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[HTCondor-users] CFP on Big Data and Cloud Performance (DCPerf'17) (due on Jan 10) (INFOCOM'17 Workshop)


     Call for Papers - Submission Due Date: January 10, 2017
ÂThe 7th International Workshop on Big Data and Cloud Performance (DCPerfâ17)
            Atlanta, USA, May 1, 2017
          part of IEEE INFOCOM'17 Workshop:

 ÂThe 36th IEEE International Conference on Computer CommunicationsÂ

Cloud data centers are the backbone infrastructure for tomorrow's information technology. Their advantages are efficient resource provisioning and low operational costs for supporting a wide range of computing needs, be it in business, scientific or mobile/pervasive environments. Because of the rapid growth in user-defined and user-generated applications and content, the range of services provided at data centers will expand tremendously and unpredictably. Particularly, big data applications and services, e.g., social, environmental sensing and IoT monitoring, present a unique class of challenges in Cloud. The high volume of mixed workloads and the diversity of services offered render the performance optimization of data centers ever more challenging. Moreover, important optimization criteria, such as scalability, reliability, manageability, power efficiency, area density, and operating costs, are often conflicting to some extent and require trade-off. In addition, the increasing mobility of users across geographically distributed areas adds another dimension to optimizing big data and cloud performance.

The goal of this workshop is to promote a community-wide discussion to find and identify suitable strategies to enable effective and scalable performance optimizations. We are looking for papers that present new techniques, introduce new theory and methodologies, propose new research directions, or discuss strategies for resolving open performance problems on big data in clouds.

Topics of Interest

Topics of interest include (but are not limited to):
- Big Data applications and services
  Emerging IoT applications
  Data flow management
  Processing platforms
  Empirical studies

- Cloud systems
  Novel architectures
  Resource allocation
  Content distribution
  Evaluation/modeling methodology

- Big Data and Cloud performance Â
  Cost/pricing designÂ
  Energy management
  Performance evaluation/modeling

- Big Data in Cloud
  Intra/inter communication
  Network protocols
  Real time analytics

Important Dates

Paper submission: Â Â Â Â Â Â Â ÂJanuary 10, 2017
Notification of acceptance: Â Â February 22, 2017
Final manuscript due: Â Â Â Â Â Â ÂMarch 12, 2017

Submission Guideline

Manuscripts must be limited to 6 pages in IEEE 8.5x11 format. Accepted papers will be published in the combined INFOCOM 2017 Workshop proceedings and will be submitted to IEEE Xplore. Submitted papers may not have been previously published in or under consideration for publication in another journal or conference. The reviews will be single blind. Manuscripts should be submitted as PDF files via EDAS: https://edas.info/newPaper.php?c=23059&track=82666

TPC Chairs

Robert Birke, IBM Zurich Research Lab, Switzerland
George Kesidis, Pennsylvania State University, USA

Program Committee

Marco Beccuti, University of Torino, Italy
Gergely BiczÃk, Budapest University of Technology and Economics, Hungary
Roberto Bruschi, CNIT, Italy
Luca Chiaraviglio, CNIT, Italy
Waltenegus Dargie, Technical University of Dresden, Germany
Aayush Gupta, IBM Almaden Research Center, CA, USA
Yennun Huang, Academia Sinica, Taiwan
Esa HyytiÃ, University of Iceland, Iceland
Samee Khan, North Dakota State University, ND, USA
Samuel Kounev, University of WÃrzburg, Germany
Diwakar Krishnamurthy, University of Calgary, Canada
Peter Kropf, University of NeuchÃtel, Switzerland
Ningfang Mi, Northeastern University, MA, USA
Masayuki Murata, Osaka University, Japan
Thu Nguyen, Rutgers University, NJ, USA
Juan F. PÃrez, University of Melbourne, Australia
Shu Tao, IBM T. J. Watson Research Center, NY, USA
Bhuvan Urgaonkar, Pennsylvania State University, PA, USA
Florian Wamser, University of WÃrzburg, Germany
Kui Wu, University of Victoria, Canada
Miki Yamamoto, Kansai University, Japan

Steering Committee

Jian-Nong Cao, Hong Kong Polytechnic University, Hong Kong
Alok Choudhary, Northwerstern University, USA
Peter Muller, IBM Research Zurich Lab, Switzerland
Martin Schmatz, IBM Research Zurich Lab, Switzerland
Anand Sivasubramaniam, Penn State University, USA
Larry Xue, Arizona State University, USA