Real time asynchronous

Enhancing the applied epistemics

Knowledge discovery in Databases

Fuzzy Rule-based decision

New approaches for image pattern analysis

Symbolic Data Analysis

Remotely sensed data

Resource management of large scale clusters is traditionally done manually. Servers are usually over-provisioned to meet the peak demand of workload. It is widely known that manual provisioning is error-prone and inefficient. These problems can be addressed by the use of autonomic clusters that manage their own resources. In those clusters, server nodes are dynamically allocated based on the system performance goals. In this thesis, we develop heuristic algorithms for the dynamic provisioning of a cluster that executes batch jobs with a shared completion deadline. <br /><br /> External factors that may affect the decision to use servers during a certain time period are modeled as a time-varying cost function. The provisioning goal is ensure that all jobs are completed on time while minimizing the total cost of server usage. Five resource provisioning heuristic algorithms which adapt to changing workload are presented. The merit of these heuristics is evaluated by simulation. In our simulation, the job arrival rate is time-dependent which captures the typical job profile of a batch environment. Our results show that heuristics that take into consideration the cost function perform better than the others.

This thesis presents techniques for accurately computing a number of fundamental operations on approximate polynomials. The general goal is to determine nearby polynomials which have a non-trivial result for the operation. We proceed by first translating each of the polynomial operations to a particular structured matrix system, constructed to represent dependencies in the polynomial coefficients. Perturbing this matrix system to a nearby system of reduced rank yields the nearby polynomials that have a non-trivial result. The translation from polynomial operation to matrix system permits the use of emerging methods for solving sophisticated least squares problems. These methods introduce the required dependencies in the system in a structured way, ensuring a certain minimization is met. This minimization ensures the determined polynomials are close to the original input. We present translations for the following operations on approximate polynomials: <ul> <li>Division</li> <li>Greatest Common Divisor (GCD)</li> <li>Bivariate Factorization</li> <li>Decomposition</li> </ul> The Least Squares problems considered include classical Least Squares (LS), Total Least Squares (TLS) and Structured Total Least Squares (STLS). In particular, we make use of some recent developments in formulation of STLS, to perturb the matrix system, while maintaining the structure of the original matrix. This allows reconstruction of the resulting polynomials without applying any heuristics or iterative refinements, and guarantees a result for the operation with zero residual. Underlying the methods for the LS, TLS and STLS problems are varying uses of the Singular Value Decomposition (SVD). This decomposition is also a vital tool for deter- mining appropriate matrix rank, and we spend some time establishing the accuracy of the SVD. We present an algorithm for <i>relatively accurate</i> SVD recently introduced in [8], then used to solve LS and TLS problems. The result is confidence in the use of LS and TLS for the polynomial operations, to provide a fair contrast with STLS. The SVD is also used to provide the starting point for our STLS algorithm, with the prescribed guaranteed accuracy. Finally, we present a generalized implementation of the Riemannian SVD (RiSVD), which can be applied on any structured matrix to determine the result for STLS. This has the advantage of being applicable to all of our polynomial operations, with the penalty of decreased efficiency. We also include a novel, yet naive, improvement that relies on ran- domization to increase the efficiency, by converting a rectangular system to one that is square. The results for each of the polynomial operations are presented in detail, and the benefits of each of the Least Squares solutions are considered. We also present distance bounds that confirm our solutions are within an acceptable tolerance.

Client applications submit streams of relational queries to database servers. For simple requests, inter-process communication costs account for a significant portion of user-perceived latency. This trend increases with faster processors, larger memory sizes, and improved database execution algorithms, and this trend is not significantly offset by improvements in communication bandwidth. Caching and prefetching are well studied approaches to reducing user-perceived latency. Caching is useful in many applications, but it does not help if future requests rarely match previous requests. Prefetching can help in this situation, but only if we are able to predict future requests. This prediction is complicated in the case of relational queries by the presence of request parameters: a prefetching algorithm must predict not only a query that will be executed in the future, but also the actual parameter values that will be supplied. We have found that, for many applications, the streams of submitted queries contain patterns that can be used to predict future requests. Further, there are correlations between results of earlier requests and actual parameter values used in future requests. We present the Scalpel system, a prototype implementation that detects these patterns of queries and optimizes request streams using context-based predictions of future requests. Scalpel uses its predictions to provide a form of semantic prefetching, which involves combining a predicted series of requests into a single request that can be issued immediately. Scalpel's semantic prefetching reduces not only the latency experienced by the application but also the total cost of query evaluation. We describe how Scalpel learns to predict optimizable request patterns by observing the application's request stream during a training phase. We also describe the types of query pattern rewrites that Scalpel's cost-based optimizer considers. Finally, we present empirical results that show the costs and benefits of Scalpel's optimizations. We have found that even when an application is well suited for its original configuration, it may behave poorly when moving to a new configuration such as a wireless network. The optimizations performed by Scalpel take the current configuration into account, allowing it to select strategies that give good performance in a wider range of configurations.