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DCPerf'20 2020 : DCPerf'20 Call For Papers

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Link: https://infocom2020.ieee-infocom.org/workshop-big-data-and-cloud-performance
 
When Apr 27, 2020 - Apr 30, 2020
Where Beijing, China
Abstract Registration Due Jan 8, 2020
Submission Deadline Jan 15, 2020
Notification Due Feb 15, 2020
Final Version Due Mar 6, 2020
Categories    performance   big data   cloud   datacenter
 

Call For Papers

The 10th Workshop on Big Data and Cloud Performance (DCPerf'20) will be held in conjunction with the IEEE INFOCOM'20 in Beijing, China on April 27, 2020. The goal of DCPerf is to promote a community-wide discussion to identify suitable strategies to enable effective and scalable performance optimizations. Submissions on any topics of datacenter, cloud and bigdata performance are welcome. Submission site will remain open until Jan. 8th, 2020.

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)

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
- Cost/pricing design
- Power/energy management
- Reliability/dependability
- Performance evaluation/modeling
Big data in the cloud
- Intra/Inter communication
- Network protocols
- Security
- Real-time analytics


Important Dates
Paper Registration and Abstract Submission: Jan. 8th, 2020
Full Paper Submission: Jan. 15th, 2020
Notification of Acceptance: Feb. 15th, 2020
Final Manuscript Due: March 6, 2020


COMMITTEE

General Chair
Wanyang Dai, Department of Mathematics, Nanjing University, China

TPC co-Chairs
Peter Mueller, IBM Research Zurich Lab, Switzerland
Rui Han, School of Computer Science&Technology, Beijing Insititute of Technology

Steering Committee
Jiannong Cao (Hong Kong Polytechnic University, Hong Kong)
Alok Choudhary (Northwerstern University, USA)
Lydia Y. Chen, Distributed System Department, Delft University of Technology, The Netherlands
Martin Schmatz (IBM Research Zurich Lab, Switzerland)
Anand Sivasubramaniam (Penn State University, USA)
Larry Xue (Arizona State University, USA)


Submission Guideline

Manuscripts must be limited to 6 pages in IEEE 8.5x11-inch format. Accepted papers will be published in the combined INFOCOM 2020 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=26866&track=99584

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