Cluster computing :A computer cluster is a group of linked computers, working together closely so that in many respects they form a single computer. The components of a cluster are commonly, but not always, connected to each other through fast local area networks. Clusters are usually deployed to improve performance and/or availability over that of a single computer, while typically being much more cost-effective than single computers of comparable speed or availability.[1]
Contents
* 1 Cluster categorizations
o 1.1 High-availability (HA) clusters
o 1.2 Load-balancing clusters
o 1.3 Compute clusters
o 1.4 Grid computing
* 2 Implementations
o 2.1 Consumer game consoles
* 3 History
* 4 Technologies
* 5 See also
* 6 References
o 6.1 Further reading
* 7 External links
Cluster categorizations
High-availability (HA) clusters
High-availability clusters (also known as Failover Clusters) are implemented primarily for the purpose of improving the availability of services that the cluster provides. They operate by having redundant nodes, which are then used to provide service when system components fail. The most common size for an HA cluster is two nodes, which is the minimum requirement to provide redundancy. HA cluster implementations attempt to use redundancy of cluster components to eliminate single points of failure.
There are commercial implementations of High-Availability clusters for many operating systems. The Linux-HA project is one commonly used free software HA package for the Linux operating system.
Load-balancing clusters
Load-balancing is when multiple computers are linked together to share computational workload or function as a single virtual computer. Logically, from the user side, they are multiple machines, but function as a single virtual machine. Requests initiated from the user are managed by, and distributed among, all the standalone computers to form a cluster. This results in balanced computational work among different machines, improving the performance of the cluster system.
Compute clusters
Often clusters are used primarily for computational purposes, rather than handling IO-oriented operations such as web service or databases. For instance, a cluster might support computational simulations of weather or vehicle crashes. The primary distinction within compute clusters is how tightly-coupled the individual nodes are. For instance, a single compute job may require frequent communication among nodes - this implies that the cluster shares a dedicated network, is densely located, and probably has homogenous nodes. This cluster design is usually referred to as Beowulf Cluster. The other extreme is where a compute job uses one or few nodes, and needs little or no inter-node communication. This latter category is sometimes called "Grid" computing. Tightly-coupled compute clusters are designed for work that might traditionally have been called "supercomputing". Middleware such as MPI (Message Passing Interface) or PVM (Parallel Virtual Machine) permits compute clustering programs to be portable to a wide variety of clusters.
Grid computing
Main article: Grid computing
Grids are usually computer clusters, but more focused on throughput like a computing utility rather than running fewer, tightly-coupled jobs. Often, grids will incorporate heterogeneous collections of computers, possibly distributed geographically, sometimes administered by unrelated organizations.
Grid computing is optimized for workloads which consist of many independent jobs or packets of work, which do not have to share data between the jobs during the computation process. Grids serve to manage the allocation of jobs to computers which will perform the work independently of the rest of the grid cluster. Resources such as storage may be shared by all the nodes, but intermediate results of one job do not affect other jobs in progress on other nodes of the grid.
An example of a very large grid is the Folding@home project. It is analyzing data that is used by researchers to find cures for diseases such as Alzheimer's and cancer. Another large project is the SETI@home project, which may be the largest distributed grid in existence. It uses approximately three million home computers all over the world to analyze data from the Arecibo Observatory radiotelescope, searching for evidence of extraterrestrial intelligence. In both of these cases, there is no inter-node communication or shared storage. Individual nodes connect to a main, central location to retrieve a small processing job. They then perform the computation and return the result to the central server. In the case of the @home projects, the software is generally run when the computer is otherwise idle. U of C Berkley has developed an open source application BOINC to allow individual users to contribute to the above and other projects such as lhc@home (Large Hadron Collider) from a single manager which can then be set to allocate a percentage of idle time to each of the projects a node is signed up for. The Software can be downloaded and a project list can be found here BOINC
The grid setup means that the nodes can take however many jobs they are able to process in one session and then return the results and acquire a new job from a central project server.
Contents
* 1 Cluster categorizations
o 1.1 High-availability (HA) clusters
o 1.2 Load-balancing clusters
o 1.3 Compute clusters
o 1.4 Grid computing
* 2 Implementations
o 2.1 Consumer game consoles
* 3 History
* 4 Technologies
* 5 See also
* 6 References
o 6.1 Further reading
* 7 External links
Cluster categorizations
High-availability (HA) clusters
High-availability clusters (also known as Failover Clusters) are implemented primarily for the purpose of improving the availability of services that the cluster provides. They operate by having redundant nodes, which are then used to provide service when system components fail. The most common size for an HA cluster is two nodes, which is the minimum requirement to provide redundancy. HA cluster implementations attempt to use redundancy of cluster components to eliminate single points of failure.
There are commercial implementations of High-Availability clusters for many operating systems. The Linux-HA project is one commonly used free software HA package for the Linux operating system.
Load-balancing clusters
Load-balancing is when multiple computers are linked together to share computational workload or function as a single virtual computer. Logically, from the user side, they are multiple machines, but function as a single virtual machine. Requests initiated from the user are managed by, and distributed among, all the standalone computers to form a cluster. This results in balanced computational work among different machines, improving the performance of the cluster system.
Compute clusters
Often clusters are used primarily for computational purposes, rather than handling IO-oriented operations such as web service or databases. For instance, a cluster might support computational simulations of weather or vehicle crashes. The primary distinction within compute clusters is how tightly-coupled the individual nodes are. For instance, a single compute job may require frequent communication among nodes - this implies that the cluster shares a dedicated network, is densely located, and probably has homogenous nodes. This cluster design is usually referred to as Beowulf Cluster. The other extreme is where a compute job uses one or few nodes, and needs little or no inter-node communication. This latter category is sometimes called "Grid" computing. Tightly-coupled compute clusters are designed for work that might traditionally have been called "supercomputing". Middleware such as MPI (Message Passing Interface) or PVM (Parallel Virtual Machine) permits compute clustering programs to be portable to a wide variety of clusters.
Grid computing
Main article: Grid computing
Grids are usually computer clusters, but more focused on throughput like a computing utility rather than running fewer, tightly-coupled jobs. Often, grids will incorporate heterogeneous collections of computers, possibly distributed geographically, sometimes administered by unrelated organizations.
Grid computing is optimized for workloads which consist of many independent jobs or packets of work, which do not have to share data between the jobs during the computation process. Grids serve to manage the allocation of jobs to computers which will perform the work independently of the rest of the grid cluster. Resources such as storage may be shared by all the nodes, but intermediate results of one job do not affect other jobs in progress on other nodes of the grid.
An example of a very large grid is the Folding@home project. It is analyzing data that is used by researchers to find cures for diseases such as Alzheimer's and cancer. Another large project is the SETI@home project, which may be the largest distributed grid in existence. It uses approximately three million home computers all over the world to analyze data from the Arecibo Observatory radiotelescope, searching for evidence of extraterrestrial intelligence. In both of these cases, there is no inter-node communication or shared storage. Individual nodes connect to a main, central location to retrieve a small processing job. They then perform the computation and return the result to the central server. In the case of the @home projects, the software is generally run when the computer is otherwise idle. U of C Berkley has developed an open source application BOINC to allow individual users to contribute to the above and other projects such as lhc@home (Large Hadron Collider) from a single manager which can then be set to allocate a percentage of idle time to each of the projects a node is signed up for. The Software can be downloaded and a project list can be found here BOINC
The grid setup means that the nodes can take however many jobs they are able to process in one session and then return the results and acquire a new job from a central project server.
