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Call for Papers

 

IEEE Cloud Computing for Emerging Markets (CCEM) 2014
15-17 October 2014 in Bangalore, india
Call for Papers, Demos, and Tutorials

The third edition of the IEEE International Conference on Cloud Computing for Emerging Markets (CCEM) continues the highly successful CCEM conference series which was originally launched in 2012.

Cloud computing has emerged as a dominant and transformational paradigm in information technology over the last few years and is beginning to effect a multitude of industries such as government, finance, telecommunications, education, retail, energy and utilities, and transportation. Research in this field has become very active and spans a number of areas including virtualization, networking, storage, security, management of cloud services, efficient cloud architectures, massive multi-tenancy, and design of cloud applications and services. Cloud computing continues to be the way of the future and many studies indicate that more than 50% of all information technology will be in this new paradigm within the next five to ten years. This transformation has great implications for emerging markets which have the potential to leap frog mature markets in their adoption of cloud computing, combine cloud computing and mobile technologies to introduce unique services that can transform the lives of billions, drive a much larger scale of adoption and challenge existing price points, while presenting unique challenges in areas such as security and user interfaces to cloud computing.

Program Highlights

  • Keynotes and invited Speakers
  • Paper Presentations
  • Start-up Showcase- Demos with multiple VCs
  • Masters and PhD Thesis paper presentations
  • Tutorials on first day

Topics of interest
We invite both paper and demo submissions on relevant topics, including but not limited to:

  • Design of Cloud Computing services, especially for emerging markets - infrastructure, platform, database, software, network, storage, and business process
  • Big data management and analytics
  • Advances in virtualization of hardware and software services
  • Security, privacy, and compliance management
  • Networking issues
  • Programming models
  • Monitoring, management, and maintenance in Cloud Computing environments
  • Performance optimization, service level agreements
  • Innovative applications and experiences in Cloud Computing for Emerging Markets

Tutorials
We also invite proposals for half-day or full day tutorials on topics of interest to the Cloud Computing community.

Important Dates
Full Paper Submissions, Demo, Tutorial, Startup Showcase, and Thesis Proposals Due: 21 July 2014

View details at the IEEE CCEM 2014 website

 


 

IEEE Transactions on Cloud Computing
Special Issue on Scientific Cloud Computing

Computational and Data-Driven Sciences have become the third and fourth pillar of scientific discovery in addition to experimental and theoretical sciences. Scientific Computing has already begun to change how science is done, enabling scientific breakthroughs through new kinds of experiments that would have been impossible only a decade ago. It is the key to solving "grand challenges" in many domains and providing breakthroughs in new knowledge, and it comes in many shapes and forms: high-performance computing (HPC) which is heavily focused on compute-intensive applications; high-throughput computing(HTC) which focuses on using many computing resources over long periods of time to accomplish its computational tasks; many-task computing (MTC) which aims to bridge the gap between HPC and HTC by focusing on using many resources over short periods of time; and data-intensive computing which is heavily focused on data distribution, data-parallel execution, and harnessing data locality by scheduling of computations close to the data. Today's "Big Data" trend is generating datasets that are increasing exponentially in both complexity and volume, making their analysis, archival, and sharing one of the grand challenges of the 21st century. Not surprisingly, it becomes increasingly difficult to design and operate large scale systems capable of addressing these grand challenges.

This journal Special Issue on Scientific Cloud Computing in the IEEE Transaction on Cloud Computing will provide the scientific community a dedicated forum for discussing new research, development, and deployment efforts in running these kinds of scientific computing workloads on Cloud Computing infrastructures. This special issue will focus on the use of cloud-based technologies to meet new compute-intensive and data-intensive scientific challenges that are not well served by the current supercomputers, grids and HPC clusters. The special issue will aim to address questions such as: What architectural changes to the current cloud frameworks (hardware, operating systems, networking and/or programming models) are needed to support science? Dynamic information derived from remote instruments and coupled simulation, and sensor ensembles that stream data for real-time analysis are important emerging techniques in scientific and cyber-physical engineering systems. How can cloud technologies enable and adapt to these new scientific approaches dealing with dynamism? How are scientists using clouds? Are there scientific HPC/HTC/MTC workloads that are suitable candidates to take advantage of emerging cloud computing resources with high efficiency? Commercial public clouds provide easy access to cloud infrastructure for scientists. What are the gaps in commercial cloud offerings and how can they be adapted for running existing and novel eScience applications? What benefits exist by adopting the cloud model, over clusters, grids, or supercomputers? What factors are limiting clouds use or would make them more usable/efficient?

Topics

  • Scientific application cases studies on Clouds
  • Performance evaluation of Cloud technologies
  • Fault tolerance and reliability in cloud systems
  • Data-intensive workloads and tools on Clouds
  • Programming models such as Map-Reduce
  • Storage cloud architectures
  • I/O and Data management in the Cloud
  • Workflow and resource management in the Cloud
  • NoSQL databases for scientific applications
  • Data streaming and dynamic applications on Clouds
  • Dynamic resource provisioning
  • Many-Task Computing in the Cloud
  • Application of cloud concepts in HPC environments
  • Virtualized High performance parallel file systems
  • Virtualized high performance I/O networks
  • Virtualization and its Impact on Applications
  • Distributed Operating Systems
  • Many-core computing and accelerators in the Cloud
  • Cloud security

 

Important Dates
Paper submission: 31 July 2014
First Round Decisions: 30 September 2014
Major Revisions Due (if needed): 31 October 2014
Final Decisions: 1 December 2014

View details (PDF, 516 KB)

 


 

Computer
Big Data: Management and Applications

Computer seeks submissions for a March 2015 special issue on big data management and applications.

With the synergistic confluence of multicore and multiprocessor computers, pervasive sensing and wireless sensor networks, cloud and mobile computing, along with groundbreaking advances in storage devices, we are generating unprecedented levels of data. It is estimated that 90 percent of the world's data has been generated in the past two years. Data too big and complex to capture, store, process, analyze, and interpret — even using state-of-the-art tools and methods — is referred to as big data.

The guest editors solicit papers covering all areas of big data management and applications, including big data infrastructure, frameworks, and tools; distributed big data interoperability and standards; big data management practices and analytics; big data privacy and security; and big data applications. Contributions that provide an interdisciplinary view of big data, and work that involves real deployments and novel applications are of particular interest.

Topics
Suggested topics include, but are not limited to, the following:

  • big data infrastructure, frameworks, and tools, such as high-performance computing architectures, distributed file systems, new programming paradigms and application frameworks, parallel class libraries, and green computing for big data;
  • distributed big data interoperability and standards, such as distributed file systems, semantic interoperability, semantic classifiers, shared vocabularies, ontologies, and data integration;
  • big data management practices and analytics, such as data models, query languages, stream data management, NoSQL systems, and indexing and query processing;
  • big data privacy and security, such as secure computations in distributed programming frameworks, security best practices for NoSQL systems, real-time security monitoring, granular access control, cryptographically enforced data-centric security, granular audits, and data provenance; and
  • big data applications, such as predictive manufacturing, physical infrastructure monitoring, sensor networks and smart building operation, customer-targeted marketing, analysis and optimization of business processes, law enforcement and security, research and learning, crowdsourcing, social network analysis, healthcare and public health management, image search, natural language understanding, and visualization.

 

Important Dates
One-page White Paper Due: 1 June 2014
Full Paper Submissions Due: 1 September 2014
Acceptance Notification: 15 November 2014
Final Papers Due: 15 December 2014
Special Issue Publication: March 2015

View details at Computing Now

 


 

IEEE Transactions on Cloud Computing
Special Issue on Big Data Computing on Clouds

Big data is an emerging paradigm applied to datasets whose size or complexity is beyond the ability of commonly used computer software and hardware tools. 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). Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, and MapReduce. As estimated by IDC, by 2020, about 40% data globally would be touched with Cloud Computing. Besides, Cloud Computing provides strong storage, computation and distributed capability in support of Big Data processing. Therefore, there is a strong demand to investigate various challenges about how to support Big Data processing by facilitating Cloud Computing potential. This special issue will focus on this challenging topic.

Topics
Original and unpublished high-quality research results are solicited to explore various challenging topics which include, but are not limited to:

  • Cloud Architecture for Big Data
  • Resource scheduling and SLA for Big Data on Cloud
  • Storage and computation management in Cloud for Big Data
  • Large-scale data intensive workflow in support of Big Data processing on Cloud
  • Multiple source data processing and integration on Cloud
  • Virtualisation and visualisation of Big Data on Cloud
  • Fault tolerance and reliability for Big Data processing on Cloud
  • MapReduce with Cloud for Big Data processing
  • Distributed file storage system with Cloud for Big Data
  • Inter-cloud technology for Big Data
  • Security, privacy and trust in Big Data processing on Cloud
  • Green, energy-efficient models and sustainability issues in Cloud for Big Data processing
  • Cloud infrastructure for social networking with Big Data
  • User friendly Cloud access for Big Data processing
  • Innovative Cloud data centre networking for Big Data
  • Wireless and mobility support in Cloud data centre for Big Data

 

Important Dates
Submission due date: 15 November 2014
Notification of acceptance: 15 March 2015
Submission of final manuscript: 15 May 2015
Publication date: 2nd Quarter, 2015 (Tentative)

View details (PDF, 119 KB)