40202410
On Performance of Parallel iSCSI Protocol for Networked Storage Systems
A newly emerging protocol for storage networking, iSCSI, was recently ratified by the Internet Engineering Task Force. The iSCSI protocol is perceived as a low cost alternative to the FC protocol for networked storages. It allows block level storage data to be transported over the popular TCP/IP network that is widely understood. This paper presents a new storage architecture allowing parallel processing of iSCSI packets. By leveraging inexpensive Ethernet ports, we are able to greatly improve the throughput of iSCSI storages through parallel processing. We have carried out a performance analysis to evaluate the performance of the new architecture as compared to the existing iSCSI storages. Our numerical results show that the new architecture has good performance potential
78709
STICS: SCSI-to-IP cache for storage area networks
Abstract Data storage plays an essential role in today's fast-growing data-intensive network services. New standards and products emerge very rapidly for networked data storage. Given the mature Internet infrastructure, the overwhelming preference among the IT community recently is using IP for storage networking because of economy and convenience. iSCSI is one of the most recent standards that allow SCSI protocols to be carried out over IP networks. However, there are many disparities between SCSI and IP in terms of protocols, speeds, bandwidths, data unit sizes, and design considerations that prevent fast and efficient deployment of storage area network (SAN) over IP. This paper introduces SCSI-to-IP cache storage (STICS), a novel storage architecture that couples reliable and high-speed data caching with low-overhead conversion between SCSI and IP protocols. A STICS block consists of one or several storage devices and an intelligent processing unit with CPU and RAM. The storage devices are used to cache and store data while the intelligent processing unit carries out the caching algorithm, protocol conversion, and self-management functions. Through the efficient caching algorithm and localization of certain unnecessary protocol overheads, STICS can significantly improve performance, reliability, and scalability over current iSCSI systems. Furthermore, STICS can be used as a basic plug-and-play building block for data storage over IP. Analogous to “cache memory” invented several decades ago for bridging the speed gap between CPU and memory, STICS is the first-ever “cache storage” for bridging the gap between SCSI and IP making it possible to build an efficient SAN over IP. We have implemented software STICS prototype on Linux operating system. Numerical results using popular benchmarks such as vxbench, IOzone, PostMark, and EMC's trace have shown a dramatic performance gain over the current iSCSI implementation.
2566279
PRINS: Optimizing Performance of Reliable Internet Storages
Distributed storage systems employ replicas or erasure code to ensure high reliability and availability of data. Such replicas create great amount of network traffic that negatively impacts storage performance, particularly for distributed storage systems that are geographically dispersed over a wide area network (WAN). This paper presents a performance study of our new data replication methodology that minimizes network traffic for data replications. The idea is to replicate the parity of a data block upon each write operation instead of the data block itself. The data block will be recomputed back at the replica storage site upon receiving the parity. We name the new methodology PRINS (Parity Replication in IP-Network Storages). PRINS trades off highspeed computation for communication that is costly and more likely to be the performance bottleneck for distributed storages. By leveraging the parity computation that exists in common storage systems (RAID), our PRINS does not introduce additional overhead but dramatically reduces network traffic. We have implemented PRINS using iSCSI protocol over a TCP/IP network interconnecting a cluster of PCs as storage nodes. We carried out performance measurements on Oracle database, Postgres database, MySQL database, and Ext2 file system using TPC-C, TPC-W, and Micro benchmarks. Performance measurements show up to 2 orders of magnitudes bandwidth savings of PRINS compared to traditional replicas. A queueing network model is developed to further study network performance for large networks. It is shown that PRINS reduces response time of the distributed storage systems dramatically.
36641724
A new and efficient FFT algorithm for distributed memory systems
This paper presents a new and optimal parallel implementation of multidimensional fast Fourier transform algorithm on distributed memory multiprocessors. Its optimality is obtained by minimizing the number of message passings necessary, at the cost of increase in message length. This distinctive feature of the new algorithm effectively utilizes the important architectural property of most of today's distributed memory multiprocessors-wormhole routing for interprocessor communications. By using the algebra of stride permutations and tenser products as a mathematical tool, we are able to derive and formulate an efficient data partition and communication scheme that reduces communication cost from O(N/sup 2/) required for the best known FFT to O(N) on an N/sup 2/-processor machine. Our data partition scheme is natural and efficient for solving discretized boundary value problems such as partial differential equations and finite element analysis. To evaluate the actual performance of our new algorithm in comparison with other existing parallel FFT algorithms, we have carried out implementation experiments on the Intel's Touchstone Delta machine.
229042
Introducing a New Cache Design into Vector Computers
Introduces an innovative cache design for vector computers, called prime-mapped cache. By utilizing the special properties of a Mersenne prime, the new design does not increase the critical path length of a processor, nor does it increase the cache access time as compared to existing cache organizations. The prime-mapped cache minimizes cache miss ratio caused by line interferences that have been shown to be critical for numerical applications by previous investigators. With negligibly additional hardware cost, significant performance gains are obtained by adding the proposed cache memory to an existing vector computer. The performance of the design is studied analytically, using a generic vector computation model. The analytical model is validated through extensive simulation experiments. A performance analysis for various vector access patterns shows that the prime-mapped cache performs significantly better than conventional cache organizations in the vector processing environment. The performance gain will increase with the increase of the speed gap between processors and memories. >
11786279
Effects of Arbitration Protocols on the Performance of Multiple-Bus Multiprocessors
9549411
Mining plans for customer-class transformation
We consider the problem of mining high-utility plans from historical plan databases that can be used to transform customers from one class to other, more desirable classes. Traditional data mining algorithms are focused on finding frequent sequences. But high frequency may not imply low costs and high benefits. Traditional Markov decision process (MDP) algorithms are designed to address this issue by bringing in the concept of utility, but these algorithms are also known to be expensive to execute. We present a novel algorithm AUPlan, which automatically generates sequential plans with high utility by combining data mining and AI planning. These high-utility plans could be used to convert groups of customers from less desirable states to more desirable ones. Our algorithm adapts the Apriori algorithm by considering the concepts of plans and utilities. We show through empirical studies that planning using our integrated algorithm produces high-utility plans efficiently.
1777247
Web-page classification through summarization
Web-page classification is much more difficult than pure-text classification due to a large variety of noisy information embedded in Web pages. In this paper, we propose a new Web-page classification algorithm based on Web summarization for improving the accuracy. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web-page classification algorithms. We then propose a new Web summarization-based classification algorithm and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that our proposed summarization-based classification algorithm achieves an approximately 8.8% improvement as compared to pure-text-based classification algorithm. We further introduce an ensemble classifier using the improved summarization algorithm and show that it achieves about 12.9% improvement over pure-text based methods.
5296157
A Novel Scalable Algorithm for Supervised Subspace Learning
Subspace learning approaches aim to discover important statistical distribution on lower dimensions for high dimensional data. Methods such as principal component analysis (PCA) do not make use of the class information, and linear discriminant analysis (LDA) could not be performed efficiently in a scalable way. In this paper, we propose a novel highly scalable supervised subspace learning algorithm called as supervised Kampong measure (SKM). It assigns data points as close as possible to their corresponding class mean, simultaneously assigns data points to be as far as possible from the other class means in the transformed lower dimensional subspace. Theoretical derivation shows that our algorithm is not limited by the number of classes or the singularity problem faced by LDA. Furthermore, our algorithm can be executed in an incremental manner in which learning is done in an online fashion as data streams are received. Experimental results on several datasets, including a very large text data set RCV1, show the outstanding performance of our proposed algorithm on classification problems as compared to PCA, LDA and a popular feature selection approach, information gain (IG).
1859019
A parallel algorithm for clustering protein-protein interaction networks
The increasing availability of interaction graphs requires new resource-efficient tools capable of extracting valuable biological knowledge from these networks. In this paper we report on a novel parallel implementation of Girvan and Newman's clustering algorithm that is capable of running on clusters of computers. Our parallel implementation achieves almost linear speed-up up to 32 processors and allows us to run this computationally intensive algorithm on large protein-protein interaction networks. Preliminary experiments show that the algorithm has very high accuracy in identifying functional related protein modules.
31763391
Guest Editors' Introduction to the Special Issue: Machine Learning for Bioinformatics-Part 1
IN recent years, rapid developments in genomics and proteomics have generated a large amount of data. Often, drawing conclusions from these data requires sophisticated computational analyses. Bioinformatics, or computational biology, is the interdisciplinary science of interpreting biological data using information technology and computer science. The importance of this new field of inquiry will grow as we continue to generate and integrate large quantities of genomic, proteomic, and other data. A particularly active area of research in bioinformatics is the application and development of machine learning techniques to biological problems.Analyzing large biological data sets requires making sense of the data by inferring structure or generalizations from the data. Examples of this type of analysis include protein structure prediction, gene classification, cancer classification based onmicroarray data, clustering of gene expression data, statistical modeling of protein-protein interaction, etc. Each of these tasks can be framed as a problem in machine learning. We therefore see a great potential to increase the interaction between machine learning and bioinformatics. This special issue is aimed at facilitating that interaction. We believe that machine learning can provide powerful tools for analyzing, predicting, and understanding data from emerging genomic and proteomic technologies. The papers submitted to this special issue provide strong evidence that this is the case. In total, more than 50 paperswere submitted to the special issue. After extensive reviews and revisions, 13 papers were accepted into the special issue,whichwill bepublished in two parts. The quality and significance of the accepted papers are very high and the papers cover a wide variety of topics. Below,weprovide a summary of the papers published in this journal issue.