MBDS: Management of Big Data Systems

Important Dates

• Paper submissions due: April 2, 2013, 11:59 p.m. PDT
• Notification to authors: April 15, 2013
• Final paper files due: May 22, 2013

Overview

Data is growing at an exponential rate and several systems have emerged to store and analyze such large amounts of data. These systems, termed “Big Data systems” are fast-evolving. Examples include the NoSQL storage systems, Hadoop Map-Reduce, data analytics platforms, search and indexing platforms, and messaging infrastructures. These systems address needs for structured and unstructured data across a wide spectrum of domains such as Web, social networks, enterprise, cloud, mobile, sensor networks, multimedia/streaming, and cyberphysical and high performance systems; and for multiple application verticals such as biosciences, healthcare, transportation, public sector, energy utilities, oil and gas, and scientific computing.

With increasing scale and complexity, managing these Big Data systems to cope with failures and performance problems is becoming non-trivial. New resource management and scheduling mechanisms are also needed for such systems, as are mechanisms for tuning and support from platform layers. Several open source and proprietary solutions have been proposed to address these requirements, with extensive contributions from industry and academia. However, there remain substantial challenges, including those that pertain to such systems' autonomic and self-management capabilities.

The objective of the MBDS track at ICAC '13 is to bring together researchers, practitioners, system administrators, system programmers, and others interested in sharing and presenting their perspectives on the effective management of Big Data systems. The focus of the track is on novel and practical systems-oriented work. MBDS offers an opportunity for researchers and practitioners from industry, academia, and national labs to showcase the latest advances in this area and also to discuss and identify future directions and challenges in all aspects on autonomic management of Big Data systems.

Two types of contributions are solicited on all aspects of Big Data management: (1) short papers and (2) panel presentations. Short papers should be no more than 6 pages, including the abstract, and will appear in the ICAC '13 conference proceedings. Proposed panel presentations require only an abstract. Topics of interest include but are not limited to the following:

• Autonomic and self-managing techniques
• Application-level resource management and scheduling mechanisms
• System tuning/auto-tuning and configuration management
• Performance management, fault management, and power management
• Scalability challenges
• Complexity challenges, as for composite, cross-tier systems with multiple control loops
• Unified management of "data in motion" and "data at rest"
• Dealing with both structured and unstructured data
• Monitoring, diagnosis, and automated behavior detection
• System-level principles and support for resource management
• Holistic management across hardware and software
• Implications of emerging hardware technologies such as non-volatile memory
• Domain specific challenges in Web, cloud, social networks, mobile, sensor networks, streaming analytics, and cyber-physical systems
• System building and experience papers for specific industry verticals

Submissions

Submissions to the MBDS track follow the same guidelines as described in the main ICAC '13 Call for Papers; in addition, submissions should be a maximum of 6 pages in length. In order to submit your work to the MBDS track, please do so via the Web submission form for this special track, as opposed to the submission form for the general ICAC '13 track. Questions? Contact the program vice-chairs of the track.

Past Event

This track is a continuation of the previous Workshop on Management of Big Data Systems, which was held on September 21, 2012, in San Jose, CA, in conjunction with ICAC 2012.

Management of Big Data Systems Organizers

Program Vice-Chairs

Karsten Schwan, Georgia Institute of Technology
Vanish Talwar, HP Labs

Program Committee

Hasan Abbasi, Oak Ridge National Laboratory
Adhyas Avasthi, Cisco Systems
Milind Bhandarkar, Greenplum, a division of EMC
Surendar Chandra, EMC
Richard Cole, Amazon
Matt Jacobs, Cloudera
Michael Kozuch, Intel
Mohamed Mansour, Amazon
Aravind Menon, Facebook
Arif Merchant, Google
Indrajit Roy, HP Labs
Weidong Shi, University of Houston
Kapil Surlaker, LinkedIn 

Publicity Chair

Dani Abel Rayan, CrowdSpots.com