c. yang, q. huang, g. zhi, z. li, c. xu, y. jiang, j. li, 2013. chapter 17 cloud computing research...

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308. Chapter 17 Cloud computing research for geosciences Chaowei Yang, Qunying Huang, Zhipeng Gui, Zhenlong Li, Chen Xu, Yunfeng Jiang, and Jing Li 1

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Chapter 17 Cloud computing researchfor geosciences

Chaowei Yang, Qunying Huang, Zhipeng Gui, Zhenlong Li, Chen Xu, Yunfeng Jiang, and Jing Li

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Learning Objectives

Understanding the future research directions of cloud computing for geosciences1. Geoscience Application Visions2. Technology Advancements3. Social Aspects

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Geoscience Application Visions

• Fundamental geospatial science enquiries• Integrating geoscience and other domains for new

discoveries• Application visions

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Geoscience Application Visions: Fundamental geospatial science enquires

• Fundamental geoscience research is a fore-front of human knowledge pursuant.

• Better understanding earthquake and tornado to predict them.

• Bridging subdomains of geosciences by sharing knowledge and research (data, information, models).

• Challenges to cloud service with the global integration of the resources

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Geoscience Application Visions: Integrating geoscience and other domains for new discoveries

• McGrath (2011) argued that the most important scientific discoveries are happening now, but within, and especially cross seemly unrelated domains of sciences.

• The integration of science domains will require unprecedented tools and methodologies to facilitate the integrated scientific discoveries.

• Cloud computing has potential to facilitate the scientific domain integration with a transcending computing service.

• Identifying the computing infrastructure needs for each domain and cross domains will help plan optimized cloud services to support the integration for new discoveries.

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Geoscience Application Visions: Application Visions

• Application vision drives the technology advancements and scientific discovery, e.g., Digital Earth vision drived the emergency of google earth and bing maps.

• Smart Earth, smart city, and virtual geographic environment may drive the development of data, information and knowledge integration.

• Global combat simulator would drive relevant technology for real time data.

• New mapping process facilitate the vision would be ideal. • All these drive the readiness of a ready computing

infrastructure with significant relevant research conducted.

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Learning Objectives

Understanding the future research directions of cloud computing for geosciences1. Geoscience Application Visions2. Technology Advancements3. Social Aspects

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Technology Advancements

1. Cloud Evaluation and Selection2. Cloud Service Resource Management3. Data Backup and Synchronization4. Interoperability5. New Visualization and Interactive Systems6. Reliability and Availability7. Real-time Simulation and Access

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Technology Advancements: Cloud Evaluation and Selection

• Cloud criteria are the foundation for measuring, evaluating and selecting cloud services.

• Return On Investment (ROI) is a significant determining factor in cloud selection from cloud consumers’ perspective.

• The third-party auditing is essential to provide a trustable understanding about the performance, reliability and consistency of cloud services.

• To assist cloud customers understand the advantages and disadvantages of cloud services as well as make a wise selection, advanced selection principles and models should be investigated.

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Technology Advancements: Cloud Service Resource Management

• The optimized cloud resource management and utilization can not only improve resource utilization and performance, but also reduce the budget, energy and labor, for both cloud providers and cloud consumers.

• Resource stranding and fragmentation seriously obstruct the utilization of computing resources and also increase the management cost.

• To reduce the cost raised by networks of geographically dispersed data centers, joint optimization of network and data center resources, and new mechanisms for geo-distributing state should also be proposed (Greenberg et al., 2009).

• Saving electricity used on computing resources is important to reduce global energy consumption. Beloglazovet al. (2010) proposed an energy efficient resource management system for virtualized cloud data centers to reduce operational costs and provide required Quality of Service (QoS).

• Automating management of VMs can reduce labor cost and time for cloud providers.

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Technology Advancements: Data Backup and Synchronization

• Cloud storage services are changing the way people access and store data. However, backup and synchronization of data becomes the scalability bottleneck for scientific applications to best utilize the on-demand computing power.

• How to access in parallel the storage should be researched to change that multiple servers have to access the data store sequentially (Das et al. 2009).

• Data backup and synchronization is one of the most important research issues within cloud computing.

• The methodologies for data backup and synchronization with massive geospatial data and frequent operations need to be further explored, and the cloud providers should integrate those strategies into their cloud services that can be easily configured by the cloud consumers.

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Technology Advancements

1. Cloud Evaluation and Selection2. Cloud Service Resource Management3. Data Backup and Synchronization4. Interoperability5. New Visualization and Interactive Systems6. Reliability and Availability7. Real-time Simulation and Access

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Technology Advancements: Interoperability• With many cloud service models (e.g., IaaS, PaaS, and SaaS) and providers (e.g.,

Amazon EC2 and Microsoft Azure), interoperability among different cloud services is of a great interest for consumers to take full advantage of the technology.

• Several organizations start to address these issues from different aspects and directions. NIST is leading the effort to develop: • Standards to support interoperability for all cloud models from low layer

stack IaaS to high layer stack SaaS. Specific standards are required for particular cloud service models.

• Standard mediator APIs to enable cloud consumers to utilize, manage, compare and integrate cloud services from different cloud providers. For example, applications built on various cloud services can be used interoperablely by using the standard mediator API.

• Cloud resource management protocols and security mechanisms to facilitate cloud interoperability. For example, orchestration layers can be used to build business processes and workflows using the cloud services provided by different cloud providers (Parameswaran et al. 2009).

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Technology Advancements

Existing methods, techniques and tools may not be applicable in the cloud computing environment for visualization because of the complexities of cloud framework and supporting infrastructure. Future research should focus on: 1. Customizing existing visualization algorithms and approaches with cloud

services to support ultra-scale interactive visualization where computing intensity is always a bottleneck.

2. Exploring spatiotemporal principles in governing the implementations of visualization and interactive systems with cloud services.

3. Designing an adaptive workflow that can best utilize cloud resources, from data preprocessing to final display, in a cloud environment.

4. Building a remote cloud visualization service to allow users from anywhere to interactively access the data, images, videos, and applications as services.

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Technology Advancements: Reliability and Availability

• Reliability and availability refer to being able to efficiently access the environment from different regions.

• There are two possible reasons for the presence of service unavailability and unreliability, including 1) the servers, which consist of multiple hard disks, memory modules, network cards, and processors, may fail even being carefully engineered (Venkatesh et al. 2010), and 2) failure in Internet that help deliver cloud services.

• Many research efforts are increasingly put on reducing the hardware and network failures for improving reliability of cloud computing.

• In order to obtain the confidences for large enterprises shifting computing styles to cloud computing, great efforts should be put on investigating the strategies for cloud system failure predications, responses, and recoveries from the perspectives of both the cloud providers and cloud consumers.

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Technology Advancements: Real-time Simulation and Access

• Real-time simulation and access are essential for different types of decision supports from emergency responses to individual daily life.

• New devices, such as tablets and smartphones, make a real-time response system easier accessible than ever before.

• This capability also introduces a tremendous workload for cloud services, especially during the emergent time period, where massive requests are possible.

• More research should be conducted including theory-based simulation and multi-scale, multi-component modeling, as well as data intensive, and interactive visualization capability for both cloud computing platforms and applications (NRC 2011a and 2011b).

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Social Aspects

• Cloud Management• Cloud Outreach• Security and Regulations• Global Collaboration

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Social Aspects: Cloud Management

• There are five cloud computing characteristics defined by NIST (2011) to depict features of cloud computing from different perspectives.

• A common requirement for implementing the characteristics is to automate the management process across cloud computing infrastructure.

• At the operational level, user interventions should be minimized to ensure autonomous self-regulation of cloud computing.

• Self-regulation demands the breaking down of existing organizational restrictions to support the automatic processing (Choi and Lee 2010).

• At the technological level, standards should be created and supported by different services.

• More difficulties come from human factors, for example, could a cloud service be trusted by cloud consumers to host their data and applications?

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Social Aspects: Cloud Outreach• Cloud service for solution will be a progressive process and needs deliberate

communication with the public. E.g., the notion of utility computing makes the public, or even some information technology decision makers to believe that we only need 100 to 200 computing centers across the U.S. and all the computing needs can be served from the centers. But the fact is that cloud computing will be limited by network bandwidth and the spatiotemporal principles constrain us to collocate our data with computing infrastructure, therefore, proximity has to be considered between cloud computing and data, users, problems, and applications.

• Another underestimated impact is security. Although a cloud system may be more secured from attacks but the overall security of data, privacy and meeting government needs sometimes prevent government from adopting the public cloud.

• Therefore, it is essential to convey the right message to the public about cloud computing so that it is neither over committed or less communicated.

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Social Aspects: Security and Regulations• Security and regulations are concerned with utilizing cloud computing for certain

information and applications, for example, labor and social security applications (Lu 2010) and are among the biggest concerns when individuals or enterprises are considering adopting cloud services.

• Traditional IT environment gives consumers complete control of their IT systems, with cloud services, enterprises are losing the physical control of the IT systems.

• Enterprise may be prohibited to host their data and information in a foreign country. • All federal IT applications must meet certain security requirements of Federal Risk and

Authorization Management Program (FedRAMP). FISMA requires federal agencies to develop, document, and implement an information security system for its data and infrastructure.

• Some companies also need to follow the industry rules and standards. • Improvements to cloud service security and privacy levels are needed for data and services

including secure web environment for developing applications (O’Leary and Kaufman 2011) and others.

• Under such a circumstance, it is very important that cloud providers enable consumers or third parties to review the security and privacy policy and verify its completeness and validity.

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Social Aspects: Global Collaboration

• Globalization calls for global collaboration to leverage strengths from different regions and to serve local customers for maximizing impacts.

• How cloud service can provide global collaboration infrastructure?

• How cloud services and the global Internet of Things can be integrated to support smart city, region, country, Earth?

• How digital earth, new geoscience can be enabled by cloud services in the global context?

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

Discussion Questions

1. What are the most advanced geoscience research and applications you have in your organization?

2. How that drives the computing infrastructure demands? 3. What are the cloud computing adoption challenges you have? 4. How did you coordinate computing sharing within your

organization, collaborators, and other organizations. 5. How cloud service could be advanced to address those

problems?

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C. Yang, Q. Huang, G. Zhi, Z. Li, C. Xu, Y. Jiang, J. Li, 2013. Chapter 17 Cloud computing research for geosciences, In Spatial Cloud Computing: a practical approach, edited by C.Yang, Q. Huang, Z. Li, C. Xu, K. Liu, CRC Press: pp. 295-308.

ReferencesDigital Earth vision: Craglia, M., K. de Bie, D. Jackson et al. 2012 Digital Earth 2020: towards

the vision for the next decade. International Journal of Digital Earth 5, no. 1: 4-21.

Earth Science Research Needs: NRC, 2012a, International Science in the National Interest at the U.S.

Geological Survey, The national academies press, Washington DC, 161pp. NRC, 2012b, New Research Opportunities in the Earth Sciences, The

national academies press, Washington DC, 117pp.

Cloud Computing Research Needs: Yang, C., Y. Xu, and D. Nebert. 2013. Redefining the Possibility of Digital

Earth and Geosciences with Spatial Cloud Computing, International Journal of Digital Earth, 6 (4) 1-15.

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