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Proceedings of the 10th Koli Calling International Conference on computing Education Research

作者： McCartney, Robert Eckerdal, Anna Moström, Jan Erik Sanders, Kate Thomas, Lynda Zander, Carol Department of Computer Science and Engineering University of Connecticut Storrs CT United States Department of Information Technology Uppsala University Uppsala Sweden Department of Computing Science Umeå University 901 87 Umeå Sweden Math/CS Department Rhode Island College Providence RI United States Department of Computer Science Aberystwyth University Aberystwyth United Kingdom Computing and Software Systems University of Washington Bothell Bothell WA United States

ISBN: (纸本)9781450305204

In this paper we present background and early results from an investigation of how computing students learn computer science topics through informal means, that is, outside of organized classes. We provide some overall perspective by discussing the variety of research areas that fall under the general \informal learning" name. From there we propose specific research questions that concern what the students learn, what resources they bring to bear, what strategies they employ, and how they evaluate their progress. Preliminary results indicate that students primarily learn specific technologies, and that both their motivation and evaluation are closely tied to projects (at school, work, or home). Copyright © 2010 ACM.

关键词： Students

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Gene Expression Classification with a Novel Coevolutionary Based Learning Classifier System on Public Clouds

Gene Expression Classification with a Novel Coevolutionary B...

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IEEE International Conference on E-Science Workshops

作者： Christian Vecchiola Mani Abedini Michael Kirley Xingchen Chu Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory University of Melbourne Australia Department of Computer Science and Software Engineering University of Melbourne Australia

Microarray technology allows for the simultaneous monitoring of thousands of genes expressions per sample. Unfortunately, the classification of these samples into distinct classes is often difficult as the number of genes (features) greatly exceeds the number of samples. Consequently, there is a need to investigate new, robust machine learning techniques capable of accurately classifying microarray data. In this paper, we present a coevolutionary learning classifier system based on feature set partitioning to classify gene expression data. A distributed implementation, which leverages Cloud computing technologies, is used to address the inherent computational costs of our model. The development and execution of this application was done using the Aneka middleware on the public Cloud (Amazon EC2) infrastructure. Experiments conducted using gene expression profiles demonstrates that the proposed implementation outperforms other well-known classifiers in terms of accuracy. Preliminary analysis into the impact of different Cloud setups on the performance of the classifier are also reported.

关键词： Cloud computing Gene expression Accuracy Machine learning Containers Computer architecture

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A Taxonomy of Autonomic Application Management in Grids

A Taxonomy of Autonomic Application Management in Grids

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Mustafizur Rahman Rajiv Ranjan Rajkumar Buyya Cloud Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia Service Oriented Computing Research Group School of Computer Science and Engineering University of New South Wales Australia

In this paper, we propose a taxonomy that characterizes and classifies different components of autonomic application management in Grids. We also survey several representative Grid systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the similarities and differences of state-of-the-art technologies utilized in autonomic application management from the perspective of Grid computing, but also identifies the areas that require further research initiatives.

关键词： Monitoring Resource management Taxonomy Processor scheduling Decision making Dynamic scheduling

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A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud computing Environments

A Particle Swarm Optimization-Based Heuristic for Scheduling...

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Suraj Pandey Linlin Wu Siddeswara Mayura Guru Rajkumar Buyya Cloud Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia CSIRO Tasmanian ICT Centre Hobart Australia

ISBN: (纸本)9781424466955;9780769540184

Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the `execution time'. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing `Best Resource Selection' (BRS) algorithm. Our results show that PSO can achieve: (a) as much as 3 times cost savings as compared to BRS, and (b) good distribution of workload onto resources.

关键词： Particle swarm optimization Processor scheduling Cloud computing Application software Cost function Computational efficiency Dynamic scheduling Computer science software engineering Application virtualization

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CloudAnalyst: A CloudSim-based Visual Modeller for Analysing Cloud computing Environments and Applications

CloudAnalyst: A CloudSim-based Visual Modeller for Analysing...

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International Conference on Advanced Information Networking and Applications

作者： Bhathiya Wickremasinghe Rodrigo N. Calheiros Rajkumar Buyya The Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering The University of Melbourne Pontifical Catholic University of Rio Grande do Sul

ISBN: (纸本)9781424466955;9780769540184

Advances in Cloud computing opens up many new possibilities for Internet applications developers. Previously, a main concern of Internet applications developers was deployment and hosting of applications, because it required acquisition of a server with a fixed capacity able to handle the expected application peak demand and the installation and maintenance of the whole software infrastructure of the platform supporting the application. Furthermore, server was underutilized because peak traffic happens only at specific times. With the advent of the Cloud, deployment and hosting became cheaper and easier with the use of pay-peruse flexible elastic infrastructure services offered by Cloud providers. Because several Cloud providers are available, each one offering different pricing models and located in different geographic regions, a new concern of application developers is selecting providers and data center locations for applications. However, there is a lack of tools that enable developers to evaluate requirements of large-scale Cloud applications in terms of geographic distribution of both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose CloudAnalyst. It was developed to simulate large-scale Cloud applications with the purpose of studying the behavior of such applications under various deployment configurations. CloudAnalyst helps developers with insights in how to distribute applications among Cloud infrastructures and value added services such as optimization of applications performance and providers incoming with the use of Service Brokers.

关键词： Cloud computing Modeling Simulation

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LM: a miner for scenario-based specifications

LM: a miner for scenario-based specifications

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International Conference on software engineering (ICSE)

作者： Tuan-Anh Doan David Lo Shahar Maoz Siau-Cheng Khoo School of Computing National University of Singapore Singapore School of Information Systems Singapore Management University Singapore Department of Computer Science Software Engineering RWTH-Aachen Germany

We present LM, a tool for mining scenario-based specifications in the form of Live Sequence Charts, a visual language that extends sequence diagrams with modalities. LM comes with a project management component, a wizard-like interface to the mining algorithm, a set of pre- and post-processing extensions, and a visualization module.

关键词： Data mining Semantics Visualization Measurement software engineering Feature extraction software algorithms

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Testing as a Service over Cloud

Testing as a Service over Cloud

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The Fifth IEEE International Symposium on Service-Oriented System engineering(第五届IEEE面向服务的系统工程国际研讨会 SOSE 2010)

作者： Lian Yu Xiangji Chen Yan Zhao Liangjie Tang Linqing Liu Wei-Tek Tsai Wei Zhao School of Software and Microelectronics Peking University P.R. China School of Computing Informatics and Decision Systems Engineering Department of Computer Science an China Research Center IBM Beijing 100094 PRC

Testing-as-a-service (TaaS) is a new model to provide testing capabilities to end users. Users save the cost of complicated maintenance and upgrade effort, and service providers can upgrade their services without impact on the end-users. Due to uneven volumes of concurrent requests, it is important to address the elasticity of TaaS platform in a cloud environment. Scheduling and dispatching algorithms are developed to improve the utilization of computing resources. We develop a prototype of TaaS over cloud, and evaluate the scalability of the platform by increasing the test task load;analyze the distribution of computing time on test task scheduling and test task processing over the cloud;and examine the performance of proposed algorithms by comparing others.

关键词： TaaS (Testing as a Service) SaaS (software as a Service) cloud computing ontology scheduling and dispatching

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Performance Tuning Towards a KVM-Based Low Latency Virtualization System

Performance Tuning Towards a KVM-Based Low Latency Virtualiz...

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International Conference on Information engineering and Computer Science (ICIECS)

作者： Baojing Zuo Kai Chen Alei Liang Haibing Guan Jun Zhang Ruhui Ma Hongbo Yang School of Software Shanghai Jiaotong University Shanghai China School of Information Security Engineering Shanghai Jiaotong University Shanghai China Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University Shanghai China

Utilizing virtualization technology to combine real-time operating system(RTOS)and off-the-shelf time-sharing general purpose operating system (GPOS)is attracting much more interest *** combination has the potential to provide a large application base,and to guarantee timely deterministic response to real-time applications,yet there is no convincible experimental result about its real-time *** this paper,we analyze the interrupt latency of RTOS running on Linux KVM based on some preliminary tunings,and find out System Management Interrupt (SMI) is the main factor which makes the maximum latency unideal, so we propose a method to limit the worst-case interrupt latency in an acceptable ***,we also find out that boosting priority may result in wastes of CPU resources when RTOS is not executing real-time tasks,so we design a co-scheduling mechanism to improve the CPU throughput of the GPOS system.

关键词： Linux Real time systems Kernel Tuning Throughput Hardware

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A Translation Framework for Virtual Execution Environment on CPU/GPU Architecture

A Translation Framework for Virtual Execution Environment on...

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Guoxing Dong Kai Chen Erzhou Zhu Yichao Zhang Zhengwei Qi Haibing Guan School of Software Shanghai Jiaotong University Shanghai China School of Information Security Engineering Shanghai Jiaotong University Shanghai China Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University Shanghai China

GPUs are many-core processors with tremendous computational power. However, as automatic parallelization has not been realized yet, developing high-performance parallel code for GPUs is still very challenging. The paper presents a novel translation framework designed for virtual execution environment based on CPU/GPU architecture. It addresses two major challenges of taking advantage of general purpose computation on graphics processing units (GPGPU) to improve performance: no rewriting the existing source code and resolving binary compatibility issues between different GPUs. The translation framework uses semi-automatic parallelization technology to port existing code to explicitly parallel programming models. It not only offers a mapping strategy from X86 platform to CUDA programming model, but also synchronizes the execution between the CPU and the GPUs. The input to our translation framework is parallelizable part of the program within binary code. With an additional information related to the parallelizable part, the translation framework transforms the sequential code into PTX code and execute it on GPUs. Experimental results on several programs from CUDA SDK Code Samples and Parboil Benchmark Suite show that our translation framework could achieve very high performance, even up to several tens of times speedup over the X86 native version.

关键词： Graphics processing unit Registers Kernel Instruction sets Driver circuits Programming Computer architecture

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Feature-Oriented Nonfunctional Requirement Analysis for software Product Line

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Journal of Computer Science & Technology 2009年第2期24卷 319-338页

作者：彭鑫 Seok-Won Lee 赵文耘 School of Computer Science Fudan University Knowledge-Intensive Software Engineering Research Group Department of Software and Information Systems College of Computing and Informatics University of North Carolina at Charlotte

Domain analysis in software product line （SPL） development provides a basis for core assets design and imple- mentation by a systematic and comprehensive commonality/variability analysis. In feature-oriented SPL methods, products of the domain analysis are domain feature models and corresponding feature decision models to facilitate application-oriented customization. As in requirement analysis for a single system, the domain analysis in the SPL development should con- sider both flmctional and nonfunctional domain requirements. However, the nonfunctional requirements （NFRs） are often neglected in the existing domain analysis methods. In this paper, we propose a context-based method of the NFR analysis for the SPL development. In the method, NFRs are materialized by connecting nonfunctional goals with real-world context, thus NFR elicitation and variability analysis can be performed by context analysis for the whole domain with the assistance of NFR templates and NFR graphs. After the variability analysis, our method integrates both functional and nonfunc- tional perspectives by incorporating the nonfunctional goals and operationalizations into an initial functional feature model. NFR-related constraints are also elicited and integrated. Finally, a decision model with both functional and nonfunctional perspectives is constructed to facilitate application-oriented feature model customization. A computer-aided grading system （CAGS） product line is employed to demonstrate the method throughout the paper.

关键词： software product line nonfunctional requirement domain analysis feature-oriented method variability analysis

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