Service migration in distributed virtual machines for adaptive grid computing
Computational grids can integrate geographically distributed resources into a seamless environment. To facilitate managing these heterogeneous resources, the virtual machine technology provides a powerful layer of abstraction and allows multiple applications to multiplex the resources of a grid computer. On the other hand, the grid dynamics requires the virtual machine system be distributed and reconfigurable. However, the existing migration approaches only move the execution entities, such as processes, threads, and mobile agents, among servers and leave the runtime services behind. They are not potent to achieve service reconfiguration in face of server overload or failures. In this paper, we propose a service migration mechanism, which moves the computational services of a virtual server, for instance a shared array runtime support system, to available servers for adaptive grid computing. In this way, parallel jobs can resume computation on a remote server without requiring service preinstallation. As an illustration of the service migration mechanism, we incorporated it into a Java-compliant distributed virtual machine, DSA, and formed a Mobile DSA (M-DSA) to accommodate adaptive parallel applications in grids. We measured the performance of M-DSA in the execution of applications from the SPLASH-2 benchmark suite on a campus grid. Experimental results show that service migration can achieve system adaptivity effectively.

title: Energy-Aware Modeling and Scheduling for Dynamic Voltage Scaling with Statistical Real-Time Guarantee
abstract: Dynamic voltage scaling (DVS) is a promising technique for battery-powered systems to conserve energy consumption. Most existing DVS algorithms assume information about task periodicity or a priori knowledge about the task set to be scheduled. This paper presents an analytical model of general tasks for DVS assuming job timing information is known only after a task release. It models the voltage scaling process as a transfer function-based filtering system, which facilitates the design of two efficient scaling algorithms. The first is a time-invariant scaling policy and it is proved to be a generalization of several popular DVS algorithms for periodic, sporadic, and aperiodic tasks. A more energy efficient policy is a time-variant scaling algorithm for aperiodic tasks. It is optimal in the sense that it is online without assumed information about future task releases. The algorithm turns out to be a water-filling process with a linear time complexity. It can be applied to scheduling based on worst-case execution times as well as online slack distribution when jobs complete earlier. We further establish two relationships between computation capacity and deadline misses to provide a statistical real-time guarantee with reduced capacity

title: sMonitor: A Non-Intrusive Client-Perceived End-to-End Performance Monitor of Secured Internet Services
abstract: End-to-end performance measurement is fundamental to building high-performance Internet services. While many Internet services often operate using HTTP over SSL/TLS, current monitors are limited to plaintext HTTP services. This paper presents sMonitor, a non-intrusive server-side end-to-end performance monitor that can monitor HTTPS services. The monitor passively collects live packet traces from a server site. It then uses a size-based analysis method on HTTP requests to infer characteristics of client accesses and measures client-perceived pageview response time in real time. We designed and implemented a prototype of sMonitor. Preliminary evaluations show measurement error of less than 5%.

title: Personalized email management at network edges
abstract: A new technique for managing and disseminating Web-based email prefetches messages and generates dynamic pages, displaying them at the network edge. Compared to other popular Web-based email servers, the prefetching and caching emails (PACE) prototype shows an improved performance with respect to user-perceived latency. Additionally, PACE'S centralized neural-network-based personalized spam filter will filter spam and viruses at the server's origin, thus saving bandwidth. Another major concern for users is the email accounts being clogged with spam. Spam filters can be classified as server-side or client-side. Server-side filters are integrated with email servers and filter out spam at the server end.

title: Hash-based proximity clustering for load balancing in heterogeneous DHT networks
abstract: DHT networks based on consistent hashing functions have an inherent load uneven distribution problem. The objective of DHT load balancing is to balance the workload of the network nodes in proportion to their capacity so as to eliminate traffic bottleneck. It is challenging because of the dynamism nature of DHT networks and time-varying load characteristics. In this paper, we present a hash-based proximity clustering approach for load balancing in heterogeneity DHTs. In the approach, DHT nodes are classified as regular nodes and supernodes according to their computing and networking capacities. Regular nodes are grouped and associated with supernodes via consistent hashing of their physical proximity information on the Internet. The supernodes form a self-organized and churn resilient auxiliary network for load balancing. The hierarchical structure facilitates the design and implementation of a locality-aware randomized load balancing algorithm. The algorithm introduces a factor of randomness in the load balancing processes in a range of neighborhood so as to deal with both the proximity and dynamism. Simulation results show the superiority of the approach, in comparison with a number of other DHT load balancing algorithms. The approach performs no worse than existing proximity-aware algorithms and exhibits strong resilience to the effect of churn. It also greatly reduces the overhead of resilient randomized load balancing algorithms due to the use of proximity information.

title: Stochastic modeling and analysis of hybrid mobility in reconfigurable distributed virtual machines
abstract: Virtualization provides a vehicle to manage the available resources and enhance their utilization in network computing. System dynamics requires virtual machines be distributed and reconfigurable. To construct reconfigurable distributed virtual machines, service migration moves the runtime services among physical servers when necessary. By incorporating the mobile agent technology, distributed virtual machines can improve their resource utilization and service availability significantly. This paper focuses on finding the optimal migration policies for service and agent migrations for high throughput in reconfigurable distributed virtual machines. We analyze three issues of this decision problem: migration candidate determination, migration timing and destination server selection. The service migration timing and destination server selection are formulated by two optimization models. We derive the optimal migration policy for distributed and heterogenous systems based on stochastic optimization theories. Renewal processes are applied to model the dynamics of migration. We solve the agent migration problem by dynamic programming and extend the optimal service migration decision by considering the interplay of the hybrid mobility. We verify the accuracy of our migration decision policy in simulations.

title: A flexible and recoverable client/server database event notification system
abstract: A software architecture is presented that allows client application programs to interact with a DBMS server in a flexible and powerful way, using either direct, volatile messages, or messages sent via recoverable queues. Normal requests from clients to the server and replies from the server to clients can be transmitted using direct or recoverable messages. In addition, an application event notification mechanism is provided, whereby client applications running anywhere on the network can register for events, and when those events are raised, the clients are notified. A novel parameter passing mechanism allows a set of tuples to be included in an event notification. The event mechanism is particularly useful in an active DBMS, where events can be raised by triggers to signal running application programs.

title: Reconstruction of the Central Layer of the Human Cerebral Cortex from MR Images
abstract: Reconstruction of the human cerebral cortex from MR images is a fundamental step in human brain mapping and in applications such as surgical path planning. In a previous paper, we described a method for obtaining a surface representation of the central layer of the human cerebral cortex using fuzzy segmentation and a deformable surface model. This method, however, suffers from several problems. In this paper, we significantly improve upon the previous method by using a fuzzy segmentation algorithm robust to intensity inhomogeneities, and using a deformable surface model specifically designed for capturing convoluted sulci or gyri. We demonstrate the improvement over the previous method both qualitatively and quantitatively, and show the result of its application to six subjects. We also experimentally validate the convergence of the deformable surface initialization algorithm.

title: A 2D moving grid geometric deformable model
abstract: Geometric deformable models based on the level set method have become very popular. To overcome an inherent limitation in accuracy while maintaining computational efficiency, adaptive grid techniques using local grid refinement have been developed for use with these models. However, this strategy requires a very complex data structure, yields large numbers of contour points, and is inconsistent with our previously presented topology-preserving geometric deformable model (TGDM). In this paper, we incorporate an alternative adaptive grid technique called the moving grid method into the geometric deformable model framework. We find that it is simpler to implement than grid refinement, requiring no large, complex, hierarchical data structures. It also limits the number of contour vertices in the final contour and supports the incorporation of the topology-preserving constraint of TGDM. After presenting the algorithm, we demonstrate its performance using both simulated and real images.

title: A Topology Preserving Level Set Method for Geometric Deformable Models
abstract: Active contour and surface models, also known as deformable models, are powerful image segmentation techniques. Geometric deformable models implemented using level set methods have advantages over parametric models due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models-the ability to automatically handle topology changes-turns out to be a liability in applications where the object to be segmented has a known topology that must be preserved. We present a new class of geometric deformable models designed using a novel topology-preserving level set method, which achieves topology preservation by applying the simple point concept from digital topology. These new models maintain the other advantages of standard geometric deformable models including subpixel accuracy and production of nonintersecting curves or surfaces. Moreover, since the topology-preserving constraint is enforced efficiently through local computations, the resulting algorithm incurs only nominal computational overhead over standard geometric deformable models. Several experiments on simulated and real data are provided to demonstrate the performance of this new deformable model algorithm.

title: Automated Segmentation of Sulcal Regions
abstract: Automatic segmentation and identification of cortical sulci play an important role in the study of brain structure and function. In this work, a method is presented for the automatic segmentation of sulcal regions of cortex. Unlike previous methods that extract the sulcal spaces within the cortex, the proposed method extracts actual regions of the cortical surface that surround sulci. Sulcal regions are segmented from the medial surface as well as the lateral and inferior surfaces. The method first generates a depth map on the surface, computed by measuring the distance between the cortex and an outer “shrink-wrap” surface. Sulcal regions are then extracted using a hierarchical algorithm that alternates between thresholding and region growing operations. To visualize the buried regions of the segmented cortical surface, an efficient technique for mapping the surface to a sphere is proposed. Preliminary results are presented on the geometric analysis of sulcal regions for automated identification.