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The Convergence of Clouds, Grids, and Autonomics

Cécile Germain-Renaud, Université Paris-Sud 11
Omer F. Rana, Cardiff University

Pages: pp. 9

This excerpt reports on a panel that took place in conjunction with the 2009 IEEE International Conference on Autonomic Computing. The full panel report, including the panelists' recommendations, is available for free on Computing Now (


At the Grids Meet Autonomic Computing workshop that took place in conjunction with the IEEE International Conference on Autonomic Computing this year (ICAC 2009), we organized a panel called "Grids/Clouds/Autonomics Convergence." This panel focused on key research challenges in grid and cloud computing that autonomic computing techniques could support. The panelists were Marc-Elian Bégin of SixSq, Robert (Bob) Jones of CERN and director of the European Enabling Grids for E-sciencE ( EGEE) project, Manish Parashar of Rutgers University and the US National Science Foundation Center for Autonomic Computing, and Onn Shehory of IBM Haifa Labs. We asked them to consider these questions:

  • What two key challenges in grid and cloud computing could autonomics address?
  • What one key challenge would you like to focus on in the next two to three years?
  • What one action would you take (or encourage) to facilitate better interaction between the grid and cloud computing communities and the autonomics community?

Key Challenges in Grid and Cloud Computing

Because both grid and cloud computing infrastructures involve complex aggregation of resources, all panel members noted that more effective systems management is important. They observed that existing cloud computing infrastructure already supports some autonomic concepts, such as determining the number of virtual machines (VMs) to support, providing dynamically expandable storage, and migrating workloads across computational platforms. They also discussed the fact that the key differentiator between grids or clouds and enterprise data centers, from the viewpoint of the role of autonomics, is the nature of the applications and usage modes. Although existing autonomic computing research can directly be applied at the infrastructure level (server, VM, cluster management, and so on), it can't be applied at the application level (or at least wasn't being applied that way when the panel took place). The panelists also said that several types of cloud environments exist, so it's necessary to understand how to use autonomic concepts across multiple cloud owners and providers.

Autonomic computing techniques could address various aspects of system behavior. For instance, it could provide

  • management of unpredictable system behavior and unforeseen user behavior and abuse;
  • better management of quality of service (QoS), primarily to gain greater confidence from the user community, thereby adding value to existing deployment;
  • better management of energy consumption; and
  • more effective resource management to support scalability so that resources behave "elastically" at higher usage levels.


About the Authors

Cécile Germain-Renaud is a professor in the Computer Science Laboratory, Université Paris-Sud 11. Contact her at
Omer F. Rana is a professor in performance engineering at the School of Computer Science, Cardiff University. Contact him at
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