[hpc-announce] CFP: IEEE BDCloud2018 (Big Data and Cloud Computing), Dec. 2018, Melbourne, Australia

[Arne Wilston]

Call for papers:

The 8th IEEE International Conference on Big Data and Cloud Computing (
BDCloud2018), 11-13 Dec. 2018, Melbourne, Australia.

Website: http://www.swinflow.org/confs/2018/bdcloud/

Key dates:
Submission Deadline: August 31, 2018 (11:59pm UTC/GMT, firm)
Notification: September 30, 2018
Final Manuscript Due: October 15, 2018

Submission site: http://www.swinflow.org/confs/2018/bdcloud/submission.htm

Publication:
Proceedings will be published by IEEE CS Press.

Special issues:
Distinguished papers will be selected for special issues in Information
Sciences, Future Generation Computer Systems, Journal of Parallel and
Distributed Computing, Concurrency and Computation: Practice and Experience

===========
Introduction

Big data is an emerging paradigm applied to datasets whose size is beyond
the ability of commonly used software tools to capture, manage, and process
the data within a tolerable elapsed time. Such datasets are often from
various sources (Variety) yet unstructured such as social media, sensors,
scientific applications, surveillance, video and image archives, Internet
texts and documents, Internet search indexing, medical records, business
transactions and web logs; and are of large size (Volume) with fast data
in/out (Velocity). More importantly, big data has to be of high value
(Value) and establish trust in it for business decision making (Veracity).

Cloud computing is positioning itself as an emerging platform for
delivering information infrastructures and resources as IT services.
Customers (enterprises or individuals) can provision and deploy Cloud
services via pay-as-you-go pricing models saving huge capital investments
in their own IT infrastructures.

As estimated by IDC, by 2020, about 40% data globally would be touched with
Cloud Computing. Cloud Computing provides strong storage, computation and
distributed capability in support of Big Data processing.

BDCloud (Big Data and Cloud Computing) was created to provide a prime
international forum for researchers, industry practitioners and domain
experts to exchange the latest advances in Big Data and Cloud Computing as
well as their synergy.

Scope and Topics

Topics of particular interest include, but are not limited to:
·   Fundamentals of cloud computing
·   Architectural cloud models
·   Programming cloud models
·   Provisioning/pricing cloud models
·   Data storage and computation in cloud computing
·   Resource and large-scale job scheduling in cloud computing
·   Security, privacy, trust, risk in cloud and big data
·   Fault tolerance and reliability in cloud computing
·   Access control to cloud computing
·   Resource virtualisation
·   Monitoring and auditing in cloud
·   Scalable and elastic cloud services
·   Social computing and impacts on the cloud
·   Innovative HCI and touch-screen models and technologies to cloud
·   Mobile commerce, handheld commerce and e-markets on cloud
·   Intelligent/agent-based cloud computing
·   Migration of business applications to cloud
·   Energy efficient cloud architecture
·   Energy aware data storage and computation in cloud computing
·   Energy aware scheduling, monitoring, auditing in cloud
·   Green Cloud
·   Cloud use case studies
·   Big Data theory, applications and challenges
·   Big Data mining and analytics on Cloud
·   Big Data Infrastructure, MapReduce and Cloud Computing
·   Big Data visualization
·   Large data stream, incremental datasets on cloud
·   Distributed and federated datasets
·   NoSQL data stores and DB scalability
·   Big Data sharing, security, privacy and trust
·   Big Data placement, scheduling, and optimization
·   Distributed file systems for Big Data
·   Big Data processing, resource scheduling and SLA on Cloud
·   Performance characterization, evaluation and optimization
·   Simulation and debugging of Big Data systems
·   Volume, Velocity, Variety, Value and Veracity of Big Data
·   Storage and computation management of Big Data
·   Large-scale workflow management in Big Data
·   Data management and distributed data systems
·   Big data applications

Submission Guidelines
Submissions must include an abstract, keywords, the e-mail address of the
corresponding author and should not exceed 8 pages for main conference,
including tables and figures in IEEE CS format. The template files for
LATEX or WORD can be downloaded here. All paper submissions must represent
original and unpublished work. Each submission will be peer reviewed by at
least three program committee members. Submission of a paper should be
regarded as an undertaking that, should the paper be accepted, at least one
of the authors will register for the conference and present the work.
Submit your paper(s) in PDF file at the submission site:
http://www.swinflow.org/confs/2018/bdcloud/submission.htm.

Publications
Accepted and presented papers will be included into the IEEE
Conference Proceedings published by IEEE CS Press. Authors of accepted
papers, or at least one of them, are requested to register and present
their work at the conference, otherwise their papers may be removed from
the digital libraries of IEEE CS and EI after the conference.

Distinguished papers will be selected for special issues in Information
Sciences, Future Generation Computer Systems, Journal of Parallel and
Distributed Computing, Concurrency and Computation: Practice and Experience.

General Chairs
Xuemin Lin, The University of New South Wales, Australia
Rajkumar Buyya, The University of Melbourne, Australia
Laurence Yang, St. Francis Xavier University, Canada

Program Chairs
Zahir Tari, RMIT University, Australia
Kim-Kwang Raymond Choo, The University of Texas at San Antonio, USA
Vladimir Vlassov, KTH Royal Institute of Technology, Sweden

Program Co-Chairs
Lina Yao, University of New South Wales, Australia
Hongzhi Yin, The University of Queensland, Australia
Weiqing Wang, Monash University, Australia

Workshop Chairs
Muhammad Ali Babar, University of Adelaide, Australia
Jian Cao, Shanghai Jiaotong University, China
Rafael Tolosana, University of Zaragoza, Spain
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