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BDCOM 2016 : Big Data for Cloud Operations Management: Problems, Approaches, Tools, and Best Practices


When Dec 5, 2016 - Dec 8, 2015
Where Washington, D.C., USA
Submission Deadline Oct 12, 2016
Notification Due Nov 1, 2016
Final Version Due Nov 15, 2016
Categories    big data   cloud   operational analytics

Call For Papers

Big Data for Cloud Operations Management: Problems, Approaches, Tools, and Best Practices

Both Big Data analytics and Cloud Computing are in 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

Sep 30, 2016: Due date for full workshop papers submission
Nov 1, 2016: Notification of paper acceptance to authors
Nov 15, 2016: Camera-ready of accepted papers due.
Dec 5-8, 2016: Workshops
Submission Format

The complete submission must be no longer than five (5) 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. If you wish, you may use this LaTeX template and style file (will ad hyperlink here). The names of authors and their affiliations should be included on the first page of the submission.

Submission Link:

Program Chairs

Sastry S Duri ( (IBM)
Prabhakar Kudva ( (IBM)
Ata Turk ( (Boston University)
Steering Committee

Canturk Isci - IBM
Ayse K. Coskun - Boston University
Larry Rudolph - Two Sigma
Program Committee Members

Kamer Kaya - Sabanci University
Alina Oprea - Northeastern University
David Cohen - Intel
Weisong Shi - Wayne University
Chris Stewart - The Ohio State University
Mukil Kesavan - VMWare
Susanne Balle - Intel
Homin Lee - Datadog
Eyal de Lara - Toronto University
Mahadev Satyanarayanan - Carnegie Mellon University
Stefan Zier - Sumologic
Dilma Da Silva - Texas A&M University
Thu D. Nguyen - Rutgers University

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