Call for Papers ----------------------------------------------------------------- The 10th International Workshop on Big Data and Cloud Performance (DCPerf’20) ----------------------------------------------------------------- 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@zurich.ibm.com