Everything about Computer Cluster totally explained
A
computer cluster is a group of coupled
computers that work together closely so that in many respects they can be viewed as though they're 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 provided by a single computer, while typically being much more cost-effective than single computers of comparable speed or availability.
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 which 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 manage the redundancy inherent in a cluster to eliminate
single points of failure.
There are many 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 OSs.
Load-balancing clusters
Load-balancing clusters operate by distributing a workload evenly over multiple back end nodes. Typically the cluster will be configured with multiple redundant load-balancing front ends.
Grid computing
Grid computing or grid clusters are a technology closely related to cluster computing. The key differences (by definitions which distinguish the two at all) between grids and traditional clusters are that grids connect collections of computers which don't fully trust each other, or which are geographically dispersed. Grids are thus more like a
computing utility than like a single computer. In addition, grids typically support more heterogeneous collections than are commonly supported in clusters.
Grid computing is optimized for workloads which consist of many independent jobs or packets of work, which don't 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 don't 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's 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.
Implementations
The
TOP500 organization's semiannual list of the 500 fastest computers usually includes many clusters. TOP500 is a collaboration between the
University of Mannheim, the
University of Tennessee, and the National Energy Research Scientific Computing Center at
Lawrence Berkeley National Laboratory. As of November 2007, the top
supercomputer is the
Department of Energy's IBM BlueGene/L system with performance of 478.2
TFlops measured with High-Performance
LINPACK benchmark.
Clustering can provide significant performance benefits versus price. The
System X supercomputer at
Virginia Tech, the 28th most powerful supercomputer on Earth as of June 2006
(External Link
), is a 12.25 TFlops computer cluster of 1100
Apple XServe G5 2.3 GHz dual-processor machines (4
GB RAM, 80 GB
SATA HD) running
Mac OS X and using
InfiniBand interconnect. The cluster initially consisted of
Power Mac G5s; the rack-mountable XServes are denser than desktop Macs, reducing the aggregate size of the cluster. The total cost of the previous Power Mac system was $5.2 million, a tenth of the cost of slower
mainframe computer supercomputers. (The Power Mac G5s were sold off.)
The central concept of a
Beowulf cluster is the use of
commercial off-the-shelf (COTS) computers to produce a cost-effective alternative to a traditional supercomputer. One project that took this to an extreme was the
Stone Soupercomputer.
However it's worth noting that FLOPs (floating point operations per second), aren't always the best metric for supercomputer speed. Clusters can have very high FLOPs, but they can't access all data the cluster as a whole has at once. Therefore clusters are excellent for parallel computation, but much poorer than traditional supercomputers at non-parallel computation.
JavaSpaces is a specification from
Sun Microsystems that enables clustering computers via a
distributed shared memory.
History
The history of cluster computing is best captured by a footnote in Greg Pfister's
In Search of Clusters: "Virtually every press release from DEC mentioning clusters says 'DEC, who invented clusters...'. IBM didn't invent them either.
Customers invented clusters, as soon as they couldn't fit all their work on one computer, or needed a backup. The date of the first is unknown, but it would be surprising if it wasn't in the 1960s, or even late 1950s."
The formal
engineering basis of cluster computing as a means of doing parallel work of any sort was arguably invented by Gene Amdahl of
IBM, who in 1967 published what has come to be regarded as the seminal paper on parallel processing:
Amdahl's Law. Amdahl's Law describes mathematically the speedup one can expect from parallelizing any given otherwise serially performed task on a parallel architecture. This article defined the engineering basis for both multiprocessor computing and cluster computing, where the primary differentiator is whether or not the interprocessor communications are supported "inside" the computer (on for example a customized internal communications bus or network) or "outside" the computer on a
commodity network.
Consequently the history of early computer clusters is more or less directly tied into the history of early networks, as one of the primary motivation for the development of a network was to link computing resources, creating a de facto computer cluster.
Packet switching networks were conceptually invented by the
RAND corporation in
1962. Using the concept of a packet switched network, the
ARPANET project succeeded in creating in
1969 what was arguably the world's first commodity-network based computer cluster by linking four different computer centers (each of which was something of a "cluster" in its own right, but probably not a
commodity cluster). The ARPANET project grew into the
Internet -- which can be thought of as "the mother of all computer clusters" (as the union of nearly all of the compute resources, including clusters, that happen to be connected). It also established the paradigm in use by
all computer clusters in the world today -- the use of packet-switched networks to perform interprocessor communications between processor (sets) located in otherwise disconnected frames.
The development of customer-built and research clusters proceeded hand in hand with that of both networks and the
Unix operating system from the early 1970s, as both
TCP/IP and the
Xerox PARC project created and formalized protocols for network-based communications. The
Hydra operating system was built for a cluster of DEC
PDP-11 minicomputers called
C.mmp at C-MU in 1971. However, it wasn't until circa
1983 that the protocols and tools for
easily doing remote job distribution and file sharing were defined (largely within the context of
BSD Unix, as implemented by
Sun Microsystems) and hence became generally available commercially, along with a shared filesystem.
The
first commercial clustering product was
ARCnet, developed by
Datapoint in 1977. ARCnet wasn't a commercial success and clustering per se didn't really take off until
DEC released their
VAXcluster product in
1984 for the
VAX/VMS operating system. The ARCnet and VAXcluster products not only supported parallel computing, but also shared
file systems and
peripheral devices. The idea was to provide the advantages of parallel processing, while maintaining data reliability and uniqueness. VAXcluster, now VMScluster, is still available on
OpenVMS systems from
HP running on Alpha and Itanium systems.
Two other noteworthy early commercial clusters were the
Tandem Himalaya (a circa
1994 high-availability product) and the
IBM S/390 Parallel Sysplex (also circa
1994, primarily for business use).
No history of commodity computer clusters would be complete without noting the pivotal role played by the development of
Parallel Virtual Machine (PVM) software in
1989. This
open source software based on
TCP/IP communications enabled the
instant creation of a virtual supercomputer -- a high performance compute cluster -- made out of any TCP/IP connected systems. Free form heterogeneous clusters built on top of this model rapidly achieved total throughput in
FLOPS that greatly exceeded that available even with the most expensive "
big iron" supercomputers. PVM and the advent of inexpensive networked PCs led, in
1993, to a
NASA project to build supercomputers out of commodity clusters. In
1995 the
invention of the "beowulf"-style cluster -- a
compute cluster built on top of a commodity network for the specific purpose of "being a supercomputer" capable of performing tightly coupled parallel HPC computations. This in turn spurred the independent development of
Grid computing as a named entity, although Grid-style clustering had been around at least as long as the
Unix operating system and the Arpanet, whether or not it, or the clusters that used it, were named.
Technologies
MPI is a widely-available communications library that enables parallel programs to be written in
C,
Fortran,
Python,
OCaml, and many other programming languages.
The GNU/Linux world supports various cluster software; for application clustering, there's
Beowulf,
distcc, and
MPICH.
Linux Virtual Server,
Linux-HA - director-based clusters that allow incoming requests for services to be distributed across multiple cluster nodes.
MOSIX,
openMosix,
Kerrighed,
OpenSSI are full-blown clusters integrated into the
kernel that provide for automatic process migration among homogeneous nodes. OpenSSI, openMosix and Kerrighed are
single-system image implementations.
Microsoft Windows Compute Cluster Server 2003 based on the
Windows Server platform provides pieces for High Performance Computing like the Job Scheduler, MSMPI library and management tools.
NCSA's recently installed Lincoln is a cluster of 450 Dell PowerEdge 1855 blade servers running Windows Compute Cluster Server 2003. This cluster debuted at #130 on the
Top500 list in June 2006.
gridMathematica provides distributed computations over clusters including data analysis, computer algebra and 3D visualization. It can make use of other technologies such as Altair PBS Professional, Microsoft Windows Compute Cluster Server, Platform LSF and Sun Grid Engine.
gLite is a set of middleware technologies created by the
Enabling Grids for E-sciencE (EGEE) project.
Further Information
Get more info on 'Computer Cluster'.
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