Clusters

Clusters

1. Clustering is an alternative to symmetric multiprocessing as an approach to providing high performance and high availability and is particularly attractive for server applications

   • Objectives or design requirements:
     • Absolute scalability: It is possible to create large clusters that far surpass the power of even the largest standalone machines. A cluster can have tens, hundreds, or even thousands of machines, each of which is a multiprocessor.
     • Incremental scalability: A cluster is configured in such a way that it is possible to add new systems to the cluster in small increments.
     • High availability: Because each node in a cluster is a standalone computer, the failure of one node does not mean loss of service.
     • Superior price/performance: By using commodity building blocks, it is possible to put together a cluster with equal or greater computing power than a single large machine, at much lower cost.

2. Clustering Methods: Benefits and Limitations

3. Operating System Design Issues

   • Failure Management: In general, two approaches can be taken to dealing with failures: highly available clusters and fault- tolerant clusters.
     • A highly available cluster offers a high probability that all resources will be in service.
     • A fault- tolerant cluster ensures that all resources are always available.
   • Load Balancing: A cluster requires an effective capability for balancing the load among available computers. This includes the requirement that the cluster be incrementally scalable.
   • Parallelizing Computation: In some cases, effective use of a cluster requires executing software from a single application in parallel. Three general approaches to the problem:
     • Parallelizing compiler: A parallelizing compiler determines, at compile time, which parts of an application can be executed in parallel. These are then split off to be assigned to different computers in the cluster.
     • Parallelized application: In this approach, the programmer writes the application from the outset to run on a cluster, and uses message passing to move data, as required, between cluster nodes.
     • Parametric computing: This approach can be used if the essence of the application is an algorithm or program that must be executed a large number of times, each time with a different set of starting conditions or parameters.

4. Cluster Computer Architecture

5. Desirable cluster middleware services and functions:

   • Single entry point: A user logs onto the cluster rather than to an individual computer.
   • Single file hierarchy: The user sees a single hierarchy of file directories under the same root directory.
   • Single control point: There is a default workstation used for cluster management and control.
   • Single virtual networking: Any node can access any other point in the cluster, even though the actual cluster configuration may consist of multiple interconnected networks. There is a single virtual network operation.
   • Single memory space: Distributed shared memory enables programs to share variables.
   • Single job-management system: Under a cluster job scheduler, a user can submit a job without specifying the host computer to execute the job.
   • Single user interface: A common graphic interface supports all users, regardless of the workstation from which they enter the cluster.
   • Single I/O space: Any node can remotely access any I/O peripheral or disk device without knowledge of its physical location.
   • Single process space: A uniform process- identification scheme is used. A process on any node can create or communicate with any other process on a remote node.
   • Checkpointing: This function periodically saves the process state and intermediate computing results, to allow rollback recovery after a failure.
   • Process migration: This function enables load balancing.

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