Distributed File System Virtualization Techniques Supporting On-Demand Virtual Machine Environments for Grid Computing
This paper presents a data management solution which allows fast Virtual Machine (VM) instantiation and efficient run-time execution to support VMs as execution environments in Grid computing. It is based on novel distributed file system virtualization techniques and is unique in that: (1) it provides on-demand cross-domain access to VM state for unmodified VM monitors; (2) it enables private file system channels for VM instantiation by secure tunneling and session-key based authentication; (3) it supports user-level and write-back disk caches, per-application caching policies and middleware-driven consistency models; and (4) it leverages application-specific meta-data associated with files to expedite data transfers. The paper reports on its performance in wide-area setups using VMware-based VMs. Results show that the solution delivers performance over 30% better than native NFS and with warm caches it can bring the application-perceived overheads below 10% compared to a local-disk setup. The solution also allows a VM with 1.6 GB virtual disk and 320 MB virtual memory to be cloned within 160 seconds for the first clone and within 25 seconds for subsequent clones.

title: Application-Tailored Cache Consistency for Wide-Area File Systems
abstract: The inability to perform optimizations based on application-specific information presents a hurdle to the deployment of pervasive LAN file systems across WAN environments. This paper proposes a novel approach addressing this problem through application-tailored caching and consistency in widearea file systems. It leverages widely available Network File System (NFS) deployments without any modifications to kernels nor applications, and employs middleware to dynamically establish Grid-wide Virtual File System (GVFS) sessions with application-tailored cache consistency. Two consistency models are discussed in this paper: a relaxed model based on invalidation polling, and a stronger model based on delegation and callback. Experimental evaluation based on microbenchmarks and scientific applications show that with application-tailored cache consistency, GVFS is able to both improve application runtimes and reduce server load significantly, compared to kernel-level NFS in WAN.

title: On the Use of Virtualization and Service Technologies to Enable Grid-Computing
abstract: The In-VIGO approach to Grid-computing relies on the dynamic establishment of virtual grids on which application services are instantiated. In-VIGO was conceived to enable computational science to take place In Virtual Information Grid Organizations. Having its first version deployed on July of 2003, In-VIGO middleware is currently used by scientists from various disciplines, a noteworthy example being the computational nanoelectronics research community (http://www.nanohub.org). All components of an In-VIGO-generated virtual grid – machines, networks, applications and data – are themselves virtual and services are provided for their dynamic creation. This article reviews the In-VIGO approach to Grid-computing and overviews the associated middleware techniques and architectures for virtualizing Grid components, using services for creation of virtual grids and automatically Grid-enabling unmodified applications. The In-VIGO approach to the implementation of virtual networks and virtual application services are discussed as examples of Grid-motivated approaches to resource virtualization and Web-service creation.

title: Autonomic resource management in virtualized data centers using fuzzy logic-based approaches
abstract: Data centers, as resource providers, are expected to deliver on performance guarantees while optimizing resource utilization to reduce cost. Virtualization techniques provide the opportunity of consolidating multiple separately managed containers of virtual resources on underutilized physical servers. A key challenge that comes with virtualization is the simultaneous on-demand provisioning of shared physical resources to virtual containers and the management of their capacities to meet service-quality targets at the least cost. This paper proposes a two-level resource management system to dynamically allocate resources to individual virtual containers. It uses local controllers at the virtual-container level and a global controller at the resource-pool level. An important advantage of this two-level control architecture is that it allows independent controller designs for separately optimizing the performance of applications and the use of resources. Autonomic resource allocation is realized through the interaction of the local and global controllers. A novelty of the local controller designs is their use of fuzzy logic-based approaches to efficiently and robustly deal with the complexity and uncertainties of dynamically changing workloads and resource usage. The global controller determines the resource allocation based on a proposed profit model, with the goal of maximizing the total profit of the data center. Experimental results obtained through a prototype implementation demonstrate that, for the scenarios under consideration, the proposed resource management system can significantly reduce resource consumption while still achieving application performance targets.

title: Supporting application-tailored grid file system sessions with WSRF-based services
abstract: This paper presents novel service-based grid data management middleware that leverages standards defined by WSRF specifications to create and manage dynamic grid file system sessions. A unique aspect of the service is that the sessions it creates can be customized to address application data transfer needs. Application-tailored configurations enable selection of both performance-related features (block-based partial file transfers and/or whole-file transfers, cache parameters and consistency models) and reliability features (file system copy-on-write checkpointing to aid recovery of client-side failures; replication, autonomous failure detection and data access redirection for server-side failures). These enhancements, in addition to cross-domain user identity mapping and encrypted communication, are implemented via user level proxies managed by the service, requiring no changes to existing kernels. Sessions established using the service is mounted as distributed file systems and can be used transparently by unmodified binary applications. The paper analyzes the use of the service to support virtual machine based grid systems and workflow execution, and also reports on the performance and reliability of service managed wide-area file system sessions with experiments based on scientific applications (NanoMOS/Matlab, CHID, GAUSS and SPECseis).

title: Cooperative Autonomic Management in Dynamic Distributed Systems
abstract: The centralized management of large distributed systems is often impractical, particularly when the both the topology and status of the system change dynamically. This paper proposes an approach to application-centric self-management in large distributed systems consisting of a collection of autonomic components that join and leave the system dynamically. Cooperative autonomic components self-organize into a dynamically created overlay network. Through local information sharing with neighbors, each component gains access to global information as needed for optimizing performance of applications. The approach has been validated and evaluated by developing a decentralized autonomic system consisting of multiple autonomic application managers previously developed for the In-VIGO grid-computing system. Using analytical results from complex random network and measurements done in a prototype system, we demonstrate the robustness, self-organization and adaptability of our approach, both theoretically and experimentally.

title: Power Allocation in OFDM-Based Cognitive Radio Systems
abstract: In this paper, we investigate the optimal power allocation strategy that aims at maximizing the capacity in OFDM- based cognitive radio systems. We show that the traditional water-filling algorithm applied in general OFDM systems needs to be modified due to the per subchannel power constraints in such systems. An iterative partitioned water-filling algorithm is proposed and proved to be optimal based on the convex optimization theory.

title: Double-orthogonal coded space-time-frequency spreading CDMA scheme
abstract: In this paper, we propose double-orthogonal coded space-time-frequency spreading code-division multiple-access (DOC-STFS-CDMA) scheme for the downlink orthogonal frequency-division multiplexing (OFDM) systems employing multiple transmit and receive antennas. First, we introduce the so-called STFS-CDMA and the corresponding minimum mean-square error (MMSE) detection algorithms for three scenarios with different user information. Then, we propose a new class of spreading codes, called double-orthogonal code (DOC), to match the MMSE detection algorithms for STFS-CDMA system. With the special double orthogonality, DOC can effectively exploit space, time, and frequency diversity to enhance the performance of MMSE detectors, and provide the well-balanced signal-to-interference-and-noise ratio to different users. Finally, the performance gain of the proposed scheme over conventional schemes is shown by computer simulation.

title: Iterative Turbo Channel Estimation for OFDM System over Rapid Dispersive Fading Channel
abstract: Coherent OFDM detection requires accurate channel state information (CSI). Mobile radio channels are both time and frequency dispersive, especially at high vehicular speeds, which makes channel estimation a challenging problem in system design. Conventional preamble-based and pilot-aided channel estimation require numerous reference signals, which significantly compromises the system throughput. This paper proposes a novel low complexity iterative turbo channel estimation technique which makes use of preamble, pilots and soft decoded data information in an iterative fashion to improve the system performance over the time and frequency selective fading channel while maintaining the system throughput. The numerical and analytical results show that the proposed technique can approach the performance of systems with perfect CSI with much fewer preamble and pilots symbols compared to existing channel estimation methods.

title: MIMO Broadcast Transmission Strategy over Fast Time-Varying Channels
abstract: In time-varying channels, the channel state information available at the transmitter (CSIT) is outdated due to inherent time delay between the uplink channel estimation and the downlink data transmission in TDD systems. In this letter, we propose an iterative precoding method and a linear decoding method which are both based on minimum mean-squared error (MMSE) criteria to mitigate the interference among data streams and users created by outdated CSIT for multiuser MIMO downlink systems. Analysis and simulation results show that the proposed method can effectively reduce the impairment of the outdated CSIT and improve the system capacity.

title: Face Alignment with Unified Subspace Optimization of Active Statistical Models
abstract: Active statistical models including active shape models and active appearance models are very powerful for face alignment. They are composed of two parts: the subspace model(s) and the search process. While these two parts are closely correlated, existing efforts treated them separately and had not considered how to optimize them overall. Another problem with the subspace model(s) is that the two kinds of parameters of subspaces (the number of components and the constraints on the components) are also treated separately. So they are not jointly optimized. To tackle these two problems, an unified subspace optimization method is proposed. This method is composed of two unification aspects: (I) unification of the statistical model and the search process: the subspace models are optimized according to the search procedure; (2) unification of the number of components and the constraints: the two kinds of parameters are modelled in an unified way, such that they can be optimized jointly. Experimental results demonstrate that our method can effectively find the optimal subspace model and significantly improve the performance