DCperf: Data Center Performance

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Past:   Proceedings on DBLP

Future:  Post a CFP for 2020 or later   |   Invite the Organizers Email

 
 

All CFPs on WikiCFP

Event When Where Deadline
DCPerf 2019 The 9th International Workshop on Big Data and Cloud Performance
Apr 29, 2019 - Apr 29, 2019 Paris, France Jan 9, 2019
DCPerf 2016 International Workshop on Big Data and Cloud Performance
Jun 27, 2016 - Jun 27, 2016 Nara, Japan Feb 17, 2016
DCPerf 2015 Workshop on Data Center Performance
Jun 29, 2015 - Jul 2, 2015 Columbus, Ohio, USA Feb 15, 2015
DCPerf 2014 Workshop on Data Center Performance
Jun 30, 2014 - Jul 3, 2014 Madrid, Spain Feb 7, 2014
DCperf 2013 CFP: ICDCS'13 Workshop on Data Center Performance (DCPerf13) (due on Jan 21th)
Jul 8, 2013 - Jul 11, 2013 Philadelphia, USA Feb 4, 2013
DCPerf 2012 The Second International Workshop on Data Center Perforamnce
Jun 18, 2012 - Jun 21, 2012 Macau, China Jan 30, 2012
DCPerf 2011 The First international Workshop on Data Center Perforamnce
Jun 24, 2011 - Jun 24, 2011 Minneapolis, MN, USA Feb 10, 2011
 
 

Present CFP : 2019

The 9th International Workshop on Big Data and Cloud Performance (DCPerf’19)
Paris, France, April 29 – May 2, 2019

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 and environmental sensing, and IoT monitoring, present a unique class of challenges in the Cloud. In addition, the high volume of mixed workloads and the diversity of services offered render the performance optimization of data centers even more challenging. Moreover, important optimization criteria, such as scalability, reliability, manageability, power efficiency, area density, and operating costs, are often conflicting. The increasing mobility of users across geographically distributed areas adds another dimension to optimizing big data and cloud applications.

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

Topics of interest include (but are not limited to)
Ð Big data applications and services
o Emerging IoT applications
o Data flow management
o Processing platforms
o Empirical studies
Ð Cloud systems
o Novel architectures
o Resource allocation
o Content distribution
o Evaluation/modeling methodology
Ð Big data and cloud performance
o Cost/pricing design
o Power/energy management
o Reliability/dependability
o Performance evaluation/modeling
Ð Big data in the cloud
o Intra/Inter communication
o Network protocols
o Security
o Real-time analytics

Important Dates
Paper Registration and Submission: January 09, 2019
Notification of Acceptance: February 7, 2019
Final Manuscript Due: March 6, 2019
Submission Guideline
Manuscripts must be limited to 6 pages in IEEE 8.5x11-inch format. Accepted papers will be published in the combined INFOCOM 2019 Workshop proceedings and will be submitted to IEEE Xplore. Submitted papers may not have been previously published in or be under consideration for publication in another journal or conference. The reviews will be single blinded. Manuscripts should be submitted as PDF files via EDAS link: https://edas.info/newPaper.php?c=25565

Organizing CommitteeTPC co-Chairs
Jia Rao, The University of Texas at Arlington, USA
Christopher Stewart, The Ohio State University, USA

Steering Committee
Jiannong 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
 

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