pullfarmInternet and Web Development

Nov 3, 2013 (4 years and 8 months ago)


Paper: (Yang
et al
. 2011)

et al
. 2011) claims that “t
he geospatial sciences face great information technology (IT) challenges
in the twenty
first century: data intensity, computing intensity, concurrent access intensity, and

It goes on to say that t
hese 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
applications; (3) deal with access spikes; (4) ensure reliability and scalability for massive number of
t 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 dat

et al
. 2011) succeeds at explaining the correlation between cloud computing and geospatial
sciences. Namely, it points out that geospatial sciences act b
oth as a driver and an enabler in the
development of new computing technologies.
The pa
per 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
oncludes 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 c
hallenges 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.

et al
. 2011) presents the following key concepts:

(1) IT challenges faced by geospatial sciences:

a. Data intensity

the volume, scalability, and diversi
ty 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 ren
ders 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 practi
cal 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 comp
uting 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,

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 ca
develop or run existing applications on such a platform. Examples are Microsoft Azure
) and Google App Engine.

c. Software as a Service (SaaS)

SaaS provides appli
cations for end users. These applications used to be provided through web browsers.
Examples are and Google’s gmail and 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

b. broad network access;

c. resources pooling;

d. rapid elasticity;

e. measured service

) 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 representa
tions 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 scienc
e 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. spatiotempora
l phenomena that are closer are more related (Tobler’ first law of geography)

) SCC framework

Figure 1. Framework of SCC: red colored components are fundamental computer system

components. (Yang et al. 2011)Virtual server virtualizes the fundamental c
omponents 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, a
nd 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.

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 ch
allenges 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
prove, with facts and ex
amples, 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.

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
ing 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
would be preserved. If valid, these concepts, especially the correlation between geospatial sciences and
cloud computing, are still the basis for any prop
osal 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

computing could resolve the challenges faced by geospatial sciences.


Chaowei Yang , Michael Goodchild , Qunying Huang , Doug Nebert , Robert

Raskin , Yan Xu , Myra Bambacus & Daniel Fay (2011) Spatial cloud computing: how can the

al sciences use and help shape cloud computing?, International Journal of Digital Earth,

4:4, 305

[2] Buyya, R., Pandey, S., and Vecchiola, S., 2009. Cloudbus toolkit for market
oriented cloud

computing. Cloud Computi
ng, Lecture Notes in Computer Science, 5931 (2009), 24_44. doi:


Olson, A.J., 2010. Data as a service: Are we in the clouds? Journal of Map & Geography

Libraries, 6 (1), 76_78.

Mell, P. and Grance, T., 2009. The NIST
definition of cloud computing Ver. 15. [online]. Available from:
computing/ [Accessed 22

November 2010].

[5] Yang, C., et al., 2011a. WebGIS performance issues and solutions. In: S. Li, S. Dragicevic, and

Veenendaal, eds. Advances in web
based GIS, mapping services and applications. London:

Taylor & Francis Group, ISBN 978

[6] Yang C., et al., 2011b. Using spatial principles to optimize distributed computing for enabling

physical science disc
overies. Proceedings of National Academy of Sciences, 106 (14), 5498_

5503. doi: 10.1073/pnas.0909315108.