Approximations and Bounds for (n, k) Fork-Join Queues: A Linear Transformation Approach

Compared to basic fork-join queues, a job in (n, k) fork-join queues only needs its k out of all n sub-tasks to be finished. Since (n, k) fork-join queues are prevalent in popular distributed systems, erasure coding based cloud storages, and modern network protocols like multipath routing, estimating the sojourn time of such queues is thus critical for the performance measurement and resource plan of computer clusters. However, the estimating keeps to be a well-known open challenge for years, and only rough bounds for a limited range of load factors have been given. In this paper, we developed a closed-form linear transformation technique for jointly-identical random variables: An order statistic can be represented by a linear combination of maxima. This brand-new technique is then used to transform the sojourn time of non-purging (n, k) fork-join queues into a linear combination of the sojourn times of basic (k, k), (k+1, k+1), ..., (n, n) fork-join queues. Consequently, existing approximations for basic fork-join queues can be bridged to the approximations for non-purging (n, k) fork-join queues. The uncovered approximations are then used to improve the upper bounds for purging (n, k) fork-join queues. Simulation experiments show that this linear transformation approach is practiced well for moderate n and relatively large k.Comment: 10 page

Frequentist and Bayesian Quantum Phase Estimation

Frequentist and Bayesian phase estimation strategies lead to conceptually different results on the state of knowledge about the true value of the phase shift. We compare the two frameworks and their sensitivity bounds to the estimation of an interferometric phase shift limited by quantum noise, considering both the cases of a fixed and a fluctuating parameter. We point out that frequentist precision bounds, such as the Cram\`er-Rao bound, for instance, do not apply to Bayesian strategies and vice-versa. Similarly, bounds for fluctuating parameters make no statement about the estimation of a fixed parameter.Comment: 4 figure

The impact of customer relationship management on enhancing the customers loan satisfaction in commercial banks in Cameroon.

CRM is a strategy and business process encompassing many technologies by which firms acquire, manage, and serve customers. The goal of a bank implementing a CRM strategy is to gather and organize both data and knowledge about customers in order to provide support to advisers, salesforce, customer service representatives, and probably end customers. Therefore, this research examined the impact of customer relationship management activities in the commercial banks in Cameroon, in order to evaluate the customer satisfaction on loan acquisition. CRM solutions are very much in high demand, especially since financial services institutions want to provide as much transparency and customized service to customer as possible in order to enhance their satisfaction. CRM systems automate critical sales, marketing, and client service activities at a financial institution. Keyword: banks, customer relationship management, customer satisfaction, loan acquisition DOI: 10.7176/EJBM/11-12-23 Publication date: April 30th 201

Research on Social Q&A Community Knowledge Transfer Model Based on Follow Relationship Network

Social Q&A community is the focus of knowledge sharing and knowledge acquisition in the Internet era. The study of user group and community structure of social Q&A community is helpful to discover its model of knowledge transfer and the characteristics of the user group. Based on the study of relevant literature and social Q&A community characteristics, we build social relationships network model and use the method of social network analysis to study user groups. Finally, we find the user characteristics and behavior pattern. Furthermore, we analyze the knowledge transfer model of social Q&A community

Precise Request Tracing and Performance Debugging for Multi-tier Services of Black Boxes

As more and more multi-tier services are developed from commercial components or heterogeneous middleware without the source code available, both developers and administrators need a precise request tracing tool to help understand and debug performance problems of large concurrent services of black boxes. Previous work fails to resolve this issue in several ways: they either accept the imprecision of probabilistic correlation methods, or rely on knowledge of protocols to isolate requests in pursuit of tracing accuracy. This paper introduces a tool named PreciseTracer to help debug performance problems of multi-tier services of black boxes. Our contributions are two-fold: first, we propose a precise request tracing algorithm for multi-tier services of black boxes, which only uses application-independent knowledge; secondly, we present a component activity graph abstraction to represent causal paths of requests and facilitate end-to-end performance debugging. The low overhead and tolerance of noise make PreciseTracer a promising tracing tool for using on production systems