2308836
RAPID-Cache-A Reliable and Inexpensive Write Cache for High Performance Storage Systems
Modern high performance disk systems make extensive use of nonvolatile RAM (NVRAM) write caches. A single-copy NVRAM cache creates a single point of failure while a dual-copy NVRAM cache is very expensive because of the high cost of NVRAM. This paper presents a new cache architecture called RAPID-Cache for Redundant, Asymmetrically Parallel, and Inexpensive Disk Cache. A typical RAPID-Cache consists of two redundant write buffers on top of a disk system. One of the buffers is a primary cache made of RAM or NVRAM and the other is a backup cache containing a two-level hierarchy: a small NVRAM buffer on top of a log disk. The small NVRAM buffer combines small write data and writes them into the log disk in large sizes. By exploiting the locality property of I/O accesses and taking advantage of well-known Log-structured File Systems, the backup cache has nearly equivalent write performance as the primary RAM cache. The read performance of the backup cache is not as critical because normal read operations are performed through the primary RAM cache and reads from the backup cache happen only during error recovery periods. The RAPID-Cache presents an asymmetric architecture with a fast-write-fast-read RAM being a primary cache and a fast-write-slow-read NVRAM-disk hierarchy being a backup cache. The asymmetrically parallel architecture and an algorithm that separates actively accessed data from inactive data in the cache virtually eliminate the garbage collection overhead, which are the major problems associated with previous solutions such as Log-structured File Systems and Disk Caching Disk. The asymmetric cache allows cost-effective designs for very large write caches for high-end parallel disk systems that would otherwise have to use dual-copy, costly NVRAM caches. It also makes it possible to implement reliable write caching for low-end disk I/O systems since the RAPID-Cache makes use of inexpensive disks to perform reliable caching. Our analysis and trace-driven simulation results show that the RAPID-Cache has significant reliability/cost advantages over conventional single NVRAM write caches and has great cost advantages over dual-copy NVRAM caches. The RAPID-Cache architecture opens a new dimension for disk system designers to exercise trade-offs among performance, reliability, and cost.
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.
11786279
Effects of Arbitration Protocols on the Performance of Multiple-Bus Multiprocessors
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. >
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.
2823399
Reducing the Calibration Effort for Location Estimation Using Unlabeled Samples
WLAN location estimation based on 802.11 signal strength is becoming increasingly prevalent in today's pervasive computing applications. As an alternative to the well-established deterministic approaches, probabilistic location determination techniques show good performance and thus become increasingly popular. For these techniques to achieve a high level of accuracy, however, adequate training samples should be collected offline for calibration. As a result, a great amount of manual effort is incurred. In this paper, we aim to solve the problem by reducing both the sampling time and the number of locations sampled in constructing the radio map. A learning algorithm is proposed to build location estimation systems based on a small fraction of the calibration data that traditional techniques require and a collection of user traces that can be cheaply obtained. Our experiments show that unlabeled user traces can be used to compensate for the effects of reducing calibration effort and can even improve the system performance. Consequently, manual effort can be significantly reduced while a high level of accuracy is still achieved
5926886
Case Mining from Large Databases
This paper presents an approach of case mining to automatically discover case bases from large datasets in order to improve both the speed and the quality of case based reasoning. Case mining constructs a case base from a large raw dataset with an objective to improve the case-base reasoning systems' efficiency and quality. Our approach starts from a raw database of objects with class attributes together with a historical database of past action sequences on these objects. The object databases can be customer records and the historical action logs can be the technical advises given to the customers to solve their problems. Our goal is to discover effective and highly representative problem descriptions associated with solution plans that accomplish their tasks. To maintain efficiency of computation, data mining methods are employed in the process of composing the case base. We motivate the application of the case mining model using a financial application example, and demonstrate the effectiveness of the model using both real and simulated datasets.
13194241
BUCS - a bottom-up cache structure for networked storage servers
This paper introduces a new caching structure to improve server performance by minimizing data traffic over the system bus. The idea is to form a bottom-up caching hierarchy in a networked storage server. The bottom level cache is located on an embedded controller that is a combination of a network interface card (NIC) and a storage host bus adapter (HBA). Storage data coming from or going to a network are cached at this bottom level cache and meta-data related to these data are passed to the host for processing. When cached data exceed the capacity of the bottom level cache, some data are moved to the host RAM that is usually larger than the bottom level cache. This new cache hierarchy is referred to as bottom-up cache structure (BUGS) in contrast to a traditional CPU-centric top-down cache where the top-level cache is the smallest and fastest, and the lower in the hierarchy the larger and slower the cache. Such data caching at the controller level dramatically reduces bus traffic and leads to great performance improvement for networked storages. We have implemented a proof-of-concept prototype using Intel's IQ80310 reference board and Linux network block device. Through performance measurements on the prototype implementation, we observed up to 3 times performance improvement of BUCS over traditional systems in terms of response time and system throughput.
16808814
Feature Weight Maintenance in Case Bases Using Introspective Learning
A key issue in case-based reasoning is how to maintain the domain knowledge in the face of a changing environment. During the case retrieval process in case-based reasoning, feature-value pairs are used to compute the ranking scores of the cases in a case base, and different feature-value pairs may have different importance measures, represented as weight values, in this computation. How to maintain a set of appropriate feature weights so that they can be used to solve future problems effectively and efficiently will be a key factor in determining the success of case-based reasoning applications.Our focus in this paper is on the dynamic maintenance of feature weights in a case base. We address a particular problem related to the feature-weight maintenance issue. In current practice, the feature weights are assigned and revised manually, not only making them highly informal and inaccurate, but also involving intensive labor. We would like to introduce a semi-automatic introspective learning method to partially address this issue. Our approach is to construct a network architecture on the case base that supports introspective learning. Weight learning and weight-evolution are accomplished in the background through the integration of a learning network into case-based reasoning, in which, while the reasoning part is still case based, the learning part is shouldered by a layered network. The computation in the network follows well-known neural network algorithms with well known properties. We demonstrate the effectiveness of our approach through experiments.
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).