[hpc-announce] IEEE Transactions on Cloud Computing - Special Issue on Big Data

Rajiv Ranjan rranjans at gmail.com
Sun Jun 9 20:45:44 CDT 2013

Special Issue on

*Autonomic Provisioning of Big Data Applications on Clouds*

IEEE Transactions on Cloud Computing

Guest Editors: Rajiv Ranjan, Lizhe Wang, Albert Zomaya, Dimitrios
Georgakopoulos, Guojun Wang, and Xian-He Sun

Editor-in-Chief: Rajkumar Buyya

**** Call for Papers ****

This special issue solicits papers that advance the fundamental
understanding, technologies, and concepts related to autonomic provisioning
of cloud resources for Big Data applications. The research advancement is
in this area is important because such large, heterogeneous, and uncertain
Big Data applications are becoming increasingly common, yet current cloud
resource provisioning methods do not scale well and nor do they perform
well under highly unpredictable conditions (data volume, data variety, data
arrival rate, etc.). If these problems are resolved, then cloud-hosted Big
Data applications will operate more efficiently, with reduced financial and
environmental costs, reduced under-utilisation of resources, and better
performance at times of unpredictable workload.

Cloud computing assembles large networks of virtualised ICT services such
as hardware resources (such as CPU, storage, and network), software
resources (such as databases, application servers, and web servers) and
applications. In industry these services are referred to as Infrastructure
as a Service (IaaS), Platform as a Service (PaaS), and Software as a
Service (SaaS). Mainstream ICT powerhouses such as Amazon, HP, and IBM  are
heavily investing in the provision and support of public cloud
infrastructure. Cloud computing is rapidly becoming a popular
infrastructure of choice among all types of organisations. Despite some
initial security concerns and technical issues, an increasing number of
organisations have moved their applications and services in to “The Cloud”.
These applications range from generic word processing software to online
healthcare. The Cloud system taps into the processing power of virtualized
computers on the back end, thus significantly speeding up the application
for the user, which just pays for the used services.

Big Data applications has become a common phenomenon in domain of science,
engineering, and commerce Some of the representative applications include
disaster management, high energy physics, genomics, connectomics,
automobile simulations, medical imaging, and the like. The ‘BigData’
problem, which is defined as the practice of collecting complex data sets
so large that it becomes difficult to analyse and interpret manually or
using on-hand data management applications (e.g., Microsoft Excel).   For
example, in case of disaster management Big Data application there is a
need to analyse “*a deluge of online data from multiple sources (feeds from
social media and mobile devices)*” for understanding and managing real-life
events such as flooding, earthquake, etc. Over 20 million tweets posted
during Hurricane Sandy (2012) lead to an instance of the BigData problem.
The statistics provided by the PearAnalytics study reveal that almost 44%
of the Twitter posts are spam and pointless, about 6% are personal or
product advertising, while 3.6% are news and 37.6% are conversational
posts. During the 2010 Haiti earthquake, text messaging via mobile phones
and Twitter made headlines as being crucial for disaster response, but only
some 100,000 messages were actually processed by government agencies due to
lack of automated and scalable ICT (cloud) infrastructure.

Although significant effort has been devoted to migrating generic web-based
application to the Cloud, scant research and development has been done to
create a unified software framework for provisioning Big Data applications
on clouds. *Provisioning means the selection, deployment, monitoring, and
run-time management of PaaS and IaaS resources for ensuring that
applications meet their Quality of Service (QoS) targets *(for data
analysis delay, data availability, alert generation delay, etc.)* as agreed
in the negotiated Service Level Agreement (SLA). *IaaS clouds such as
Amazon EC2 and GoGrid are too low-level, making development of Big Data
applications difficult and resource provisioning unintelligent and
inefficient. PaaS clouds such as Microsoft Azure are at an appropriate
level, but it does not provide the right kind of abstraction required for
supporting real-time analysis of massive dataset from multiple sources. Big
Data applications are uncertain, as it has to deal with data which can be
from multiple contexts and originated from heterogeneous sources.  Furthermore,
it is a difficult problem to estimate the behavior of Big Data applications
in terms of data volume, data arrival rate, data types, and data processing
time distributions. Secondly, from a cloud resource perspective, without
knowing the requirements or behaviours of BigData applications, it is
difficult to make decisions about the size of resources to be provisioned
at any given time. Furthermore, the availability, load, and throughput of
cloud resources can vary in unpredictable ways, due to failure, malicious
attacks, or congestion of network links.

The special issue will also encourage submission of revised and extended
versions of 2-3 best papers (based on votes of a panel) in the area of
Cloud Computing from IEEE HPCC 2013 (
http://trust.csu.edu.cn/conference/hpcc2013/Call%20for%20Papers.htm), IEEE
CCGRID 2014 (http://datasys.cs.iit.edu/events/CCGrid2014/), and IEEE IC2E
2014 conferences.

* *


  Areas of interest for this special issue include the following:

-      Algorithms for petabyte efficient non-SQL query-based Big Data
processing and related Cloud resource optimisation.

-      Big Data Application behavior prediction models

-      Real-time analytics on streaming Big Data

-      Collaborative sharing and management

-      Big Data application performance evaluation study on public and
private clouds

-      Dynamic learning technique for new Big application behavior

-      Queuing theory based cloud resource performance model solvers

-      Stochastic fault-tolerance and reliability models

-      Decentralized networking models for scalable Big Data application
health monitoring

-      Energy-efficiency models for provisioning of Big Data application

-      Innovative Big Data Application use cases (disaster management, high
energy physics, genomics, connectomics, automobile simulations, medical
imaging, and the like)

-      Security, privacy and trust-based  Big Data  application provisioning

* *


Submission due date: March 1, 2014

Notification of acceptance: June 15, 2014

Submission of final manuscript: August 15, 2014

Publication date: 2nd Quarter, 2014(Tentative)

*Submission & Major Guidelines*

The special issue invites original research papers that make significant
contributions to the state-of-the-art in “Autonomic Provisioning of Big
Data Applications on Clouds”.  The papers must not have been previously
published or submitted for journal or conference publications. However, the
papers that have been previously published with reputed conferences could
be considered for publication in the special issue if they are
substantially revised from their earlier versions with at least 30% new
contents or results that comply with the copyright regulations, if any.  Every
submitted paper will receive at least three reviews. The editorial review
committee will include well known experts in the area of Grid, Cloud, and
Autonomic computing.

Selection and Evaluation Criteria:

-      Significance to the readership of the journal

-      Relevance to the special issue

-      Originality of idea, technical contribution, and significance of the
presented results

-      Quality, clarity, and readability of the written text

-      Quality of references and related work

-      Quality of research hypothesis, assertions, and conclusion

*Guest Editors*

*Dr. Rajiv Ranjan – Corresponding Guest Editor*

Research Scientist, CSIRO ICT Center

Computer Science and Information Technology Building (108)

North Road, Australian National University, Acton, ACT, Australia

Email: raj.ranjan at csiro.au

* *

*Prof. Lizhe Wang*

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences

No.9 Dengzhuang South Road, Hadian District

Beijing 100094, P.R. China

Email:lzwang at ceode.ac.cn

* *

*Prof. Albert Zomaya*

Australian Research Council Professorial Fellow

Chair Professor of High Performance Computing & Networking

School of Information Technologies, Building J12

The University of Sydney

Sydney, NSW 2006, Australia

Email: albert.zomaya at sydney.edu.au

*Prof. Dimitrios Georgakopoulos*

Research Director, Information Engineering, Laboratory, CSIRO ICT Center

Computer Science and Information Technology Building (108)

North Road, Australian National University, Acton, ACT, Australia

Email: dimitrios.georgakopoulos at csiro.au

*Dr. Guojun Wang*

Chairman and Professor of Department of Computer Science,

Central South University, Changsha, Hunan Province,

P. R. China, 410083

Tel/Fax: +86 731 88877711, Mobile: +86 13508486821

Email: csgjwang at mail.csu.edu.cn; csgjwang at gmail.com

*Prof. Xian-He Sun*

Director, The SCS Laboratory, Department of Computer Science

Illinois Institute of Technology

Email: sun at iit.edu
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