GPUs as Storage System Accelerators

Massively multicore processors, such as Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign systems and to explore new ways to engineer them to recalibrate the cost-to-performance relation. This project explores the feasibility of harnessing GPUs' computational power to improve the performance, reliability, or security of distributed storage systems. In this context, we present the design of a storage system prototype that uses GPU offloading to accelerate a number of computationally intensive primitives based on hashing, and introduce techniques to efficiently leverage the processing power of GPUs. We evaluate the performance of this prototype under two configurations: as a content addressable storage system that facilitates online similarity detection between successive versions of the same file and as a traditional system that uses hashing to preserve data integrity. Further, we evaluate the impact of offloading to the GPU on competing applications' performance. Our results show that this technique can bring tangible performance gains without negatively impacting the performance of concurrently running applications.Comment: IEEE Transactions on Parallel and Distributed Systems, 201

Asymptotically-Optimal Incentive-Based En-Route Caching Scheme

Content caching at intermediate nodes is a very effective way to optimize the operations of Computer networks, so that future requests can be served without going back to the origin of the content. Several caching techniques have been proposed since the emergence of the concept, including techniques that require major changes to the Internet architecture such as Content Centric Networking. Few of these techniques consider providing caching incentives for the nodes or quality of service guarantees for content owners. In this work, we present a low complexity, distributed, and online algorithm for making caching decisions based on content popularity, while taking into account the aforementioned issues. Our algorithm performs en-route caching. Therefore, it can be integrated with the current TCP/IP model. In order to measure the performance of any online caching algorithm, we define the competitive ratio as the ratio of the performance of the online algorithm in terms of traffic savings to the performance of the optimal offline algorithm that has a complete knowledge of the future. We show that under our settings, no online algorithm can achieve a better competitive ratio than $\Omega(\log n)$, where $n$ is the number of nodes in the network. Furthermore, we show that under realistic scenarios, our algorithm has an asymptotically optimal competitive ratio in terms of the number of nodes in the network. We also study an extension to the basic algorithm and show its effectiveness through extensive simulations

Online algorithms for content caching: an economic perspective

Content Caching at intermediate nodes, such that future requests can be served without going back to the origin of the content, is an effective way to optimize the operations of computer networks. Therefore, content caching reduces the delivery delay and improves the users’ Quality of Experience (QoE). The current literature either proposes offline algorithms that have complete knowledge of the request profile a priori, or proposes heuristics without provable performance. In this dissertation, online algorithms are presented for content caching in three different network settings: the current Internet Network, collaborative multi-cell coordinated network, and future Content Centric Networks (CCN). Due to the difficulty of obtaining a prior knowledge of contents’ popularities in real scenarios, an algorithm has to make a decision whether to cache a content or not when a request for the content is made, and without the knowledge of any future requests. The performance of the online algorithms is measured through a competitive ratio analysis, comparing the performance of the online algorithm to that of an omniscient optimal offline algorithm. Through theoretical analyses, it is shown that the proposed online algorithms achieve either the optimal or close to the optimal competitive ratio. Moreover, the algorithms have low complexity and can be implemented in a distributed way. The theoretical analyses are complemented with simulation-based experiments, and it is shown that the online algorithms have better performance compared to the state of the art caching schemes

TESTING A CONCEPTUAL FRAMEWORK FOR SELF- CARE IN PERSONS WITH DIABETES: THE EFFECT OF DEPRESSION

Diabetes is a major source of morbidity, mortality, and economic expense. Not only do people with diabetes have a higher risk of developing depression, the rate of depression is much higher than in the general population (ADA, 2010). Depression is believed to influence Diabetes Self Care Management (DSCM), self efficacy, and self care agency. Therefore, the main study aim was to examine the relationships among these factors using a cross-sectional model testing design. The secondary aim was to examine item characteristics and reliability of the Diabetes Self Management Scale (DSMS). A convenience sample of 78 individuals with type 1 or type 2 diabetes mellitus who were taking insulin was recruited. Participants completed five psychometric questionnaires. Path analysis techniques were used to examine relationships among the variables. For the DSMS, item and reliability resulted in a reduced 40-item scale with an alpha of 0.947. The new scale had a strong correlation with self efficacy (r=0.80) which supports the validity of the scale. The results of the path analysis testing showed that depression negatively affected self efficacy (B=-1.43; p<.01; r2=.18) and self care agency (B=0.53; p<.01; r2=.23). The effect of depression on DSCM was completely mediated by self efficacy and self care agency. The findings may indicate that enhancing self efficacy and self-care agency might mitigate the negative impact of depression on DSCM

Long-Term Contrarian profits in the Middle East Market Indices

This paper examines whether there is an existence of a long-term contrarian profits at the Middle East (ME) market indices. This paper shows strong evidence for the long-term contrarian strategy in the Middle East indices. The result of this study demonstrates that the long-term contrarian profits for the Middle East markets can’t be explained by two-factor model. In spite of whether winners are smaller or larger than losers, there are long-term abnormal profits. Finally, the findings in this paper suggest that the long-term contrarian profits may be stronger and more enveloping than is usually understood. KEYWORDS: long-term contrarian, Middle East (ME), market indices, two-factor model.

Syrinx von Hees and Nefeli Papoutsakis (eds.), The Sultan’s Anthologist – Ibn Abī Ḥajālah and His Work, (Arabische Literatur und Rhetorik – Elfhundert bis Achtzehnhundert (ALEA), 436 pp., Baden-Baden: Ergon Verlag 2018, ISBN-13: 978-3956502828.

This publication is with permission of the rights owner (De Gruyter) freely accessible.Peer Reviewe