Vipin Chaudhary

History-based access control for mobile code

In this paper, we present a history-based access-control mechanism that is suitable for mediating accesses from mobile code. The key idea behind history-based access-control is to maintain a selective history of the access requests made by individual programs and to use this history to improve the differentiation between safe and potentially dangerous requests. What a program is allowed to do depends on its own behavior and identity and not the location it was loaded from or the identity of its author/provider. History-based access-control has the potential to significantly expand the set of programs that can be executed without compromising security or ease of use. We describe the design and implementation of Deeds, a history-based access-control mechanism for Java. Access-control policies for Deeds are written in Java, and can be updated while the programs whose accesses are being mediated are still executing.

VMeter: Power modelling for virtualized clouds

Datacenters are seeing unprecedented growth in recent years. The energy requirements to operate these large scale facilities are increasing significantly both in terms of operation cost as well as their indirect impact on ecology due to high carbon emissions. There are several ongoing research efforts towards the development of an integrated cloud management system to provide comprehensive online monitoring of resource utilization along with the implementation of power-aware policies to reduce the total energy consumption. However, most of these techniques provide online power monitoring based on the power consumption of a physical node running one or more Virtual Machines (VM). They lack a fine-grained mechanism to profile the power of an individual hosted VM. In this work we present a novel power modelling technique, VMeter, based on online monitoring of system-resources having high correlation with the total power consumption. The monitored system sub-components include: CPU, cache, disk, and DRAM. The proposed model predicts instantaneous power consumption of an individual VM hosted on a physical node besides the full system power consumption. Our model is validated using computationally diverse and industry standard benchmark programs. Our evaluation results show that our model is able to predict instantaneous power with an average mean and median accuracy of 93% and 94%, respectively, against the actual measured power using an externally attached power meter.

A Comparison of Virtualization Technologies for HPC

Virtualization is a common strategy for improving the utilization of existing computing resources, particularly within data centers. However, its use for high performance computing (HPC) applications is currently limited despite its potential for both improving resource utilization as well as providing resource guarantees to its users. This paper systematically evaluates various VMs for computationally intensive HPC applications using various standard benchmarks. Using VMWare Server, xen, and OpenVZ we examine the suitability of full virtualization, paravirtualization, and operating system-level virtualization in terms of network utilization SMP performance, file system performance, and MPI scalability. We show that the operating system-level virtualization provided by OpenVZ provides the best overall performance, particularly for MPI scalability.

A generalized scheme for mapping parallel algorithms

A generalized mapping strategy that uses a combination of graph theory, mathematical programming, and heuristics is proposed. The authors use the knowledge from the given algorithm and the architecture to guide the mapping. The approach begins with a graphical representation of the parallel algorithm (problem graph) and the parallel computer (host graph). Using these representations, the authors generate a new graphical representation (extended host graph) on which the problem graph is mapped. An accurate characterization of the communication overhead is used in the objective functions to evaluate the optimality of the mapping. An efficient mapping scheme is developed which uses two levels of optimization procedures. The objective functions include minimizing the communication overhead and minimizing the total execution time which includes both computation and communication times. The mapping scheme is tested by simulation and further confirmed by mapping a real world application onto actual distributed environments. >

Labeling of Lumbar Discs Using Both Pixel- and Object-Level Features With a Two-Level Probabilistic Model

Backbone anatomical structure detection and labeling is a necessary step for various analysis tasks of the vertebral column. Appearance, shape and geometry measurements are necessary for abnormality detection locally at each disc and vertebrae (such as herniation) as well as globally for the whole spine (such as spinal scoliosis). We propose a two-level probabilistic model for the localization of discs from clinical magnetic resonance imaging (MRI) data that captures both pixel- and object-level features. Using a Gibbs distribution, we model appearance and spatial information at the pixel level, and at the object level, we model the spatial distribution of the discs and the relative distances between them. We use generalized expectation-maximization for optimization, which achieves efficient convergence of disc labels. Our two-level model allows the assumption of conditional independence at the pixel-level to enhance efficiency while maintaining robustness. We use a dataset that contains 105 MRI clinical normal and abnormal cases for the lumbar area. We thoroughly test our model and achieve encouraging results on normal and abnormal cases.

Lumbar Disc Localization and Labeling with a Probabilistic Model on Both Pixel and Object Features

Repeatable, quantitative assessment of intervertebral disc pathology requires accurate localization and labeling of the lumbar region discs. To that end, we propose a two-level probabilistic model for such disc localization and labeling. Our model integrates both pixel-level information, such as appearance, and object-level information, such as relative location. Utilizing both levels of information adds robustness to the ambiguous disc intensity signature and high structure variation. Yet, we are able to do efficient (and convergent) localization and labeling with generalized expectation-maximization. We present accurate results on 20 normal cases (96%) and a promising extension to a pathology case.

Evaluating the use of GPUs in liver image segmentation and HMMER database searches

In this paper we present the results of parallelizing two life sciences applications, Markov random fields-based (MRF) liver segmentation and HMMERs Viterbi algorithm, using GPUs. We relate our experiences in porting both applications to the GPU as well as the techniques and optimizations that are most beneficial. The unique characteristics of both algorithms are demonstrated by implementations on an NVIDIA 8800 GTX Ultra using the CUDA programming environment. We test multiple enhancements in our GPU kernels in order to demonstrate the effectiveness of each strategy. Our optimized MRF kernel achieves over 130× speedup, and our hmmsearch implementation achieves up to 38× speedup. We show that the differences in speedup between MRF and hmmsearch is due primarily to the frequency at which the hmmsearch must read from the GPUs DRAM.

Utilizing OFDM Guard Interval for Spectrum Sensing

Spectrum sensing is crucial for dynamic spectrum management systems. In this paper, we propose a scheme that utilizes the guard interval of OFDM symbol at the transmitter for spectrum sensing. The cyclic prefix is not inserted in the guard interval at the transmitter, whereas the circulant convolution is secured at the OFDM receiver through the proposed mechanism. Simulation results show that the scheme can be implemented with no impact on the BER under various channel conditions, and detection of incumbent DTV signal is possible in the OFDM guard interval. In addition, we develop enhancements to the circulant convolution preserving mechanism for handling the transceiver imperfections in practice.

A Robust Iris Localization Method Using an Active Contour Model and Hough Transform

Iris segmentation is one of the crucial steps in building an iris recognition system since it affects the accuracy of the iris matching significantly. This segmentation should accurately extract the iris region despite the presence of noises such as varying pupil sizes, shadows, specular reflections and highlights. Considering these obstacles, several attempts have been made in robust iris localization and segmentation. In this paper, we propose a robust iris localization method that uses an active contour model and a circular Hough transform. Experimental results on 100 images from CASIA iris image database show that our method achieves 99% accuracy and is about 2.5 times faster than the Daugman’s in locating the pupillary and the limbic boundaries.

Extending OpenMP for heterogeneous chip multiprocessors

The emergence of system-on-chip (SOC) design shows the growing popularity of the integration of multiple-processors into one chip. We propose that high-level abstraction of parallel programming like OpenMP is suitable for chip multiprocessors. For SOCs, the heterogeneity exists within one chip such that it may have different types of multiprocessors, e.g. RISC-like processors or DSP-like processors. Incorporating different processors into OpenMP is challenging. We present our solutions to extend OpenMP directives to tackle this heterogeneity. Several optimization techniques are proposed to utilize advanced architecture features of our target SOC, the software scalable system on chip (3SoC). Preliminary performance evaluation shows scalable speedup using different types of processors and performance improvement through individual optimization.