A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Improving the Performance and Endurance of Persistent Memory with Loose-Ordering Consistency

Persistent memory provides high-performance data persistence at main memory. Memory writes need to be performed in strict order to satisfy storage consistency requirements and enable correct recovery from system crashes. Unfortunately, adhering to such a strict order significantly degrades system performance and persistent memory endurance. This paper introduces a new mechanism, Loose-Ordering Consistency (LOC), that satisfies the ordering requirements at significantly lower performance and endurance loss. LOC consists of two key techniques. First, Eager Commit eliminates the need to perform a persistent commit record write within a transaction. We do so by ensuring that we can determine the status of all committed transactions during recovery by storing necessary metadata information statically with blocks of data written to memory. Second, Speculative Persistence relaxes the write ordering between transactions by allowing writes to be speculatively written to persistent memory. A speculative write is made visible to software only after its associated transaction commits. To enable this, our mechanism supports the tracking of committed transaction ID and multi-versioning in the CPU cache. Our evaluations show that LOC reduces the average performance overhead of memory persistence from 66.9% to 34.9% and the memory write traffic overhead from 17.1% to 3.4% on a variety of workloads.Comment: This paper has been accepted by IEEE Transactions on Parallel and Distributed System

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

Secure Virtualization and Multicore Platforms State-of-the-Art report

SVaM

A Survey of Techniques for Improving Security of GPUs

Graphics processing unit (GPU), although a powerful performance-booster, also has many security vulnerabilities. Due to these, the GPU can act as a safe-haven for stealthy malware and the weakest `link' in the security `chain'. In this paper, we present a survey of techniques for analyzing and improving GPU security. We classify the works on key attributes to highlight their similarities and differences. More than informing users and researchers about GPU security techniques, this survey aims to increase their awareness about GPU security vulnerabilities and potential countermeasures

BAG : Managing GPU as buffer cache in operating systems

This paper presents the design, implementation and evaluation of BAG, a system that manages GPU as the buffer cache in operating systems. Unlike previous uses of GPUs, which have focused on the computational capabilities of GPUs, BAG is designed to explore a new dimension in managing GPUs in heterogeneous systems where the GPU memory is an exploitable but always ignored resource. With the carefully designed data structures and algorithms, such as concurrent hashtable, log-structured data store for the management of GPU memory, and highly-parallel GPU kernels for garbage collection, BAG achieves good performance under various workloads. In addition, leveraging the existing abstraction of the operating system not only makes the implementation of BAG non-intrusive, but also facilitates the system deployment