: (1) the compressed data preserves enough information to communicate the 3D images effectively (minimum PSNR >40) and (2) even without inter-frame motion estimation, very high compression ratios (average >15) are achievable at speeds sufficient to allow real-time communication (average 13 ms for each video frame)

Real-Time 3D Video Compression for Tele-Immersive Environments

Tele-immersive systems can improve productivity and aid communication by allowing distributed parties to exchange information via a shared immersive experience. The TEEVE research project at the University of Illinois at Urbana-Champaign and the University of California at Berkeley seeks to foster the development and use of tele-immersive environments by a holistic integration of existing components that capture, transmit, and render three-dimensional (3D) scenes in real time to convey a sense of immersive space. However, the transmission of 3D video poses significant challenges. First, it is bandwidth-intensive, as it requires the transmission of multiple large-volume 3D video streams. Second, existing schemes for 2D color video compression such as MPEG, JPEG, and H.263 cannot be applied directly because the 3D video data contains depth as well as color information. Our goal is to explore from a different angle of the 3D compression space with factors including complexity, compression ratio, quality, and real-time performance. To investigate these trade-offs, we present and evaluate two simple 3D compression schemes. For the first scheme, we use color reduction to compress the color information, which we then compress along with the depth information using zlib. For the second scheme, we use motion JPEG to compress the color information and run-length encoding followed by Huffman coding to compress the depth information. We apply both schemes to 3D videos captured from a real tele-immersive environment. Our experimental results show that: (1) the compressed data preserves enough information to communicate the 3D images effectively (min. PSNR > 40) and (2) even without inter-frame motion estimation, very high compression ratios (avg. > 15) are achievable at speeds suffi..

Real-Time 3D Video Compression for Tele-Immersive Environments

Tele-immersive systems can improve productivity and aid communication by allowing distributed parties to exchange information via a shared immersive experience. The TEEVE research project at the University of Illinois at Urbana-Champaign and the University of California at Berkeley seeks to foster the development and use of tele-immersive environments by a holistic integration of existing components that capture, transmit, and render three-dimensional (3D) scenes in real time to convey a sense of immersive space. However, the transmission of 3D video poses significant challenges. First, it is bandwidth-intensive, as it requires the transmission of multiple large-volume 3D video streams. Second, existing schemes for 2D color video compression such as MPEG, JPEG, and H.263 cannot be applied directly because the 3D video data contains depth as well as color information. Our goal is to explore from a different angle of the 3D compression space with factors including complexity, compression ratio, quality, and real-time performance. To investigate these trade-offs, we present and evaluate two simple 3D compression schemes. For the first scheme, we use color reduction to compress the color information, which we then compress along with the depth information using zlib. For the second scheme, we use motion JPEG to compress the color information and run-length encoding followed by Huffman coding to compress the depth information. We apply both schemes to 3D videos captured from a real tele-immersive environment. Our experimental results show that: (1) the compressed data preserves enough information to communicate the 3D images effectively (min. PSNR > 40) and (2) even without inter-frame motion estimation, very high compression ratios (avg. > 15) are achievable at speeds sufficient to allow real-time communication (avg. ≈ 13 ms per 3D video frame). </p