Object-Based Caching for MPI-IO

As the size of the data sets manipulated by data-intensive scientific applications approaches the petabyte level and beyond, the need for scalable I/O techniques becomes increasingly important and difficult. Much of the research on this issue has been performed within the context ofMPI-IO: the de-facto standard parallel I/O interface for data-intensive applications. Its popularity stems from the fact that MPI-IO provides to applications a rich and flexile parallel I/O API coupled with highly efficient implementations of this API. This problem is being further addressed by the development of powerful parallel I/O subsystems, and state-of-the-art file systems that can efficiently access this infrastructure. However, even with such advances, I/O continues to be a significant bottleneck in application performance.The goal of this research is to provide high-performance I/O for data-intensive applications. A key insight is that a major obstacle in the way of this goal is the legacy view of a file as a linear sequence of bytes. This is because scientific applications rarely access data in a way that matches this file model, using instead what is more accurately described as an object model. In fact, it is the runtime translation between these two data models that is a major contributor to poor I/O performance. To address this issue, this research will develop a more powerful object-based file model for MPI applications, and an object-based caching system to serve as an interface between MPI applications and object-based files. Objects will be carefully defined to encapsulate information about an application\u27s I/O access patterns, and such information will be used to increase the parallelism of file accesses and decrease the cost of maintaining global cache coherence

Using Hybrid Angle/Distance Information for Distributed Topology Control in Vehicular Sensor Networks

In a vehicular sensor network (VSN), the key design issue is how to organize vehicles effectively, such that the local network topology can be stabilized quickly. In this work, each vehicle with on-board sensors can be considered as a local controller associated with a group of communication members. In order to balance the load among the nodes and govern the local topology change, a group formation scheme using localized criteria is implemented. The proposed distributed topology control method focuses on reducing the rate of group member change and avoiding the unnecessary information exchange. Two major phases are sequentially applied to choose the group members of each vehicle using hybrid angle/distance information. The operation of Phase I is based on the concept of the cone-based method, which can select the desired vehicles quickly. Afterwards, the proposed time-slot method is further applied to stabilize the network topology. Given the network structure in Phase I, a routing scheme is presented in Phase II. The network behaviors are explored through simulation and analysis in a variety of scenarios. The results show that the proposed mechanism is a scalable and effective control framework for VSNs

A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data

Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context

HEC: Collaborative Research: SAM^2 Toolkit: Scalable and Adaptive Metadata Management for High-End Computing

The increasing demand for Exa-byte-scale storage capacity by high end computing applications requires a higher level of scalability and dependability than that provided by current file and storage systems. The proposal deals with file systems research for metadata management of scalable cluster-based parallel and distributed file storage systems in the HEC environment. It aims to develop a scalable and adaptive metadata management (SAM2) toolkit to extend features of and fully leverage the peak performance promised by state-of-the-art cluster-based parallel and distributed file storage systems used by the high performance computing community. There is a large body of research on data movement and management scaling, however, the need to scale up the attributes of cluster-based file systems and I/O, that is, metadata, has been underestimated. An understanding of the characteristics of metadata traffic, and an application of proper load-balancing, caching, prefetching and grouping mechanisms to perform metadata management correspondingly, will lead to a high scalability. It is anticipated that by appropriately plugging the scalable and adaptive metadata management components into the state-of-the-art cluster-based parallel and distributed file storage systems one could potentially increase the performance of applications and file systems, and help translate the promise and potential of high peak performance of such systems to real application performance improvements. The project involves the following components: 1. Develop multi-variable forecasting models to analyze and predict file metadata access patterns. 2. Develop scalable and adaptive file name mapping schemes using the duplicative Bloom filter array technique to enforce load balance and increase scalability 3. Develop decentralized, locality-aware metadata grouping schemes to facilitate the bulk metadata operations such as prefetching. 4. Develop an adaptive cache coherence protocol using a distributed shared object model for client-side and server-side metadata caching. 5. Prototype the SAM2 components into the state-of-the-art parallel virtual file system PVFS2 and a distributed storage data caching system, set up an experimental framework for a DOE CMS Tier 2 site at University of Nebraska-Lincoln and conduct benchmark, evaluation and validation studies

An adaptive trust based service quality monitoring mechanism for cloud computing

Cloud computing is the newest paradigm in distributed computing that delivers computing resources over the Internet as services. Due to the attractiveness of cloud computing, the market is currently flooded with many service providers. This has necessitated the customers to identify the right one meeting their requirements in terms of service quality. The existing monitoring of service quality has been limited only to quantification in cloud computing. On the other hand, the continuous improvement and distribution of service quality scores have been implemented in other distributed computing paradigms but not specifically for cloud computing. This research investigates the methods and proposes mechanisms for quantifying and ranking the service quality of service providers. The solution proposed in this thesis consists of three mechanisms, namely service quality modeling mechanism, adaptive trust computing mechanism and trust distribution mechanism for cloud computing. The Design Research Methodology (DRM) has been modified by adding phases, means and methods, and probable outcomes. This modified DRM is used throughout this study. The mechanisms were developed and tested gradually until the expected outcome has been achieved. A comprehensive set of experiments were carried out in a simulated environment to validate their effectiveness. The evaluation has been carried out by comparing their performance against the combined trust model and QoS trust model for cloud computing along with the adapted fuzzy theory based trust computing mechanism and super-agent based trust distribution mechanism, which were developed for other distributed systems. The results show that the mechanisms are faster and more stable than the existing solutions in terms of reaching the final trust scores on all three parameters tested. The results presented in this thesis are significant in terms of making cloud computing acceptable to users in verifying the performance of the service providers before making the selection