BDCOM 2017: IEEE BigData Workshop on Cloud Operations Management

11-14 DECEMBER, 2017, BOSTON

BDCOM: BigData for Cloud Operations Management

Both Big Data analytics and Cloud Computing are growing rapidly. We are observing a widespread adoption of solutions utilizing and combining these frameworks. One key field that the power of Big Data Analytics can be immensely beneficial for Cloud Computing is operational analytics. Cloud Computing enables deployments at scale that can adapt to changing demands. Agile methods use these capabilities to build application and services that rapidly adapt to changing business conditions. With continuous integration and delivery, a cloud environment is very dynamic with changes at many levels. In such an environment, it is necessary to ensure the components and services are configured correctly and securely; the cloud is in a highly available, reliable and secure state; and the services in the cloud are functioning at their optimum levels. Massive amounts of data generated by an ever-increasing number of monitors for components in the IT stack need to be aggregated, analyzed, understood and responsive actions taken in real-time.

Even newer methods of ensuring availability, reliability and security through both manual and automated testing/configuration are being challenged with increase in scale and speed. Agility demands that developers iterate in a fast pace and identify, diagnose, and, fix problems quickly and correctly. There has been extensive research and development to derive insights from operational data, for example, intelligent resource and security data collection, anomaly and performance variation detection, root cause analysis, configuration analysis, efficient cloud resource utilization, security/vulnerability analysis, etc. This workshop is an effort to bring practitioners together for sharing and validating ideas and finding new approaches for deriving insights from operational data. We invite submission of papers related (but not limited) to the following areas:

Research Topics

  • Uses of Big Data analytics in cloud operations management
  • IT operation analytics
  • Data-driven cloud configuration analytics
  • Capturing, Filtering and Representing cloud operational data
  • Tools/ frameworks/ services for operational analytics in cloud
  • Learning/mining techniques for cloud operational analytics
  • Analytics feedback for continuous integration/deployment
  • Experiences/Challenges/Best Practices monitoring cloud deployments
  • Real time data collection and real time analytics in cloud operational management
  • Cost analysis of cloud operational monitoring and analytics

Important Dates

  • Oct 7, 2017 Extended to Oct 9th 2017: Due date for full workshop papers submission
  • Nov 1, 2017: Notification of paper acceptance to authors
  • Nov 15, 2017: Camera-ready of accepted papers
  • Dec 11-14, 2017: Workshops

Chairs

  • Ata Turk - Boston University
  • Prabhakar Kudva - IBM
  • Sastry S. Duri - IBM

Steering Committee

  • Ayse Coskun - Boston University
  • Canturk Isci - IBM
  • Larry Rudolph - Two Sigma

Program Committee Members

  • David Cohen - Intel
  • Homin Lee - Datadog
  • Dilma Da Silva - Texas A&M University
  • Thu D. Nguyen - Rutgers University
  • Sreenivas Rangan Sukumar - CRAY
  • Byungchul Tak - KNU
  • Raja Sambasivan - Boston University
  • Devesh Tiwari - Northeastern University
  • Peter Portante - RedHat
  • Daniel McPherson - RedHat
  • John Goodhue - MGHPCC


Submission Format
Please submit your papers in PDF format via the Web form. Do not email submissions. 
The complete submission must be no longer than six (6) pages not including references. We solicit original papers on the topics listed above but we also encourage the submission of shorter position papers that describe novel research directions and work that is in its formative stages, as well as papers about practical experiences and lessons learned from production systems.

Submissions should be typeset in two-column format in 10-point type on 12-point (single-spaced) leading, with the text block being no more than 6.5" wide by 9" deep. The names of authors and their affiliations should be included on the first page of the submission.

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