Acquiring a private computing cloud is the first step that an enterprise would choose to enable the cloud model and get its considerable benefits while keeping the control within the enterprise. The enterprise level applications that provide the infrastructure enabling cloud computing services are typically built by integrating inter-related complex software components. Critical challenges of these applications are the increasing level of inter-component dependencies and the customized growth, which make recurrent deployment of such applications, as the one required in private clouds, labor intensive and error prone. In this paper we investigate the type of issues faced when deploying a cloud computing management infrastructure and propose a solution to self-assist the deployment. We show how by leveraging virtual image technologies we can detect faulty installations and their signatures early in the deployment process. We also propose a methodology to capture in a shared repository and update these signatures for reuse in subsequent deployments in the form of two level signature patterns. We explore the perspective of our solution and criteria of analysis.

Image-based search has become an increasingly active area of research. Despite the fact that many resources have been spent on creating new ideas and improving existing algorithms and designs, existing image-based search engines do not provide scalability or accuracy that is even remotely close to today's text-based search engines. The challenges in image-based search are due to a number of factors, including the computation intensity of existing algorithms and the difficulty of detecting and recognizing objects. In this paper, we present a system called Proxima, which leverages contextual information provided by a mobile device, such as time, location, and user data, to search for people in a stored image database who are "similar" to the person in the input image. Unlike other systems that automatically associate metadata with images, Proxima utilizes the image itself as part of the database query. We also describe a prototype that implements the Proxima algorithm to provide an image-based search service for social networking.

We present the design of E-kernel, an embedding kernel on the Victor V256 message-passing partitionable multiprocessor, developed for the support of program mapping and network reconfiguration. E-kernel supports the embedding of a new network topology onto Victor's 2D mesh and also the embedding of a task graph onto the 2D mesh network or the reconfigured network. In the current implementation, the reconfigured network can be a line or an even-size ring, and the task graphs meshes or tori of a variety of dimensions and shapes or graphs with similar topologies. For application programs having these task graph topologies and that are designed according to the communication model of E-kernel, they can be run without any change on partitions connected by the 2D mesh, line, or ring. Further, E-kernel attempts the communication optimization of these programs on the different networks automatically, thus making both the network topology and the communication optimization attempt completely transparent to the application programs. Many of the embeddings used in E-kernel are optimal or asymptotically optimal (with respect to minimum dilation cost). The implementation of E-kernel translated some of the many theoretical results in graph embeddings into practical tools for program mapping and network reconfiguration in a parallel system. E-kernel is functional on Victor V256. Measurements of E-kernel's performance on V256 are also included.>