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

DC: Small: Energy-aware Coordinated Caching in Cluster-based Storage Systems

The main goal of this project is to improve the performance and energy efficiency of I/O (Input/Output) operations of large-scale cluster computing platforms. The major activities include: 1) characterize the memory access workloads; 2) investigate the new and emerging new storage and memory devices, such as SSD and PCM, on I/O performance. (3) study energy-efficient buffer and cache replacement algorithms, (4) leveraging SSD as a new caching device to improve the energy efficiency and performance of I/O performanc

CSR: Small: Collaborative Research: SANE: Semantic-Aware Namespace in Exascale File Systems

Explosive growth in volume and complexity of data exacerbates the key challenge facing the management of massive data in a way that fundamentally improves the ease and efficacy of their usage. Exascale storage systems in general rely on hierarchically structured namespace that leads to severe performance bottlenecks and makes it hard to support real-time queries on multi-dimensional attributes. Thus, existing storage systems, characterized by the hierarchical directory tree structure, are not scalable in light of the explosive growth in both the volume and the complexity of data. As a result, directory-tree based hierarchical namespace has become restrictive, difficult to use, and limited in scalability for today\u27s large-scale file systems. This project investigates a novel semantic-aware namespace scheme to provide dynamic and adaptive namespace management and support typical file-based operations in Exascale file systems. The project leverages semantic correlations among files and exploits the evolution of metadata attributes to support customized namespace management, with the end goal of efficiently facilitating file identification and end users data lookup. This project provides significant performance improvements for existing file systems in Exascale file systems. Since Exascale file systems constitute one of the backbones of the high-performance computing infrastructure, the semantic-aware techniques also benefits a great number of scientific and engineering data-intensive applications. This project strengthens the ongoing development of high performance computing infrastructures at both UNL and UMaine. The project enhances undergraduate and graduate education at both participating institutions and outreach to K-12 in UMaine via an ongoing NSF-funded ITEST program

DC:Small: Energy-aware Coordinated Caching in Cluster-based Storage Systems

As the computing capacity increases rapidly in large-scale cluster computing platforms, power management becomes an increasingly important concern. This project focuses on the research of reducing disk and memory power consumption through energy-aware cooperative caching in cluster-based storage systems. The project leverages I/O characteristics of scientific applications and dynamic power management features of disk drives and memory chips to reduce I/O energy consumption. This project involves three components: (1) investigate program context based pattern detection to predict I/O activities in the operating systems, (2) investigate disk energy aware cooperative cache management schemes, and (3) prototype the management schemes and incorporate into cluster-based file systems. This project has broader impact through its contributions to the energy-aware computing, graduate education, and undergraduate education via an existing NSF-REU site award

Collaborative Research: HECURA: A New Semantic-Aware Metadata Organization for Improved File-System Performance and Functionality in High-End Computing

Existing data storage systems based on the hierarchical directory-tree organization do not meet the scalability and functionality requirements for exponentially growing datasets and increasingly complex metadata queries in large-scale Exabyte-level file systems with billions of files. This project focuses on a new decentralized semantic-aware metadata organization that exploits semantics of file metadata to improve system scalability, reduce query latency for complex data queries, and enhance file system functionality. The research has four major components: 1) exploit metadata semantic-correlation to organize metadata in a scalable way, 2) exploit the semantic and scalable nature of the new metadata organization to significantly speed up complex queries and improve file system functionality, 3) fully leverage the semantic-awareness of the new metadata organization to optimize storage system designs, such as caching, prefetching, and data de-duplication, and 4) implement the new metadata organization, complex query functions, and system design optimizations in large-scale storage systems. This project has broader impact to data-intensive scientific and engineering applications, graduate and undergraduate education, and K-12 education through its contributions to storage system research and its integration with an existing NSF-REU site award and an NSF-ITEST award

Energy Modeling of Processors in Wireless Sensor Networks Based on Petri Nets

Power minimization is a serious issue in wireless sensor networks to extend the lifetime and minimize costs. However, in order to gain an accurate understanding of issues regarding power minimization, modeling techniques capable of accurately predicting energy consumption are needed. This paper demonstrates that Petri nets are a viable option of modeling a processor. In fact, this paper shows that the Petri nets’ accuracy surpasses a Markov model utilizing supplementary variables to account for constant delays

Energy Modeling of Processors inWireless Sensor Networks based on Petri Nets

Power minimization is a serious issue in wireless sensor networks to extend the lifetime and minimize costs. However, in order to gain an accurate understanding of issues regarding power minimization, modeling techniques capable of accurately predicting energy consumption are needed. This paper demonstrates that Petri nets are a viable option of modeling a processor. In fact, this paper shows that the Petri nets’ accuracy surpasses a Markov model utilizing supplementary variables to account for constant delays

Localization Using Extended Kalman Filters in Wireless Sensor Networks

Introduction: Localization arises repeatedly in many location-aware applications such as navigation, autonomous robotic movement, and asset tracking. Analytical localization methods include triangulation and trilateration. Triangulation uses angles, distances, and trigonometric relationships to locate an object. Trilateration, on the other hand, uses only distance measurements to identify the position of the target