In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.

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A tutorial on support vector regression

Numerous systems have been designed which use virtualization to subdivide the ample resources of a modern computer. Some require specialized hardware, or cannot support commodity operating systems. Some target 100% binary compatibility at the expense of performance. Others sacrifice security or functionality for speed. Few offer resource isolation or performance guarantees; most provide only best-effort provisioning, risking denial of service.This paper presents Xen, an x86 virtual machine monitor which allows multiple commodity operating systems to share conventional hardware in a safe and resource managed fashion, but without sacrificing either performance or functionality. This is achieved by providing an idealized virtual machine abstraction to which operating systems such as Linux, BSD and Windows XP, can be ported with minimal effort.Our design is targeted at hosting up to 100 virtual machine instances simultaneously on a modern server. The virtualization approach taken by Xen is extremely efficient: we allow operating systems such as Linux and Windows XP to be hosted simultaneously for a negligible performance overhead --- at most a few percent compared with the unvirtualized case. We considerably outperform competing commercial and freely available solutions in a range of microbenchmarks and system-wide tests.

/pdf/xen-and-the-art-of-virtualization-vt3kaikb6y.pdf

Xen and the art of virtualization

OceanStore is a utility infrastructure designed to span the globe and provide continuous access to persistent information. Since this infrastructure is comprised of untrusted servers, data is protected through redundancy and cryptographic techniques. To improve performance, data is allowed to be cached anywhere, anytime. Additionally, monitoring of usage patterns allows adaptation to regional outages and denial of service attacks; monitoring also enhances performance through pro-active movement of data. A prototype implementation is currently under development.

/pdf/oceanstore-an-architecture-for-global-scale-persistent-4qe9kbb44y.pdf

OceanStore: an architecture for global-scale persistent storage

This paper presents a new technique for disk storage management called a log-structured file system. A log-structured file system writes all modifications to disk sequentially in a log-like structure, thereby speeding up both file writing and crash recovery. The log is the only structure on disk; it contains indexing information so that files can be read back from the log efficiently. In order to maintain large free areas on disk for fast writing, we divide the log intosegmentsand use a segment cleaner to compress the live information from heavily fragmented segments. We present a series of simulations that demonstrate the efficiency of a simple cleaning policy based on cost and benefit. We have implemented a prototype log-structured file system called Sprite LFS; it outperforms current Unix file systems by an order of magnitude for small-file writes while matching or exceeding Unix performance for reads and large writes. Even when the overhead for cleaning is included, Sprite LFS can use 70% of the disk bandwidth for writing, whereas Unix file systems typically can use only 5–10%.

The design and implementation of a log-structured file system

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

lmbench is a micro-benchmark suite designed to focus attention on the basic building blocks of many common system applications, such as databases, simulations, software development, and networking. In almost all cases, the individual tests are the result of analysis and isolation of a customer's actual performance problem. These tools can be, and currently are, used to compare different system implementations from different vendors. In several cases, the benchmarks have uncovered previously unknown bugs and design flaws. The results have shown a strong correlation between memory system performance and overall performance. lmbench includes an extensible database of results from systems current as of late 1995.

/pdf/lmbench-portable-tools-for-performance-analysis-4k1j04zp5q.pdf

lmbench: portable tools for performance analysis

Configuring redundant disk arrays is a black art To configure an array properly, a system administrator must understand the details of both the array and the workload it will support Incorrect understanding of either, or changes in the workload over time, can lead to poor performance We present a solution to this problem: a two-level storage hierarchy implemented inside a single disk-array controller In the upper level of this hierarchy, two copies of active data are stored to provide full redundancy and excellent performance In the lower level, RAID 5 parity protection is used to provide excellent storage cost for inactive data, at somewhat lower performance The technology we describe in this article, know as HP AutoRAID, automatically and transparently manages migration of data blocks between these two levels as access patterns change The result is a fully redundant storage system that is extremely easy to use, is suitable for a wide variety of workloads, is largely insensitive to dynamic workload changes, and performs much better than disk arrays with comparable numbers of spindles and much larger amounts of front-end RAM cache Because the implementation of the HP AutoRAID technology is almost entirely in software, the additional hardware cost for these benefits is very small We describe the HP AutoRAID technology in detail, provide performance data for an embodiment of it in a storage array, and summarize the results of simulation studies used to choose algorithms implemented in the array

The HP AutoRAID hierarchical storage system

The requirements of wide-area distributed database systems differ dramatically from those of local-area network systems. In a wide-area network (WAN) configuration, individual sites usually report to different system administrators, have different access and charging algorithms, install site-specific data type extensions, and have different constraints on servicing remote requests. Typical of the last point are production transaction environments, which are fully engaged during normal business hours, and cannot take on additional load. Finally, there may be many sites participating in a WAN distributed DBMS.In this world, a single program performing global query optimization using a cost-based optimizer will not work well. Cost-based optimization does not respond well to site-specific type extension, access constraints, charging algorithms, and time-of-day constraints. Furthermore, traditional cost-based distributed optimizers do not scale well to a large number of possible processing sites. Since traditional distributed DBMSs have all used cost-based optimizers, they are not appropriate in a WAN environment, and a new architecture is required.We have proposed and implemented an economic paradigm as the solution to these issues in a new distributed DBMS called Mariposa. In this paper, we present the architecture and implementation of Mariposa and discuss early feedback on its operating characteristics.

/pdf/mariposa-a-wide-area-distributed-database-system-24bnqnm9n0.pdf

Mariposa: a wide-area distributed database system

Research results [ROSE91] suggest that a log-structured file system (LFS) offers the potential for dramatically improved write performance, faster recovery time, and faster file creation and deletion than traditional UNIX file systems. This paper presents a redesign and implementation of the Sprite [ROSE91] log-structured file system that is more robust and integrated into the vnode interface [KLEI86]. Measurements show its performance to be superior to the 4BSD Fast File System (FFS) in a variety of benchmarks and not significantly less than FFS in any test. Unfortunately, an enhanced version of FFS (with read and write clustering) [MCVO91] provides comparable and sometimes superior performance to our LFS. However, LFS can be extended to provide additional functionality such as embedded transactions and versioning, not easily implemented in traditional file systems.

/pdf/an-implementation-of-a-log-structured-file-system-for-unix-4mybps41yt.pdf

An implementation of a log-structured file system for UNIX

Many people have observed that computer systems spend much of their time idle, and various schemes have been proposed to use this idle time productively. The commonest approach is to off-load activity from busy periods to less-busy ones in order to improve system responsiveness. In addition, speculative work can be performed in idle periods in the hopes that it will be needed later at times of higher utilization, or non-renewable resource like battery power can be conserved by disabling unused resources.

We found opportunities to exploit idle time in our work on storage systems, and after a few attempts to tackle specific instances of it in ad hoc ways, began to investigate general mechanisms that could be applied to this problem. Our results include a taxonomy of idle-time detection algorithms, metrics for evaluating them, and an evaluation of a number of idleness predictors that we generated from our taxonomy.

http://www.hpl.hp.com/techreports/96/HPL-96-140.pdf

Carl Staelin

Papers

lmbench: portable tools for performance analysis

The HP AutoRAID hierarchical storage system

Mariposa: a wide-area distributed database system

An implementation of a log-structured file system for UNIX

Idleness is not sloth