ICSE 2011 Workshops
33rd International Conference on Software Engineering
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Workshop on Software Engineering for Cloud Computing (SECLOUD 2011), May 22, 2011, Waikiki, Honolulu, HI, USA

SECLOUD 2011 – Proceedings

Contents - Abstracts - Authors

Workshop on Software Engineering for Cloud Computing (SECLOUD 2011)


Title Page

Front matter for the ICSE 2011' SECLOUD Workshop.

Research Papers

An Internationally Distributed Cloud for Science: The Cloud-Enabled Space Weather Platform
Everett Toews, Barton Satchwill, Robert Rankin, John Shillington, and Todd King
(Cybera Inc., Canada; University of Alberta, Canada; UC Los Angeles, USA)
The purpose of the Cloud-Enabled Space Weather Platform (CESWP) project is to bring the power and flexibility of cloud computing to space weather physicists. The goal is to lower the barriers for the physicists to conduct their science. That is, to make it easier to collaborate with other scientists, develop space weather models, run simulations, produce visualizations and enable provenance. Success of the project is measured by the broad acceptance and use of the platform by the space weather science community.
To deliver cloud computing and storage, infrastructure as a service, the project has built an internationally distributed cloud based on Eucalyptus [1]. To provide a graphical user interface for the physicists to interact with we selected the Groovy programming language and the Grails web framework. To construct the software we followed the Scrum agile software development methodology. This paper will report on the motivation and risks of such an undertaking. It will also report on the suitability of Eucalyptus as a cloud framework and the utility of the tools used to build an application on top of it.

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Modeling Cloud Failure Data: A Case Study of the Virtual Computing Lab
Meiyappan Nagappan, Aaron Peeler, and Mladen Vouk
(North Carolina State University, USA)
Virtual Computing Lab is a higher education cloud computing environment that on demand, allocates a chosen software stack on the required hardware and gives access to the customers, in this case NCSU students, faculty and staff. VCL has been in operation since 2004. An important component of the quality of the services provided by a cloud is the reliability and availability. For example, typical availability of the system exceeds 0.999, and reservation reliability is in the 0.99 range. VCL provides comprehensive information (provenance, logs, etc.) about its execution, its resources, and its performance. We mined the VCL log files to find out more about its reliability and availability, and the character of its faults and failures. This paper presents some of these results.

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Uni4Cloud: An Approach based on Open Standards for Deployment and Management of Multi-cloud Applications
Americo Sampaio and Nabor Mendonça
(Universidade de Fortaleza, Brazil)
Cloud computing is changing the way applications are being developed, deployed and managed. Application developers can focus on business and functionality and leverage infrastructure clouds (IaaS) to provide them low cost resources (e.g., computation, storage, and networking) that can be controlled based on application needs. However, current IaaS cloud developers have to deal with daunting tasks to configure and deploy their applications in different cloud providers. This paper presents the Uni4Cloud approach that facilitates to model, deploy and configure complex applications in multiple infrastructure clouds. We demonstrate through an enterprise application case study how Uni4Cloud facilitates to deploy components (e.g., application server, database, load balancer) in multiple clouds using a model-based approach that helps to automatically configure and deploy applications independent of IaaS cloud provider. Moreover, the approach is based on cloud computing standards such as the Open Virtualization Format (OVF) and Open Cloud Computing Interface (OCCI) to favor interoperability and to avoid being locked in to specific cloud providers.

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Application Migration to Cloud: A Taxonomy of Critical Factors
Van Tran, Jacky Keung, Anna Liu, and Alan Fekete
(University of New South Wales, Australia; The Hong Kong Polytechnic University, China; NICTA, Australia; The University of Sydney, Australia)
Cloud computing has attracted attention as an important platform for software deployment, with perceived benefits such as elasticity to fluctuating load, and reduced operational costs compared to running in enterprise data centers. While some software is written from scratch specially for the cloud, many organizations also wish to migrate existing applications to a cloud platform. Such a migration exercise to a cloud platform is not easy: some changes need to be made to deal with differences in software environment, such as programming model and data storage APIs, as well as varying performance qualities. We report here on experiences in doing a number of sample migrations. We propose a taxonomy of the migration tasks involved, and we show the breakdown of costs among categories of task, for a case-study which migrated a .NET n-tier application to run on Windows Azure. We also indicate important factors that impact on the cost of various migration tasks. This work contributes towards our future direction of building a framework for cost-benefit trade-off analysis that would apply to migrating applications to cloud platforms, and could help decision-makers evaluate proposals for using cloud computing.

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Cloud Adoption: A Goal-Oriented Requirements Engineering Approach
Shehnila Zardari and Rami Bahsoon
(University of Birmingham, UK)
We motivate the need for a new requirements engineering methodology for systematically helping businesses and users to adopt cloud services and for mitigating risks in such transition. The methodology is grounded in goal oriented approaches for requirements engineering. We argue that Goal Oriented Requirements Engineering (GORE) is a promising paradigm to adopt for goals that are generic and flexible statements of users’ requirements, which could be refined, elaborated, negotiated, mitigated for risks and analysed for economics considerations. We describe the steps of the proposed process and exemplify the use of the methodology through an example. The methodology can be used by small to large scale organisations to inform crucial decisions related to cloud adoption.

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Evaluating Cloud Computing in the NASA DESDynI Ground Data System
John J. Tran, Luca Cinquini, Chris A. Mattmann, Paul A. Zimdars, David T. Cuddy, Kon S. Leung, Oh-Ig Kwoun, Dan Crichton, and Dana Freeborn
(Jet Propulsion Laboratory, USA; University of Southern California, USA)
The proposed NASA Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission would be a first-of-breed endeavor that would fundamentally change the paradigm by which Earth Science data systems at NASA are built. DESDynI is evaluating a distributed architecture where expert science nodes around the country all engage in some form of mission processing and data archiving. This is compared to the traditional NASA Earth Science missions where the science processing is typically centralized. What's more, DESDynI is poised to profoundly increase the amount of data collection and processing well into the 5 terabyte/day and tens of thousands of job range, both of which comprise a tremendous challenge to DESDynI's proposed distributed data system architecture. In this paper, we report on a set of architectural trade studies and benchmarks meant to inform the DESDynI mission and the broader community of the impacts of these unprecedented requirements. In particular, we evaluate the benefits of cloud computing and its integration with our existing NASA ground data system software called Apache Object Oriented Data Technology (OODT). The preliminary conclusions of our study suggest that the use of the cloud and OODT together synergistically form an effective, efficient and extensible combination that could meet the challenges of NASA science missions requiring DESDynI-like data collection and processing volumes at reduced costs.

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A Cloud-Enabled Regional Climate Model Evaluation System
Andrew F. Hart, Cameron E. Goodale, Chris A. Mattmann, Paul A. Zimdars, Dan Crichton, Peter Lean, Jinwon Kim, and Duane Waliser
(Jet Propulsion Laboratory, USA; University of Southern California, USA; UC Los Angeles, USA)
The climate research community is increasingly interested in utilizing direct, observational measurements to validate model output in an effort to tune those models to better approximate our planet’s dynamic climate. The current emphasis on performing these comparisons at regional, as opposed to global, scales presents challenges both scientific and technical, since regional ecosystems are highly heterogeneous and the available data is not readily consumed on a regional basis. If provided with a common approach for efficiently accessing and utilizing the existing observational datasets, climate researchers have the potential to effect lasting societal, economic and political benefits. A key challenge, however, is that model-to-observational comparison requires massive quantities of data and significant computational capabilities. Further complicating matters is the fact that, currently, observational data and model outputs exist in a variety of data formats, utilize varying degrees of specificity and resolution, and reside in disparate, highly heterogeneous data systems. In this paper we present a soft- ware architectural approach that leverages the advantages of cloud computing and modern open-source software technologies to address the regional climate modeling problem. Our system, dubbed RCMES, is highly scalable and elastic, allows for both local and distributed management of the satellite observations and generated model outputs, and delivers this information to climate researchers in a way that is easily integrated into existing climate simulations and statistical tools.

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A Tale of Migration to Cloud Computing for Sharing Experiences and Observations
Muhammad Ali Babar and Muhammad Aufeef Chauhan
(IT University of Copenhagen, Denmark; Mälardalen University, Sweden)
Cloud computing is an emerging paradigm, which promises to make the utility computing model comprehensively implemented by using virtualization technologies. An increasing number of enterprises have started providing and using Cloud-enabled infrastructures and services. However, the advancement of cloud computing poses several new challenges to existing methods and approaches to develop and evolve software intensive systems. This paper reports our experiences and observations gained from migrating an Open Source Software (OSS), Hackystat, to cloud computing. We expect that our description of Hackystat’s architecture prior and after migration and design decisions can provide some guidance about modifying architecture of a service-based system for cloud computing. Moreover, we also hope that our experiences reported in this paper can contribute to the identification of some research questions for improving software engineering support for developing and evolving cloud-enabled systems.

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A MapReduce Workflow System for Architecting Scientific Data Intensive Applications
Phuong Nguyen and Milton Halem
(University of Maryland Baltimore County, USA)
MapReduce is promising for developing both scalable business and scientific data intensive applications. However, there are few existing scientific workflow systems which can benefit from the MapReduce programming model. We propose a workflow system for integrating structure, and orchestrating MapReduce jobs for scientific data intensive workflows. The system consists of a simple workflow design C++ API, a job scheduler, and a runtime support system for Hadoop or Sector/Sphere frameworks. A climate satellite data intensive processing and analysis application is developed as a use case and an evaluation for the workflow system. The evaluation shows that it is possible to make the steps in the climate data intensive application automatically from data gridding to complex data analysis using the workflow system. The performance of the climate analysis application is significantly improved by the enabled MapReduce workflow system compared with the sequential embarrassing parallel methods. The overhead of the workflow system is negligible. However, the graphic user interface is still under development for the workflow system.

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An Application Architecture to Facilitate Multi-Site Clinical Trial Collaboration in the Cloud
Jonathan Sharp
(City of Hope, USA)
The regulatory environment in the U.S. healthcare sector and the privacy concerns surrounding personal health information complicate research collaborations between investigators, especially collaborations across healthcare organizational boundaries. This paper examines software systems traditionally employed by healthcare providers to utilize clinical data for research purposes within and between organizations. A conceptual software architecture utilizing cloud-based services is then proposed that, it is suggested, may facilitate collaboration between researchers in multi-site clinical trials. Several related challenge areas are then identified.

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Demonstration of LMMP (Lunar Mapping and Modeling) Using Amazon's Elastic Compute Cloud
Bach Bui, George Chang, Richard Kim, Emily Law, and Shan Malhotra
(Jet Propulsion Laboratory, USA)
The Lunar Mapping and Modeling Project (LMMP) is currently being built by NASA. The goal is to provide a single point of access to the best state of knowledge of the moon’s terrain, rock and crater fields, resource maps, lighting conditions and thermal conditions. The architecture and design employ a variety of technologies, allowing for execution of complex models, the processing of large data sets and the distribution of the information, over the internet, to both authenticated users and the general public. The architecture supports a variety of light-weight clients including a Flash based display, an iPAD/iPhone interface and a set of programmatic APIs that allow rich clients to interact with the LMMP system.

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Demonstration of LMMP Workflow System Using Cloud Computing Architecture
George Chang, Emily Law, and Shan Malhotra
(Jet Propulsion Laboratory, USA)
The Lunar Mapping and Modeling Project (LMMP) is currently being built by NASA. The goal is to provide a single point of access to the best state of knowledge of the moon’s terrain, rock and crater fields, resource maps, lighting conditions and thermal conditions. The LMMP contains a workflow system that allows us to allocate jobs to remote computing resources. We will demonstrate this workflow capability.

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Demonstration of LMMP Automated Performance Testing Using Cloud Computing Architecture
George Chang, Emily Law, and Shan Malhotra
(Jet Propulsion Laboratory, USA)
The Lunar Mapping and Modeling Project (LMMP) is currently being built by NASA. The goal is to provide a single point of access to the best state of knowledge of the moon’s terrain, rock and crater fields, resource maps, lighting conditions and thermal conditions. The project uses cloud computing scalable infrastructure to support users. This demonstration will show how cloud computing can be used to support large scale automated testing, simulating users from around the world, in a cost effective manner.

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Building Climatological Services on the Cloud
Thomas Huang, Michael E. Gangl, and Andrew W. Bingham
(Jet Propulsion Laboratory, USA)
The NASA Physical Oceanographic Distributed Active Archive Center (PO.DAAC) at Jet Propulsion Laboratory is funded by the NASA Earth Science Data and Information System (ESDIS) project to conduct a study of cloud services for data management, data access and data processing. The study is to improve our understanding and articulate the cost/benefit of cloud technologies for the NASA Distributed Active Archive Centers (DAACs) and Science Investigator- led Production Systems (SIPs). This demonstration focuses on our experience in developing climatology services using Apache Hadoop to store and analyze temporal and spatial characteristics of scatterometer data over Antarctica.

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edubase Cloud: An Open-source Cloud Platform for Cloud Engineers
Nobukazu Yoshioka, Shigetoshi Yokoyama, Yoshionori Tanabe, and Shinichi Honiden
(National Institute of Informatics, Japan)
Education for cloud engineers is crucial in terms of innovation in the development of cloud technologies. We propose a new cloud platform based on open-source software that uses multi-clouds for the education.

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