[hpc-announce] CFP - The 10th Workshop on Big Data and Cloud Performance (DCPerf'20) at IEEE Infocom'20

[Peter Mueller]

                     Call for Papers
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The 10th International Workshop on Big Data and Cloud Performance 
                       (DCPerf’20)
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           in conjunction with IEEE INFOCOM 2020
              April 27, 2020, Beijing, China

https://infocom2020.ieee-infocom.org/workshop-big-data-and-cloud-performance

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

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

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)

Contact:
  Peter Mueller - pmu at zurich.ibm.com