Rule groupings in expert systems using nearest neighbour decision rules, and convex hulls

Expert System shells are lacking in many areas of software engineering. Large rule based systems are not semantically comprehensible, difficult to debug, and impossible to modify or validate. Partitioning a set of rules found in CLIPS (C Language Integrated Production System) into groups of rules which reflect the underlying semantic subdomains of the problem, will address adequately the concerns stated above. Techniques are introduced to structure a CLIPS rule base into groups of rules that inherently have common semantic information. The concepts involved are imported from the field of A.I., Pattern Recognition, and Statistical Inference. Techniques focus on the areas of feature selection, classification, and a criteria of how 'good' the classification technique is, based on Bayesian Decision Theory. A variety of distance metrics are discussed for measuring the 'closeness' of CLIPS rules and various Nearest Neighbor classification algorithms are described based on the above metric

Parallel Application Scheduling on Networks of Workstations

Parallel application support is one of the ways that have been recently proposed for exploiting the idle computing capacity of workstation networks. However, it has been unclear how to most effectively schedule the processors among different job requests. The distributed memory nature of such environments and the structure of the corresponding applications cause many solutions that were successful for sharedmemory machines to be inadequate in the new environment. In this thesis, we investigate how knowledge of system load and application characteristics can be used in scheduling decisions. We propose a new algorithm that, with proper exploitation of both the information types above, manages to improve the performance of non-preemptive scheduling relative to other rules. Thus, we show that with the appropriate support in the operating system, the application developers can be left free to use the programming model best suited to the individual application needs. ii Acknowledgments It ..

Server-Based Smoothing of Variable Bit-Rate Streams

We introduce an algorithm that uses buffer space available at the server for smoothing disk transfers of variable bit-rate streams. Previous smoothing techniques prefetched stream data into the client buffer space, instead. However, emergence of personal computing devices with widely different hardware configurations means that we should not always assume abundance of resources at the client side. The new algorithm is shown to have optimal smoothing effect under the specified constraints. We incorporate it into a prototype server, and demonstrate significant increase in the number of streams concurrently supported at different system scales. We also extend our algorithm for striping variable bit-rate streams on heterogeneous disks. High bandwidth utilization is achieved across all the different disks, which leads to server throughput improved by several factors at high loads. 1

Maximizing throughput in replicated disk striping of variable bit-rate streams

In a system offering on-demand real-time streaming of media files, data striping across an array of disks can improve load balancing, allowing higher disk utilization and increased system throughput. However, it can also cause complete service disruption in the case of a disk failure. Reliability can be improved by adding data redundancy and reserving extra disk bandwidth during normal operation. In this paper, we are interested in providing fault-tolerance for media servers that support variable bit-rate encoding formats. Higher compression efficiency with respect to constant bit-rate encoding can significantly reduce per-user resource requirements, at the cost of increased resource management complexity. For the first time, the interaction between storage system fault-tolerance and variable bit-rate streaming with deterministic QoS guarantees is investigated. We implement into a prototype server and experimentally evaluate, using detailed simulated disk models, alternative data replication techniques and disk bandwidth reservation schemes. We show that with the minimum reservation scheme introduced here, single disk failures can be tolerated at a cost of less than 20 % reduced throughput during normal operation, even for a disk array of moderate size. We also examine the benefit from load balancing techniques proposed for traditional storage systems and find only limited improvement in the measured throughput.

Low-cost management of inverted files for online full-text search

In dynamic environments with frequent content updates, we re-quire online full-text search that scales to large data collections and achieves low search latency. Several recent methods that support fast incremental indexing of documents typically keep on disk mul-tiple partial index structures that they continuously update as new documents are added. However, spreading indexing information across multiple locations on disk tends to considerably decrease the search responsiveness of the system. In the present paper, we take a fresh look at the problem of online full-text search with consid-eration of the architectural features of modern systems. Selective Range Flush is a greedy method that we introduce to manage the index in the system by using fixed-size blocks to organize the data on disk and dynamically keep low the cost of data transfer between memory and disk. As we experimentally demonstrate with the Pro-teus prototype implementation that we developed, we retrieve in-dexing information at latency that matches the lowest achieved by existing methods. Additionally, we reduce the total building cost by 30 % in comparison to methods with similar retrieval time

Maximizing Throughput in Replicated Disk Striping of Variable Bit-Rate Streams Abstract

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Virtualization-aware Access Control for Multitenant Filesystems

Abstract-In a virtualization environment that serves multiple tenants, storage consolidation at the filesystem level is desirable because it enables data sharing, administration efficiency, and performance optimizations. The scalable deployment of filesystems in such environments is challenging due to intermediate translation layers required for networked file access or identity management. First we present several security requirements in multitenant filesystems. Then we introduce the design of the Dike authorization architecture. It combines native access control with tenant namespace isolation and compatibility to object-based filesystems. We use a public cloud to experimentally evaluate a prototype implementation of Dike that we developed. At several thousand tenants, our prototype incurs limited performance overhead up to 16%, unlike an existing solution whose multitenancy overhead approaches 84% in some cases