6548761
Cooperative caching: using remote client memory to improve file system performance
Emerging high-speed networks will allow machines to access remote data nearly as quickly as they can access local data. This trend motivates the use of cooperative caching: coordinating the file caches of many machines distributed on a LAN to form a more effective overall file cache. In this paper we examine four cooperative caching algorithms using a trace-driven simulation study. These simulations indicate that for the systems studied cooperative caching can halve the number of disk accesses, improving file system read response time by as much as 73%. Based on these simulations we conclude that cooperative caching can significantly improve file system read response time and that relatively simple cooperative caching algorithms are sufficient to realize most of the potential performance gain.
14669009
Considering the energy consumption of mobile storage alternatives
This paper is motivated by a simple question: what are the energy consumption characteristics of mobile storage alternatives? To answer this question, we are faced with a design space of multiple dimensions. Two important dimensions are the type of storage technologies and the type of file systems. In this paper, we explore some options along each of these two dimensions. We have constructed a logical-disk system, which can be configured to run on different storage technologies and to emulate the behavior of different file systems. As we explore these configuration options, we find that the energy behavior is determined by a complex interaction of three factors: the power management mechanism of the storage device, the distribution of idleness in the workload, and the file system strategies that attempt to exploit and bridge these first two factors.
5165465
Adaptive control for packet video
Describes a portable and robust software mechanism for adaptive frame rate control for real time packet video transfer and viewing in workstation environments. No special hardware or system support is assumed. It includes (1) a responsive feedback system for dynamic presentation layer rate control in response to network load, (2) a simple and effective transport layer flow control scheme built upon an unreliable network protocol, and (3) data structures and scheduling support for integrating the rate and flow control mechanisms. The authors demonstrate that excellent jitter control is achieved by gracefully trading image resolution, and loss rates can be drastically reduced by appropriate flow control.<<ETX>>
208003557
Serverless network file systems
We propose a new paradigm for network file system design: serverless network file systems. While traditional network file systems rely on a central server machine, a serverless system utilizes workstations cooperating as peers to provide all file system services. Any machine in the system can store, cache, or control any block of data. Our approach uses this location independence, in combination with fast local area networks, to provide better performance and scalability than traditional file systems. Furthermore, because any machine in the system can assume the responsibilities of a failed component, our serverless design also provides high availability via redundatn data storage. To demonstrate our approach, we have implemented a prototype serverless network file system called xFS. Preliminary performance measurements suggest that our architecture achieves its goal of scalability. For instance, in a 32-node xFS system with 32 active clients, each client receives nearly as much read or write throughput as it would see if it were the only active client.
11858758
Improving the performance of log-structured file systems with adaptive methods
File system designers today face a dilemma. A log-structured file system (LFS) can offer superior performance for many common workloads such as those with frequent small writes, read traffic that is predominantly absorbed by the cache, and sufficient idle time to clean the log. However, an LFS has poor performance for other workloads, such as random updates to a full disk with little idle time to clean. In this paper, we show how adaptive algorithms can be used to enable LFS to provide high performance across a wider range of workloads. First, we show how to improve LFS write performance in three ways: by choosing the segment size to match disk and workload characteristics, by modifying the LFS cleaning policy to adapt to changes in disk utilization, and by using cached data to lower cleaning costs. Second, we show how to improve LFS read performance by reorganizing data to match read patterns. Using trace-driven simulations on a combination of synthetic and measured workloads, we demonstrate that these extensions to LFS can significantly improve its performance.
6622555
A quantitative analysis of cache policies for scalable network file systems
Current network file system protocols rely heavily on a central server to coordinate file activity among client workstations. This central server can become a bottleneck that limits scalability for environments with large numbers of clients. In central server systems such as NFS and AFS, all client writes, cache misses, and coherence messages are handled by the server. To keep up with this workload, expensive server machines are needed, configured with high-performance CPUs, memory systems, and I/O channels. Since the server stores all data, it must be physically capable of connecting to many disks. This reliance on a central server also makes current systems inappropriate for wide area network use where the network bandwidth to the server may be limited.
In this paper, we investigate the quantitative performance effect of moving as many of the server responsibilities as possible to client workstations to reduce the need for high-performance server machines. We have devised a cache protocol in which all data reside on clients and all data transfers proceed directly from client to client. The server is used only to coordinate these data transfers. This protocol is being incorporated as part of our experimental file system, xFS. We present results from a trace-driven simulation study of the protocol using traces from a 237 client NFS installation. We find that the xFS protocol reduces server load by more than a factor of six compared to AFS without significantly affecting response time or file availability.
2587881
Isomap Based on the Image Euclidean Distance
Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (isomap) is one of the representative techniques of manifold. It is intuitive, well understood and produces reasonable mapping results. However, if the input data for manifold learning are corrupted with noises, the isomap algorithm is topologically unstable. In this paper, we present an improved manifold learning method when the input data are images - the image Euclidean distance based isomap (imisomap), in which we use a new distance for images called image Euclidean distance (IMED). Experimental results demonstrate a consistent performance improvement of the algorithm imisomap over the traditional isomap based on Euclidean distance
1431727
Combining Flexibility and Scalability in a Peer-to-Peer Publish/Subscribe System
The content-based publish/subscribe model has been adopted by many services to deliver data between distributed users based on application-specific semantics. Two key issues in such systems, the semantic expressiveness of content matching and the scalability of the matching mechanism, are often found to be in conflict due to the complexity associated with content matching. In this paper, we present a novel content-based publish/subscribe architecture based on peer-to-peer matching trees. The system achieves scalability by partitioning the responsibility of event matching to self-organized peers while allowing customizable matching functionalities. Experimental results using a variety of real world datasets demonstrate the scalability and flexibility of the system.
4989255
Face Detection Based on the Manifold
Data collection for both training and testing a classifier is a tedious but essential step towards face detection and recognition. It is a piece of cake to collect more than hundreds of thousands of examples from web and digital camera nowadays. How to train a face detector based on the collected immense face database? This paper presents a manifold-based method to select a training set. That is to say we learn the manifold from the collected enormous face database and then subsample and interweave the training set by the estimated geodesic distance in the low-dimensional manifold embedding. By the resulting training set, we train an AdaBoost-based face detector. The trained detector is tested on the MIT+CMU frontal face test set. The experimental results show that the proposed method based on the manifold is efficient to train a classifier confronted with the huge database.
1050293
Understanding captions in biomedical publications
From the standpoint of the automated extraction of scientific knowledge, an important but little-studied part of scientific publications are the figures and accompanying captions. Captions are dense in information, but also contain many extra-grammatical constructs, making them awkward to process with standard information extraction methods. We propose a scheme for "understanding" captions in biomedical publications by extracting and classifying "image pointers" (references to the accompanying image). We evaluate a number of automated methods for this task, including hand-coded methods, methods based on existing learning techniques, and methods based on novel learning techniques. The best of these methods leads to a usefully accurate tool for caption-understanding, with both recall and precision in excess of 94% on the most important single class in a combined extraction/classification task.
7961359
LexTrim: A Lexical Cohesion Based Approach to Parse-and-Trim Style Headline Generation
In this paper we compare two parse-and-trim style headline generation systems. The Topiary system uses a statistical learning approach to finding topic labels for headlines, while our approach, the LexTrim system, identifies key summary words by analysing the lexical cohesion structure of a text. The performance of these systems is evaluated using the ROUGE evaluation suite on the DUC 2004 news stories collection.