In recent years, data storage has emerged as an important research field driven by the demand for scalable structures and technologies to satisfy the growing needs of massive data management and processing. Big Data challenges storage systems with more capacity, scalability and efficient accessibility. Dispersing a huge data object in a large-scale distributed storage system is necessary to enhance data reliability and availability. By introducing redundancy in the system, we can protect data integrity from node failures. As node failures occur frequently in large-scale storage systems, a considerable volume of network traffic is dedicated to the repair of failed storage nodes. Several classes of distributed storage codes, such as regenerating codes, locally repairable codes, have been introduced recently to reduce this overhead and disk input/output cost. However, there still remains substantial research work for advancing distributed storage coding and systems in both theory and applications.
This workshop will provide an excellent platform for researchers and practitioners from academia and industry to exchange ideas and experiences that distributed storage systems can offer to Big Data applications, and to understand the challenges that we need tackle to realize the full potential.
Topics of interest include but are not limited to:
The full manuscript should be at most 8 pages using the 2-column IEEE format. Additional pages will be charged additional fee.
Papers MUST be submitted in PDF format and only through the Online Submission System.
The authors of accepted papers must guarantee that their papers will be presented at the conference. At least one author of each accepted paper must register for the conference in order to include the paper in IEEE Xplore Digital Library.
Our workshop will be held in conjunction with the following workshop: 3rd Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH).
Date: 5-December, 2016
|08:30-15:30||3rd Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH)|
|15:50-16:15||Towards Optimizing Large-Scale Data Transfers with End-to-End Integrity Verification Si Liu (Illinois Institute of Technology, USA), Eun-Sung Jun (Hongik University, South Korea), Rajkumar Kettimuthu (Argonne National Laboratory, USA), Xian-He Sun (Illinois Institute of Technology, USA), and Michael Papka (Argonne National Laboratory, USA)|
|16:15-16:40||EStore: An Effective Optimized Data Placement Structure for Hive Xin Li, Hui Li, Zhihao Huang, Bing Zhu (Peking University Shenzhen Graduate School, China), and Jiawei Cai (Guangdong Super Computing & Data Technology Co., Ltd, China)|
|16:40-17:05||SS-Dedup: A High Throughput Stateful Data Routing Algorithm for Cluster Deduplication System Zhihao Huang, Hui Li, Xin Li (Peking University Shenzhen Graduate School, China), and Wei He (Guangdong Super Computing & Data Technology Co., Ltd, China)|
|17:05-17:30||CoLoc: Distributed Data and Container Colocation for Data-Intensive Applications
Thomas Renner, Lauritz Thamsen, and Odej Kao (Technische Universitaet Berlin, Germany)
|17:30-17:55||Persisting In-Memory Databases Using SCM
Ellis Giles (Rice University, USA), Kshitij Doshi (Intel Corporation, USA), and Peter Varman (Rice University, USA)
Venue: Regency B, Hyatt Regency Washington on Capitol Hill, Washington D.C., USA