14080651
GcpSensor: a CPU performance tool for grid environments
In grid computing environments, CPU performance discovery and allocation is a serious issue. Before they run their applications, grid users need to choose grid nodes based on available CPUs performance. Hence a tool is needed for measuring CPUs performance. To address this need, GcpSensor (grid CPU performance sensor) was developed. GcpSensor runs a typical benchmark program in every grid node to measure CPU performance and returns a metric named WMFLOPS which can be used to compare different CPUs performance directly. An algorithm, which can choose a moderate computation cost dynamically, is designed to ensure that every test is comprehensive and the added overhead is comparatively small. Our experiments have proved that GcpSensor can indicate the CPU performance sensitively and that users can use the GcpSensor data to compare different CPUs performance straightforward, thus the accuracy and efficiency for selecting computing resources are increased.
18861911
Service Identification and Packaging in Service Oriented Reengineering
With the adoption to Web service technologies, more and more existing non-service-oriented software systems turn to be legacy systems. They require a service-oriented reengineering process in order to survive in serviceoriented computing environment. In this paper, we present an architecture-based service-oriented approach to support service-oriented reengineering. It integrates and reuses software components as services over Internet by wrapping underlying computing models with XML. Service identification and service packaging processes are described in details, which are the keys to serviceoriented reengineering. Target services are defined according to service identification, which is based on a comparison of analyses in both problem domain and legacy systems. Clustering techniques are applied in service identification process to analyse recovered architectural information and to identify related modules. Service packaging is benefit from architecture-based development and reuse.
36195188
On the Three Threshold Policy in the Multi-Server Queueing System with Vacations
In this paper, we study an M/M/c queue with a three threshold vacation policy denoted by (e, d, N). With such a policy, the servers keep serving the customers until the number of idle servers reaches d and then e of d servers start taking a vacation together. These e servers keep taking vacations until the number of customers in the system is at least N at a vacation completion instant, then the e servers return to serve the queue again. Using the matrix analytic method, we obtain the stationary performance measures and prove the conditional stochastic decomposition properties for the waiting time and queue length. This model is a generalization of previous multi-server vacation models and offers a useful performance evaluation and system design tool in multi-task server queueing systems.
47187257
Exploring the Performance Benefits of End-to-End Path Switching
This work explores the feasibility of improving the performance of end-to-end data transfers between different sites through path switching. Our study is focused on both the logic that controls path switching decisions and the configurations required to achieve sufficient path diversity. Specifically, we investigate two common approaches offering path diversity multi-homing and overlay networks - and investigate their characteristics in the context of a representative wide-area testbed. We explore the end-to-end delay and loss characteristics of different paths and find that substantial improvements can potentially be achieved by path switching, especially in lowering end-to-end losses. Based on this assessment, we develop a simple path-switching mechanism capable of realizing those performance improvements. Our experimental study demonstrates that substantial performance improvements are indeed achievable using this approach.
62753126
A Bayesian framework for automatic concept discovery in image collections
This paper develops a Bayesian framework for automatic hidden semantic concept discovery to address effective semantics-intensive content-based image retrieval. Each image in the database is segmented to regions associated with homogenous color, texture, and shape features. By employing self-organization map learning, a uniform and sparse region-based representation is obtained. With this representation a probabilistic model based on the statistical-hidden-class assumptions of the image database is developed, to which expectation-maximization technique is applied to analyze semantic concepts hidden in the database. An elaborated retrieval algorithm is designed to support the probabilistic model. The semantic similarity is measured through integrating the posterior probabilities of the transformed query image, as well as a constructed negative vector, to the discovered semantic concepts. The proposed approach has a solid statistical foundation and the experimental evaluations on a database of 10,000 general-purposed images demonstrate its promise of the retrieval effectiveness.
32852823
Restricted Edge Connectivity of Edge Transitive Graphs
1150626
A Flexible New Technique for Camera Calibration
We propose a flexible technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled. The proposed procedure consists of a closed-form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data have been used to test the proposed technique and very good results have been obtained. Compared with classical techniques which use expensive equipment such as two or three orthogonal planes, the proposed technique is easy to use and flexible. It advances 3D computer vision one more step from laboratory environments to real world use.