Optimal remapping in dynamic bulk synchronous computations via a stochastic control approach
A bulk synchronous computation proceeds in phases that are separated by barrier synchronization. For dynamic bulk synchronous computations that exhibit varying phase-wise computational requirements, remapping at run-time is an effective approach to ensure parallel efficiency. This paper introduces a novel remapping strategy for computations whose workload changes can be modeled as a Markov chain. It is shown that optimal remapping cart be formulated as a binary decision process: remap or not at a given synchronizing instant. The optimal strategy is then developed for long lasted computations by employing optimal stopping rules in a stochastic control framework. The existence of optimal controls is established. Necessary and sufficient conditions for the optimality are obtained. Furthermore, a policy iteration algorithm is devised to reduce computational complexity and enhance fast convergence to the desired optimal control.

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: 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: 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: 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: 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: Snakes, shapes, and gradient vector flow
abstract: Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several two-dimensional (2-D) examples and one three-dimensional (3-D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.

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: An Automated Technique for Statistical Characterization of Brain Tissues in Magnetic Resonance Imaging
abstract: A procedure for estimating the joint probability density function (pdf) of T1, T2 and proton spin density (PD) for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) in the brain is presented. The pdf's have numerous applications, including the study of tissue parameter variability in pathology and across populations. The procedure requires a multispectral, spin echo magnetic resonance imaging (MRI) data set of the brain. It consists of five automated steps: (i) preprocess the data to remove extracranial tissue using a sequence of image processing operators; (ii) estimate T1, T2 and PD by fitting the preprocessed data to an imaging equation; (iii) perform a fuzzy c-means clustering on the same preprocessed data to obtain a spatial map representing the membership value of the three tissue classes at each pixel location; (iv) reject estimates which are not from pure tissue or have poor fits in the parameter estimation, and classify the remaining estimates as either GM, WM or CSF; (v) compute statistics on the classified estimates to obtain a probability mass function and a Gaussian joint pdf of the tissue parameters for each tissue class. Some preliminary results are shown comparing computed pdf's of young, elderly and Alzheimer's subjects. Two brief examples applying the joint pdf's to pulse sequence optimization and generation of computational phantoms are also provided.

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.