Paper: (Yang et al. 2011)
(Yang et al. 2011) claims that “the geospatial sciences face great information technology (IT) challenges
in the twenty-first century: data intensity, computing intensity, concurrent access intensity, and
spatiotemporal intensity.” It goes on to say that these challenges call for a computing infrastructure that
can resolve these issues so that scientists can be relieved of IT tasks and focus on their research. The
computing infrastructure, according to the authors, should be able to: (1) better support discovery,
access, utilization, and processing of data; (2) provide real-time IT resources to enable real-time
applications; (3) deal with access spikes; (4) ensure reliability and scalability for massive number of
concurrent users. This problem bears significant resemblance to the one that motivates the birth of
spatial databases. Thus, potential solutions, together with their underlying methodologies, will help
bring insight into the design and implementation of spatial databases.
(Yang et al. 2011) succeeds at explaining the correlation between cloud computing and geospatial
sciences. Namely, it points out that geospatial sciences act both as a driver and an enabler in the
development of new computing technologies. The paper then presents the framework of spatial cloud
computing (SCC), as an example of how spatial sciences can help shape cloud computing. Four scenarios
corresponding to the four aspects of IT challenges listed earlier are analyzed to give proof. The paper
concludes with a list of factors the success of SCC relies on. The most significant contribution of the
paper is the well-presented relationship between cloud computing and geospatial sciences. The authors
discuss, in great detail, characteristics of both challenges faced by spatial sciences and cloud computing.
They also point out how the features of cloud computing, either individually or combining together,
addresses the issues hampering the progress of spatial sciences. It becomes clear at the end of the
discussion that cloud computing can both contribute to and benefit from geospatial sciences.
(Yang et al. 2011) presents the following key concepts:
(1) IT challenges faced by geospatial sciences:
a. Data intensity – the volume, scalability, and diversity of data collected by sensors at faster pace are
posing grand challenges in the organization and processing of them;
b. Computing intensity – the complexity of algorithms and models developed based on understanding of
the datasets and Earth phenomena renders it time-consuming or even impossible to execute them;
c. Concurrent intensity – massive number of concurrent accessing requests to distributed geographic
information processing services makes it hard to ensure fast access and deal with access spikes;
d. Spatiotemporal intensity – the intrinsic space-time dimensions of geospatial datasets.
(2) Elements of geospatial sciences
Earth observation, parameter extraction, model simulation, decision support, social impact, and
feedback are recognized as practical approaches to resolve regional, local, and global issues.
(3) Cloud computing services
a. Infrastructure as a Service (IaaS)
IaaS delivers computer infrastructure including physical machines, networks, storage and system
software, as virtualized computing resources over computer networks (Buyya et al. 2009). Users can
configure, deploy, and run operating systems and applications based on them. Examples include the
Amazon Elastic Compute Cloud (EC2, http://aws.amazon.com/ec2/).
b. Platform as a Service (PaaS)
PaaS provides a platform service including a layer of cloud-based software and Application Programming
Interface (API) besides a computing platform for software developers to develop applications. Users can
develop or run existing applications on such a platform. Examples are Microsoft Azure
(http://www.microsoft.com/windowsazure) and Google App Engine.
c. Software as a Service (SaaS)
SaaS provides applications for end users. These applications used to be provided through web browsers.
Examples are Salesforce.com and Google’s gmail and apps (http://www.google.com/apps/).
d. Data as a Service (DaaS)
DaaS supports data discovery, access, and utilization and delivers data and data processing on demand
to end users regardless of geographic or organizational location of provider and consumer (Olson 2010).
(4) Characteristics of cloud computing (Mell and Grance 2009, Yang et al. 2011a, b)
a. on-demand self-service;
b. broad network access;
c. resources pooling;
d. rapid elasticity;
e. measured service
(5) Spatial Cloud Computing (SCC)
Spatial cloud computing refers to the cloud computing paradigm that is driven by geospatial sciences,
and optimized by spatiotemporal principles for enabling geospatial science discoveries and cloud
computing within distributed computing environment.
(6) Geospatial principles
a. physical phenomena are continuous and digital representations are discrete for both space and time;
b. physical phenomena are heterogeneous in space, time, and space-time scales;
c. physical phenomena are semi-independent across localized geographic domains and can be divided
and conquered;
d. geospatial science and application problems include the spatiotemporal locations of the data storage,
computing/processing resources, the physical phenomena, and the users; all four locations interact to
complicate the spatial distributions of intensities;
e. spatiotemporal phenomena that are closer are more related (Tobler’ first law of geography)
(7) SCC framework
Figure 1. Framework of SCC: red colored components are fundamental computer system
components. (Yang et al. 2011)Virtual server virtualizes the fundamental components and support platform,
software, data, and application. IaaS, PaaS, SaaS and DaaS are defined depending on end
users’ involvements in the components. For example, end user of IaaS will have control on the
virtualized OS platform, software, data, and application as illustrated in yellow colour in the
right column. All blue colored components will require spatiotemporal principles to optimize
the arrangement and selection of relevant computing resources for best ensuring cloud benefits.
(Yang et al. 2011) applies case study to validate their theory that cloud computing could potentially
solve the four intensity problems geospatial sciences are facing. The authors assume that by
demonstrating how cloud computing could resolve each aspects of the IT challenges it would naturally
follow that cloud computing could be a solution to the problems geospatial sciences are confronted with.
Provided that the listed intensity problems were exhaustive, this methodology of case study would
prove, with facts and examples, the effect cloud computing could potentially bring to the field of
geospatial sciences. However, it could be argued that one scenario does not suffice to justify the point.
Cases might exist where one scenario listed by the paper could be improved by the utilization of cloud
computing while whether the underlying issue would be resolved by it remains to be seen.
(Yang et al. 2011) assumes that the methods and principles of geospatial sciences that can drive and
shape the computing technology would remain unchanged. Such an assumption can be unreliable as
both the development in technology and geospatial sciences itself might cause changes to occur.
Potential changes of methods and principles would lead to changes of requirements in terms of
supporting computing technologies. Therefore, the whole paper could be rendered unjustifiable.
Should the paper be rewritten today, all the conceptual analyses, provided that they were up-to-date,
would be preserved. If valid, these concepts, especially the correlation between geospatial sciences and
cloud computing, are still the basis for any proposal to resolve the problem. New achievements, if any,
would be included, and examples would be updated as needed. Theories might already have been
developed and thus would be introduced together with scenario analyses to better support the idea that
cloud computing could resolve the challenges faced by geospatial sciences.
References
[1] Chaowei Yang , Michael Goodchild , Qunying Huang , Doug Nebert , Robert
Raskin , Yan Xu , Myra Bambacus & Daniel Fay (2011) Spatial cloud computing: how can the
geospatial sciences use and help shape cloud computing?, International Journal of Digital Earth,
4:4, 305-329, doi: 10.1080/17538947.2011.587547
[2] Buyya, R., Pandey, S., and Vecchiola, S., 2009. Cloudbus toolkit for market-oriented cloud
computing. Cloud Computing, Lecture Notes in Computer Science, 5931 (2009), 24_44. doi:
10.1007/978-3-642-10665-1_4.
[3] Olson, A.J., 2010. Data as a service: Are we in the clouds? Journal of Map & Geography
Libraries, 6 (1), 76_78.
[4] Mell, P. and Grance, T., 2009. The NIST definition of cloud computing Ver. 15. [online].
NIST.gov. Available from: http://csrc.nist.gov/groups/SNS/cloud-computing/ [Accessed 22
November 2010].
[5] Yang, C., et al., 2011a. WebGIS performance issues and solutions. In: S. Li, S. Dragicevic, and
B. Veenendaal, eds. Advances in web-based GIS, mapping services and applications. London:
Taylor & Francis Group, ISBN 978-0-415-80483-7.
[6] Yang C., et al., 2011b. Using spatial principles to optimize distributed computing for enabling
physical science discoveries. Proceedings of National Academy of Sciences, 106 (14), 5498_
5503. doi: 10.1073/pnas.0909315108.