Migration decision for hybrid mobility in reconfigurable distributed virtual machines
Virtual machine (VM) is an important mechanism to multiplex computer resources. The increasing popularity of network computing has renewed research interests in the adaptive and distributed virtual machines. Service migration is a vital technique to construct reconfigurable VMs. By incorporating mobile agent technology, VM systems can improve their resource utilization, load-balancing and fault-tolerance significantly. This work focuses on the decision problem of hybrid mobility for load-balancing in reconfigurable distributed VMs. We tackle this problem from three aspects: migration candidate determination, migration timing and destination server selection. The service migration timing and destination server selection are formulated as two optimization models. We derive the optimal migration policy for distributed and heterogeneous 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. Our decision policy is complementary to the existing service and agent migration techniques. Its accuracy is verified by simulations.

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: 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: 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: 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: 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: 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: Nearly Completely Positive Graphs
abstract: A non-cp graph G is called nearly vertex (edge) cp if any subgraph of G obtained by deleted a vertex (an edge) of G is completely positive. This paper presents necessary and sufficient conditions for any doubly nonnegative matrix realization A of a nearly cp graph to be completely positive.

title: Finding the Brain Cortex Using Fuzzy Segmentation, Isosurfaces, and Deformable Surface Models
abstract: A method for finding the cortical surface of the brain from magnetic resonance images using a combination of fuzzy segmentation, isosurface extraction, and a deformable surface is presented. After MR images are acquired and preprocessed to remove extracranial tissue, fuzzy membership functions for gray matter, white matter, and cerebrospinal fluid are computed. An iterative procedure using isosurfaces of filtered white matter membership functions is then used to obtain a topologically correct estimate of the cortical surface. This estimate forms the initialization of a deformable surface, which is then allowed to converge to peaks of the gray matter membership function. We demonstrate the results of each step and show the final parameterized map of the medial layer of the cortex.

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.