Distributed Operating Systems

Distributed operating systems have many aspects in common with centralized ones, but they also differ in certain ways. This paper is intended as an introduction to distributed operating systems, and especially to current university research about them. After a discussion of what constitutes a distributed operating system and how it is distinguished from a computer network, various key design issues are discussed. Then several examples of current research projects are examined in some detail, namely, the Cambridge Distributed Computing System, Amoeba, V, and Eden. © 1985, ACM. All rights reserved

Resource sharing across heterogenous networks

Sharing resources on a computer network, especially in heterogeneous environments, has m any benefits: new applications become possible, and use of technology cheaper. This dissertation investigates how resources— in particular printing resources—may b e shared. While still incomplete, an existing theoretical framework for data communication and resource sharing, the ISO-051 Reference Model, provides useful background information and tools for analysis. A discussion o f this framework complements a survey o f the principles and current state of file and printer servers, and distributed systems. An analysis of the design and implementation of a printer server acting as a b ridge between two networks illustrates problem s and results found in distributed system s generally. The dissertation concludes by analyzing the strengths and shortcomings of the Reference Model and distributed systems. This and developments in technology lead to a proposal of an extended model for printer services, and clarification of printer servers' needs and requirements

The BG News February 26, 2001

The BGSU campus student newspaper February 26, 2001. Volume 86 - Issue 104https://scholarworks.bgsu.edu/bg-news/7769/thumbnail.jp

File Access Performance of Diskless Workstations

This paper studies the performance of single-user workstations that access files remotely over a local area network. From the environmental, economic, and administrative points of view, workstations that are diskless or that have limited secondary storage are desirable at the present time. Even with changing technology, access to shared data will continue to be important. It is likely that some performance penalty must be paid for remote rather than local file access. Our objectives are to assess this penalty and to explore a number of design alternatives that can serve to minimize it. Our approach is to use the results of measurement experiments to parameterize queuing network performance models. These models then are used to assess performance under load and to evahrate design alternatives. The major conclusions of our study are: (1) A system of diskless workstations with a shared file server can have satisfactory performance. By this, we mean performance comparable to that of a local disk in the lightly loaded case, and the ability to support substantial numbers of client workstations without significant degradation. As with any shared facility, good design is necessary to minimize queuing delays under high load. (2) The key to efficiency is protocols that allow volume transfers at every interface (e.g., between client and server, and between disk and memory at the server) and at every level (e.g., between client and server at the level of logical request/response and at the level of local area network packet size). However, the benefits of volume transfers are limited to moderate sizes (8-16 kbytes) by several factors. (3) From a performance point of view, augmenting the capabilities of the shared file server may be more cost effective than augmenting the capabilities of the client workstations. (4) Network contention should not be a performance problem for a lo-Mbit network and 100 active workstations in a software development environment

A study of linear synchronous motors in the tractive and levitative modes

SIGLEAvailable from British Library Document Supply Centre- DSC:DX82374 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Theoretical and computational modeling of rna-ligand interactions

Ribonucleic acid (RNA) is a polymeric nucleic acid that plays a variety of critical roles in gene expression and regulation at the level of transcription and translation. Recently, there has been an enormous interest in the development of therapeutic strategies that target RNA molecules. Instead of modifying the product of gene expression, i.e., proteins, RNAtargeted therapeutics aims to modulate the relevant key RNA elements in the disease-related cellular pathways. Such approaches have two significant advantages. First, diseases with related proteins that are difficult or unable to be drugged become druggable by targeting the corresponding messenger RNAs (mRNAs) that encode the amino acid sequences. Second, besides coding mRNAs, the vast majority of the human genome sequences are transcribed to noncoding RNAs (ncRNAs), which serve as enzymatic, structural, and regulatory elements in cellular pathways of most human diseases. Targeting noncoding RNAs would open up remarkable new opportunities for disease treatment. The first step in modeling the RNA-drug interaction is to understand the 3D structure of the given RNA target. With current theoretical models, accurate prediction of 3D structures for large RNAs from sequence remains computationally infeasible. One of the major challenges comes from the flexibility in the RNA molecule, especially in loop/junction regions, and the resulting rugged energy landscape. However, structure probing techniques, such as the “selective 20-hydroxyl acylation analyzed by primer extension” (SHAPE) experiment, enable the quantitative detection of the relative flexibility and hence structure information of RNA structural elements. Therefore, one may incorporate the SHAPE data into RNA 3D structure prediction. In the first project, we investigate the feasibility of using a machine-learning-based approach to predict the SHAPE reactivity from the 3D RNA structure and compare the machine-learning result to that of a physics-based model. In the second project, in order to provide a user-friendly tool for RNA biologists, we developed a fully automated web interface, “SHAPE predictoR” (SHAPER) for predicting SHAPE profile from any given 3D RNA structure. In a cellular environment, various factors, such as metal ions and small molecules, interact with an RNA molecule to modulate RNA cellular activity. RNA is a highly charged polymer with each backbone phosphate group carrying one unit of negative (electronic) charge. In order to fold into a compact functional tertiary structure, it requires metal ions to reduce Coulombic repulsive electrostatic forces by neutralizing the backbone charges. In particular, Mg2+ ion is essential for the folding and stability of RNA tertiary structures. In the third project, we introduce a machine-learning-based model, the “Magnesium convolutional neural network” (MgNet) model, to predict Mg2+ binding site for a given 3D RNA structure, and show the use of the model in investigating the important coordinating RNA atoms and identifying novel Mg2+ binding motifs. Besides Mg2+ ions, small molecules, such as drug molecules, can also bind to an RNA to modulate its activities. Motivated by the tremendous potential of RNA-targeted drug discovery, in the fourth project, we develop a novel approach to predicting RNA-small molecule binding. Specifically, we develop a statistical potential-based scoring/ranking method (SPRank) to identify the native binding mode of the small molecule from a pool of decoys and estimate the binding affinity for the given RNA-small molecule complex. The results tested on a widely used data set suggest that SPRank can achieve (moderately) better performance than the current state-of-art models

Flexible and efficient computation in large data centres

Increasingly, online computer applications rely on large-scale data analyses to offer personalised and improved products. These large-scale analyses are performed on distributed data processing execution engines that run on thousands of networked machines housed within an individual data centre. These execution engines provide, to the programmer, the illusion of running data analysis workflows on a single machine, and offer programming interfaces that shield developers from the intricacies of implementing parallel, fault-tolerant computations. Many such execution engines exist, but they embed assumptions about the computations they execute, or only target certain types of computations. Understanding these assumptions involves substantial study and experimentation. Thus, developers find it difficult to determine which execution engine is best, and even if they did, they become “locked in” because engineering effort is required to port workflows. In this dissertation, I first argue that in order to execute data analysis computations efficiently, and to flexibly choose the best engines, the way we specify data analysis computations should be decoupled from the execution engines that run the computations. I propose an architecture for decoupling data processing, together with Musketeer, my proof-of-concept implementation of this architecture. In Musketeer, developers express data analysis computations using their preferred programming interface. These are translated into a common intermediate representation from which code is generated and executed on the most appropriate execution engine. I show that Musketeer can be used to write data analysis computations directly, and these can execute on many execution engines because Musketeer automatically generates code that is competitive with optimised hand-written implementations. The diverse execution engines cause different workflow types to coexist within a data centre, opening up both opportunities for sharing and potential pitfalls for co-location interference. However, in practice, workflows are either placed by high-quality schedulers that avoid co-location interference, but choose placements slowly, or schedulers that choose placements quickly, but with unpredictable workflow run time due to co-location interference. In this dissertation, I show that schedulers can choose high-quality placements with low latency. I develop several techniques to improve Firmament, a high-quality min-cost flow-based scheduler, to choose placements quickly in large data centres. Finally, I demonstrate that Firmament chooses placements at least as good as other sophisticated schedulers, but at the speeds associated with simple schedulers. These contributions enable more efficient and effective use of data centres for large-scale computation than current solutions.Google fellowship in distributed system

Bryn Mawr College Undergraduate College Catalogue and Calendar, 1920

Volume contains a register of alumnae and former students, a calendar of graduate course, and a calendar of undergraduate and graduate courses for 1920.https://repository.brynmawr.edu/bmc_calendars/1012/thumbnail.jp

Chemical Information Bulletin

Periodic supplement for "the regular journals of the American Chemical Society," containing annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Literature