On the Use of Fuzzy Modeling in Virtualized Data Center Management
One of the most important goals of data-center management is to reduce cost through efficient use of resources. Virtualization techniques provide the opportunity of carving individual physical servers into multiple virtual containers that can be run and managed separately. A key challenge that comes with virtualization is the simultaneous on-demand provisioning of shared 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 with local controllers at the virtual-container level and a global controller at the resource-pool level. Autonomic resource allocation is realized through the interaction of the local and global controllers. A novelty of the controller designs is their use of fuzzy logic to efficiently and robustly deal with the complexity of the virtualized data center and the uncertainties of the dynamically changing workloads. 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: 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: 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: Towards Real-Time Distributed Signal Modeling for Brain-Machine Interfaces
abstract: New architectures for Brain-Machine Interface communication and control use mixture models for expanding rehabilitation capabilities of disabled patients. Here we present and test a dynamic data-driven (BMI) Brain-Machine Interface architecture that relies on multiple pairs of forward-inverse models to predict, control, and learn the trajectories of a robotic arm in a real-time closed-loop system. A method of window-RLS was used to compute the forward-inverse model pairs in real-time and a model switching mechanism based on reinforcement learning was used to test the ability to map neural activity to elementary behaviors. The architectures were tested with in vivodata and implemented using remote computing resources.

title: A user-level secure grid file system
abstract: A grid-wide distributed file system provides convenient data access interfaces that facilitate fine-grained cross-domain data sharing and collaboration. However, existing widely-adopted distributed file systems do not meet the security requirements for grid systems. This paper presents a Secure Grid File System (SGFS) which supports GSI-based authentication and access control, end-to-end message privacy, and integrity. It employs user-level virtualization of NFS to provide transparent grid data access leveraging existing, unmodified clients and servers. It supports user and application-tailored security customization per SGFS session, and leverages secure management services to control and configure the sessions. The system conforms to the GSI grid security infrastructure and allows for seamless integration with other grid middleware. A SGFS prototype is evaluated with both file system benchmarks and typical applications, which demonstrates that it can achieve strong security with an acceptable overhead, and substantially outperform native NFS in wide-area environments by using disk caching.

title: Robust Transmission for Multiuser MIMO Downlink Systems with Imperfect CSIT
abstract: The performance of multiuser MIMO (MU-MIMO) downlink systems with preceding and decoding method by minimizing the total mean square error (TMSE) of all data streams of all users (T-MMSE) depends on the accuracy of the channel state information (CSI) available at the transmitter (CSIT). In this paper, a robust transmission scheme is proposed to suppress the interferences among data streams and users caused by imperfect CSIT. At the transmitter, the precoders are obtained through an iterative procedure by minimizing the expectation of the TMSE of all data streams of all users conditioned on the estimated CSI under a total transmit power constraint. At each user, a linear decoder is calculated based on the minimum mean square error (MMSE) criteria according to the realtime CSI. To fulfill it, a new pilot-sending architecture is introduced. Numerical results demonstrate that the proposed scheme is robust not only to the channel uncertainty at the transmitter but also to the channel uncertainty at each user, and can effectively improve the system performance.

title: Subspace analysis and optimization for AAM based face alignment
abstract: Active appearance models (AAM) is very powerful for extracting objects, e.g. faces, from images. It is composed of two parts: the AAM subspace model and the AAM search. While these two parts are closely correlated, existing efforts treated them separately and had not considered how to optimize them overall. In this paper, an approach is proposed to optimize the subspace model while considering the search procedure. We first perform a subspace error analysis, and then to minimize the AAM error we propose an approach which optimizes the subspace model according to the search procedure. For the subspace error analysis, we decomposed the subspace error into two parts, which are introduced by the subspace model and the search procedure respectively. This decomposition shows that the optimal results of AAM can be achieved only by optimizing both of them jointly rather than separately. Furthermore, based on this error decomposition, we develop a method to find the optimal subspace model according to the search procedure by considering both the two decomposed errors. Experimental results demonstrate that our method can find the optimal AAM subspace model rapidly and improve the performance of AAM significantly.

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: The Impacts of Radio Channels and Node Mobility on Link Statistics in Mobile Ad Hoc Networks
abstract: Understanding link statistics in mobile ad hoc networks (MANETs) is essential to design adaptive routing protocols and achieve desired network performance. While much attention has been given to the node mobility impacts, little has been done to investigate the influence of dynamic channel fadings and the joint effects of the interactions among radio channels, transmission range, node mobility and node-pair distance on link statistics. In this paper, we investigate the link stability and availability by using a distance transition probability matrix of a relative distance between two nodes. Our analysis takes the node effective transmission range Re into account. The relative node movement is based on the Semi-Markov Smooth (SMS) mobility model  which captures the smooth node speed (V) transition and the radio channel variations in a small time- scale. We show that the PDF of link lifetime in MANETs can be effectively approximated by the exponential distribution characterized by the parameter V/Re. Moreover, we And that the impacting factors on residual link lifetime are in the decreasing order of node speed, transmission range, node-pair distance. The analytical results are validated by extensive simulations.

title: Linear Precoding for V-BLAST Systems in the Presence of Fading Correlations
abstract: This letter proposed a linear precoding scheme for the V-BLAST system that requires only knowledge of the statistical CSI; the transmitter does not need the instantaneous CSI. Power allocation on the eigenmodes of the transmit correlation matrix is one way to minimize bit error rate (BER). Simulation results show that the proposed precoding V-BLAST system provides a significant reduction in the BER compared with the conventional V-BLAST systems.