15800513
ClusterProbe: an open, flexible and scalable cluster monitoring tool
In this paper, we describe the ClusterProbe, an open, flexible, scalable, and Java-based cluster monitoring tool. The tool provides an open environment by developing a multiple-protocol communication interface that can be connected to various types of external accesses from the clients. ClusterProbe is flexible that the monitoring tool can be easily extended to adapt to the resource changes by using the Java-RMI mechanism. In addition, the design of ClusterProbe allows it to scale up to commodious capacity with its cascading hierarchical architecture. Several useful services are implemented based on ClusterProbe, including the visualization of cluster resource information in various form and cluster fault management. The tool has been used to assist the execution of a cluster-based search engine and a distributed N-body application. All experiments demonstrate high efficiency.
11115305
Graph Scaling: A Technique for Automating Program Construction and Deployment in ClusterGOP
Program development and resource management are critical issues in large-scaled parallel applications and they raise diffculties for the programmers. Automation tools can benefit the programmer by reducing the time and work required for programming, deploying, and managing parallel applications. In our previous work, we have developed a visual tool, VisualGOP, to help visual construction and automatic mapping of parallel programs to execute on the ClusterGOP platform, which provides a graph-oriented model and the environment for running the parallel applications on clusters. In VisualGOP, the programmer needs to manually build the task interaction graph. This may lead to scalability problem for large applications. In this paper, we propose a graph scaling approach that helps the programmer to develop and deploy a large-scale parallel application minimizing the effort of graph construction, task binding and program deployment. The graph scaling algorithms expand or reduce a task graph to match the specified scale of the program and the hardware architecture, e.g., the problem size, the number of processors and interconnection topology, so as to produce an automatic mapping. An example is used to illustrate the proposed approach and how programmer benefits in the automation tools.
1335499
Dynamic configuration management in a graph-oriented Distributed Programming Environment
Dynamic configuration is a desirable property of a distributed system where dynamic modification and extension to the system and the applications are required. It allows the system configuration to be specified and changed while the system is executing. This paper describes a software platform that facilitates a novel approach to the dynamically configurable programming of parallel and distributed applications and systems. This platform is based on a graph-oriented model and it provides support for constructing reconfigurable distributed programs. We describe the design and implementation of a dynamic configuration manager for the graph-oriented distributed programming environment. The requirements and services for dynamic reconfiguration are identified. The architectural design of a dynamic configuration manager is presented, and a parallel virtual machine-based prototypical implementation of the manager, on a local area network of workstations, is described.
14112050
Distributed particle simulation method on adaptive collaborative system
Abstract This paper presents a distributed N-body method based on an adaptive collaborative system model. The collaborative system is formed by the distributed objects on a distributed system. The system can be reconfigured during the computation to fully utilize the computing power of the networked hosts. The method is implemented in Java and RMI to support distributed computing in heterogeneous environment. A distributed tree structure is designed for communication-efficient computation of N-body method. The performance test shows satisfactory speedup and portability of the method on both homogeneous and heterogeneous clusters. The collaborative system model can be used in various applications and it is expandable to wide-area environment.
14338621
A distributed object model for solving irregularly structured problems on cluster
This paper presents a distributed object model MOIDE (Multithreaded Object-oriented Infrastructure on Distributed Environment) for solving irregularly structured problems on cluster. The primary appeal of MOIDE is its flexible system structure that is adaptive to heterogeneous architecture of a cluster. MOIDE integrates the object-oriented and multithreaded methodologies to set up a unified computing environment. Both the shared-data access and remote messaging are incorporated in a two-layer communication mechanism for efficient inter-object communication with the common communication interface. MOIDE supports dynamic load balancing by its autonomous load scheduling technique. A runtime support system implements the MOIDE model as a platform-independent infrastructure for developing and executing irregularly structured applications. N-body, ray tracing, and conjugate gradient applications are implemented to illustrate the advantages of MOIDE model.
61852216
Cross-Evaluation: A new model for information system evaluation
In this article, we introduce a new information system evaluation method and report on its application to a collaborative information seeking system, AntWorld. The key innovation of the new method is to use precisely the same group of users who work with the system as judges, a system we call Cross-Evaluation. In the new method, we also propose to assess the system at the level of task completion. The obvious potential limitation of this method is that individuals may be inclined to think more highly of the materials that they themselves have found and are almost certain to think more highly of their own work product than they do of the products built by others. The keys to neutralizing this problem are careful design and a corresponding analytical model based on analysis of variance. We model the several measures of task completion with a linear model of five effects, describing the users who interact with the system, the system used to finish the task, the task itself, the behavior of individuals as judges, and the self-judgment bias. Our analytical method successfully isolates the effect of each variable. This approach provides a successful model to make concrete the “three-realities” paradigm, which calls for “real tasks,” “real users,” and “real systems.” © 2006 Wiley Periodicals, Inc.
7298536
A multistage fast motion estimation scheme for video compression
This paper presents a novel multistage motion estimation (ME) scheme called content adaptive search technique (CAST). The proposed scheme consists of four stages: motion vector field (MVF) prediction, block-based segmentation, motion parameter extraction, and adaptive search strategy. Through pre-processing the MVF of the previous reference frame in the first three stages, CAST extracts the motion parameters for each region. The 4th stage is a combination of various techniques including MV prediction, search area decision and an adaptive fast search algorithm that is adjusted by a mathematical model for the block distortion surface (BDS). Experiment shows that the proposed scheme improves the visual quality, while yielding a faster speed, comparing with the other predictive ME algorithms.
2615609
EigenNail for Finger Force Direction Recognition
This paper presents a technique termed eigennails to classify fingertip force during contact based on the coloration patterns in the fingernail and surrounding skin. Fingertip force is classified into six directions: no force, normal force only, two directions (left/right) of lateral shear force, and two directions (forward/backward) of longitudinal shear forces. Based on the face recognition technique eigenfaces, a small number of eigennails are sufficient to express the color pattern features for shear force direction classification. Results show that 98% of 960 fingernail images of 8 different subjects are correctly classified. The lowest imaging resolution without sacrificing classification accuracy is found to be 10-by-10.
17387040
Automated judgment of document qualities
The authors report on a series of experiments to automate the assessment of document qualities such as depth and objectivity. The primary purpose is to develop a quality-sensitive functionality, orthogonal to relevance, to select documents for an interactive question-answering system. The study consisted of two stages. In the classifier construction stage, nine document qualities deemed important by information professionals were identified and classifiers were developed to predict their values. In the confirmative evaluation stage, the performance of the developed methods was checked using a different document collection. The quality prediction methods worked well in the second stage. The results strongly suggest that the best way to predict document qualities automatically is to construct classifiers on a person-by-person basis.
16353382
Determining the size of spatial clusters in focused tests: Comparing two methods by means of simulation in a GIS
Abstract. Based on a four-point evaluation system consisting of accuracy, consistency, power, and chance to commit type I errors, this study compares Tango's minimum p (MinP) and Stone's maximum relative risk (MaxRR) methods for detecting focused cluster size through simulations in GIS. It reveals that the MinP method is more effective than the MaxRR method. The MinP method exhibits higher levels of accuracy and consistency; and its power and chance to commit type I errors are similar to the MinP method. The MaxRR method has a tendency to underestimate the cluster size, while the MinP method tends to overestimate the cluster size, particularly when the clusters are relatively big and have high relative risk levels. In addition, the MinP method seems to be most effective in revealing the size of clusters when clusters are neither too strong nor too weak. The lowest detection rates for clustering occur when the clustering signal is relatively weak, which is easily understandable. In practice, it might be useful to use both the methods to estimate a range of possible cluster sizes, where the MaxRR method indicates the lower estimate, while the MinP method gives the higher estimate of the cluster size.