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A computational grid ensures the on-demand delivery of computing resources, in a security-aware, shared, scalable, and standards-based computing environment. A major concern is how to evolve a general and an encompassing framework that guarantees users’ satisfaction measured as Quality of Services (QoS). To obtain a higher QoS, effective QoS perceived by subscribers (users) must conform to specified QoS agreements in the Service Level Agreements (SLAs) document—a legal contract between the Grid Services Provider (GSP) and users. Sometimes the effective user QoS does not conform to the specifications in the SLA because of the vagueness in linguistic definitions in the SLA. Existing approaches overcommitted resources to meet QoS. In this paper, we propose a fuzzy logic framework for calibrating a grid resources user-QoS that addresses the vagueness in linguistic definitions of the SLA document without overcommitting grid resources. [ABSTRACT FROM AUTHOR]

The problem of growing computational complexity in the finance industry demands manageable, high-speed and real-time solutions in solving complex mathematical problems such as option pricing. In current option trading scenarios, determining a fair price for options “any time” and “anywhere” has become vital yet difficult computational problem. In this study, we have designed, implemented, and deployed an architecture for pricing options on-line using a hand-held device that is J2ME-based Mobile computing-enabled and is assisted by web mining tools. In our architecture, the client is a MIDP user interface, and the back end servlet runs on a standalone server bound to a known port address. In addition, the server uses table-mining techniques to mine real-time data from reliable web sources upon the mobile trader’s directive. The server performs all computations required for pricing options since mobile devices have limited battery power, low bandwidth, and low memory. We have parallelized and implemented various computational techniques such as binomial lattice and finite differencing. To the best of our knowledge, this is one of the first studies that facilitate the mobile-enabled-trader to compute the price of an option in ubiquitous fashion. This architecture aims at providing the trader with various computational techniques to avail (to provide results from approximate to accurate results) while on-the-go and to make important and effective trading decisions using the results that will ensure higher returns on investments in options. [ABSTRACT FROM AUTHOR]

Pricing of derivatives is one of the central problems in computational finance. Since the theory of derivative pricing is highly mathematical, numerical techniques such as lattice approach, finite-difference and finite-element among others have been employed. Recently fast Fourier transform (FFT) has been employed for derivative pricing in sequential computers. In this paper, we report development of a multithreaded FFT pricing algorithm and performance evaluation on a multithreaded platform. The focus of this study is on the effectiveness of using a parallel computer for financial problems and performance evaluation of a multithreaded algorithm for finance applications such as derivative pricing. In general, a parallel algorithm for FFT, with blocked data distribution of N elements on P processors, involves communication for log P iterations and terminates after log N iterations. The first (log N - log P) iterations therefore, require no communication and a sequential algorithm can be used in each processor. We call this a local algorithm. At the end of the (log N - log P) iterations, the processors switch to a multithreaded algorithm where sending and receiving of threads is through message passing. We call this a remote algorithm. The algorithm has been implemented on the EARTH (Efficient Architecture for Running THreads) multithreaded platform. Our results indicate that the FFT multithreaded algorithm for option pricing is very efficient giving a relative speedup of 50% on 64 processors. This study reveals an important commercial application for High Performance Computing. [ABSTRACT FROM AUTHOR]