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CometCloud is an autonomic computing engine

 Overview

CometCloud is an autonomic computing engine for Cloud and Grid environments. It is based on the Comet decentralized coordination substrate, and supports highly heterogeneous and dynamic cloud/Grid infrastructures, integration of public/private clouds and autonomic cloudbursts. CometCloud provides a shared coordination space over the Chord overlay network and various types of programming paradigms such as Master/Worker, Workflow, and MapReduce/Hadoop.

CometCloud supports autonomic cloudbursts and autonomic cloud-bridging on a virtual cloud which integrates local computational environments and public cloud services on-the-fly. Also it supports real-world scientific and engineering applications.

What is CometCloud capable of?

  • Support core programming paradigms for real-world data and compute intensive applications
  • Enables autonomic cloudbursts and cloud-bridging and on-demand scale-out and scale-in, driven by dynamic policies, economic model, QoS constraints, etc.
  • Programming system support deployment of native (Java as well as non-Java) applications without any change.
  • Current deployments include a virtual cloud integrating Amazon EC2, Eucalyptus, local clusters, and TeraGrid.
  • Incorporates mechanisms for fault-tolerance to handle node and communication failures and recovery.

 

What are CometCloud Features?

  • Pull-based task consumption: workers pull tasks from the Comet space whenever they are available
  • Policy-based autonomic cloudbursts: CometCloud supports on-demand scale-out and scale-in by policy and economic model.
  • Cloud-bridging: CometCloud provides a cloud-bridging over a virtual cloud which integrates datacenter, clouds such as Amazon EC2 and Eucalyptus, and Grids such as Teragrid.
  • Support for multi-core processors: users can run multiple workers on a node and utilized multiple cores.
  • Support MapReduce with naïve disk writing: instead of using Hadoop file system, CometCloud uses the Comet space for storing data to support MapReduce and writes data into the disk if available memory amount goes below a threshold.
  • Master throttling: CometCloud can control the rate of task generation of masters. If the rate is high, then more tasks keep in the space.
  • Multiple masters: multiple masters can generate tasks and insert them into the same Comet space to collaborate.
  • Task update: if a task with the same task id is inserted, the existing task is updated.
  • Garbage collection: if tasks remain in the space for some time without being consumed, then they are cleared.
  • Distributed task generation by workers: even workers can generate tasks and insert them into the space. Because every task should have a unique task id, CometCloud provides a way to provide a globally unique id to distributed workers.
  • Fault-tolerance by replicating task space: each node replicates the preceding node’s local space, and it merges the replicated space when its preceding node fails.
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