Optimal periodic remapping of dynamic bulk synchronous computations
Dynamic remapping is critical to the performance of bulk synchronous computations that have non-deterministic behaviors because of the need of barrier synchronization between phases. There are two basic issues in remapping: when and how to remap. This paper presents a formal analysis of the issue of when to remap for dynamic computations with a priori known statistical behaviors, with an objective of finding optimal remapping frequencies for a given tolerance of load imbalance. The problem is formulated as two complement sequential stochastic optimization. Since general optimization techniques tend to reveal stationary properties of the workload process, they are not readily applicable to the analysis of the effect of periodic remapping. Instead, this paper develops new analytical approaches to precisely characterize the transient statistical behaviors of the workload process on both homogeneous and heterogeneous machines. Optimal remapping frequencies are derived for various random workload change processes with known or unknown probabilistic distributions. They are shown accurate via simulations.

title: Decay function model for resource configuration and adaptive allocation on Internet servers
abstract: Server-side resource configuration and allocation for QoS guarantee is a challenge in performance-critical Internet applications. To overcome the difficulties caused by the high-variability and burstiness of Internet traffic, this paper presents a decay function model of request scheduling algorithms for the resource configuration and allocation problem. Under the decay function model, request scheduling is modelled as a transfer-function based filter system that has an input process of requests and an output process of server load. Unlike conventional queueing network models that rely on mean-value analysis for input renewal or Markovian processes, this decay function model works for general time-series based or measurement based processes and hence facilitates the study of statistical correlations between the request traffic, server load, and QoS of requests. Based on the model, we apply filter design theories in signal processing in the optimality analysis of various scheduling algorithms. We reveal a relationship between the server capacity, scheduling policy, service deadline, and other request properties in a formalism and present the optimality condition with respect to the second moments of request properties for an important class of fixed-time scheduling policies. Simulation results verify the relationship and show that optimal fixed-time scheduling can effectively reduce the server workload variance and guarantee service deadlines with high robustness on the Internet.

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: 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: 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: 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: 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 necessary and sufficient condition for a graph G with diameter 5 to be 2-diameter-stable
abstract: A graph G is called an ( l, d )-graph (with respect to edges), if d ( G − E )⩽ d , for each E ⊂ E ( G ) such that | E |⩽ l − 1. The l -diameter-stable graphs are ( l, d )-graphs with diameter d . These two concepts were first proposed by Hartman and Rubin . A necessary and sufficient condition for a graph G with diameter 2 or 3 to be 2-diameter-stable was given in . A necessary and sufficient condition for a graph G with diameter 4 to be 2-diameter-stable was given in . In this paper similar result was obtained for graphs with diameter 5.

title: A topology preserving deformable model using level sets
abstract: Active contour and surface models, also known as deformable models, constitute a class of powerful segmentation techniques. Geometric deformable models implemented via level-set methods have advantages over parametric ones 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 objects to be segmented have a known topology that must be preserved. In this paper, we present a geometric deformable model that preserves topology using the simple point concept from digital topology. This algorithm maintains the other advantages of standard geometric deformable models including sub-pixel accuracy and production of nonintersecting curves (or surfaces). Several experiments on simulated and real data are provided to demonstrate the performance of the proposed algorithm.

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: 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.