Tutorial Descriptions

Tuesday, June 17, 2014

Half Day Morning

Daniel Menascé is the author of over 225 papers and five books published by Prentice Hall and translated into Russian, Korean, and Portuguese. He has received two Outstanding Teaching awards from George Mason University and was a finalist in the 2014 Outstanding Faculty competition among all public and private higher education institutions in the state of Virginia. Menascé has given keynote addresses and presented tutorials at various conferences.

T1
Autonomic Computing and Its Applications
Daniel A. Menascé, George Mason University

Daniel Menascé is the author of over 225 papers and five books published by Prentice Hall and translated into Russian, Korean, and Portuguese. He has received two Outstanding Teaching awards from George Mason University and was a finalist in the 2014 Outstanding Faculty competition among all public and private higher education institutions in the state of Virginia. Menascé has given keynote addresses and presented tutorials at various conferences.

9:00 am-12:30 pm

Washington AB

This tutorial provides an overview of AC and the various technologies that have been used to design and implement AC systems. Examples will be given in a variety of areas. The tutorial follows this outline:

  1. AC Overview (15 min)
  2. Techniques used: model-driven, learning-based, control-theory (45 min)
  3. Applications of AC (1 hour and 45 minutes):
    • Cloud computing and data centers
    • Adaptive software systems
    • E-commerce and Web systems
    • SOA systems
    • Databases
    • Emergency departments
  4. Concluding Remarks (15 min)
Who should attend:

Researchers and Autonomic Computing (AC) practitioners. No prerequisites required.

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Half Day Afternoon

Dr. Iqbal Mohomed is a Research Staff Member at IBM's T.J. Watson Research Center in NY. His research interests are in distributed systems, cloud and mobile computing. Most recently, he is working on workload orchestration in cloud environments.

Dr. Mohomed earned his Ph.D. from the University of Toronto in 2008. His dissertation work was on automatic customization of web content for mobile devices. As a postdoc at Microsoft Research Silicon Valley, he worked on several middleware systems for mobile devices including the StarTrack middleware for efficient organization of user location data and the Contrail system for enabling secure communication across mobile users (awarded Best Paper at Middleware 2011). At IBM Research, Dr. Mohomed has worked on various projects including the use of personal mobile devices to enable efficient long-term health monitoring (HARMONI), a cloud-based monitoring system for virtual machines (Cloudscope) and a distributed system for optimizing placement of resources in a private cloud computing system.

Dr. Asser N. Tantawi is a Research Staff Member at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY. He received his Ph.D. degree in computer science from Rutgers University in 1982. Dr. Tantawi has published numerous articles in scientific journals and international conferences. His fields of interest include cloud optimization, workload management, analytics, systems modeling, stochastic analysis, model-based control, load balancing, resource optimization, queueing models. He is a senior member of IEEE and a member of ACM and IFIP WG 7.3 (Computer System Modeling). He has also served as an ACM national lecturer.

T2
Autonomic Cloud Workload Optimization: Placement in OpenStack
Iqbal I. Mohomed, IBM T.J. Watson Research Center

Dr. Iqbal Mohomed is a Research Staff Member at IBM's T.J. Watson Research Center in NY. His research interests are in distributed systems, cloud and mobile computing. Most recently, he is working on workload orchestration in cloud environments.

Dr. Mohomed earned his Ph.D. from the University of Toronto in 2008. His dissertation work was on automatic customization of web content for mobile devices. As a postdoc at Microsoft Research Silicon Valley, he worked on several middleware systems for mobile devices including the StarTrack middleware for efficient organization of user location data and the Contrail system for enabling secure communication across mobile users (awarded Best Paper at Middleware 2011). At IBM Research, Dr. Mohomed has worked on various projects including the use of personal mobile devices to enable efficient long-term health monitoring (HARMONI), a cloud-based monitoring system for virtual machines (Cloudscope) and a distributed system for optimizing placement of resources in a private cloud computing system.


Asser N. Tantawi, IBM T.J. Watson Research Center

Dr. Asser N. Tantawi is a Research Staff Member at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY. He received his Ph.D. degree in computer science from Rutgers University in 1982. Dr. Tantawi has published numerous articles in scientific journals and international conferences. His fields of interest include cloud optimization, workload management, analytics, systems modeling, stochastic analysis, model-based control, load balancing, resource optimization, queueing models. He is a senior member of IEEE and a member of ACM and IFIP WG 7.3 (Computer System Modeling). He has also served as an ACM national lecturer.

1:30 pm-5:00 pm

Washington AB

This tutorial opens the door for the ICAC audience to apply some of the autonomic computing ideas to the optimized deployment of workloads in the cloud. We have designed the tutorial to have two parts: (I) Overview of cloud management, OpenStack, Heat, and HOT technologies; and (II) Optimization algorithms for solving the large-scale placement problem of workloads in the cloud, in a scaleable manner. Part I acts as an introduction to the area for those who may be experts in autonomic computing, but are not quite familiar with the state-of-the-art of cloud management. And, part II should appeal to the theoreticians and application-oriented in the audience alike.

  • Overview of cloud management (1.5 hrs):
    • Overview of OpenStack open source cloud software
    • Heat template-driven orchestration engine
    • HOT: The Heat orchestration template
    • Cloud workload definition
    • Architecture of a workload placement engine
    • End-to-end flow
  • Workload Optimization (1.5 hrs)
    • Definition of workload placement optimization problem
    • Problem complexity and scalability
    • Algorithmic approaches to placement optimization
    • Examples and case studies

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