File Name: cluster computing and grid computing .zip
A computer cluster is a set of loosely or tightly connected computers that work together so that, in many aspects, they can be viewed as a single system. Unlike grid computers , computer clusters have each node set to perform the same task, controlled and scheduled by software. The components of a cluster are usually connected to each other through fast local area networks , with each node computer used as a server running its own instance of an operating system.
Clusters are usually deployed to improve performance and availability over that of a single computer, while typically being much more cost-effective than single computers of comparable speed or availability.
Computer clusters emerged as a result of convergence of a number of computing trends including the availability of low-cost microprocessors, high-speed networks, and software for high-performance distributed computing. In contrast to high-reliability mainframes clusters are cheaper to scale out, but also have increased complexity in error handling, as in clusters error modes are not opaque to running programs.
The desire to get more computing power and better reliability by orchestrating a number of low-cost commercial off-the-shelf computers has given rise to a variety of architectures and configurations. The computer clustering approach usually but not always connects a number of readily available computing nodes e. Computer clustering relies on a centralized management approach which makes the nodes available as orchestrated shared servers. It is distinct from other approaches such as peer to peer or grid computing which also use many nodes, but with a far more distributed nature.
A computer cluster may be a simple two-node system which just connects two personal computers, or may be a very fast supercomputer. A basic approach to building a cluster is that of a Beowulf cluster which may be built with a few personal computers to produce a cost-effective alternative to traditional high performance computing. An early project that showed the viability of the concept was the node Stone Soupercomputer.
Although a cluster may consist of just a few personal computers connected by a simple network, the cluster architecture may also be used to achieve very high levels of performance.
The TOP organization's semiannual list of the fastest supercomputers often includes many clusters, e. Greg Pfister has stated that clusters were not invented by any specific vendor but by customers who could not fit all their work on one computer, or needed a backup.
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 published what has come to be regarded as the seminal paper on parallel processing: Amdahl's Law. The history of early computer clusters is more or less directly tied into the history of early networks, as one of the primary motivations for the development of a network was to link computing resources, creating a de facto computer cluster.
The first production system designed as a cluster was the Burroughs B in the mids. This allowed up to four computers, each with either one or two processors, to be tightly coupled to a common disk storage subsystem in order to distribute the workload.
Unlike standard multiprocessor systems, each computer could be restarted without disrupting overall operation. The ARC 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. Within the same time frame, while computer clusters used parallelism outside the computer on a commodity network, supercomputers began to use them within the same computer. Following the success of the CDC in , the Cray 1 was delivered in , and introduced internal parallelism via vector processing.
Computer clusters may be configured for different purposes ranging from general purpose business needs such as web-service support, to computation-intensive scientific calculations. In either case, the cluster may use a high-availability approach. Note that the attributes described below are not exclusive and a "computer cluster" may also use a high-availability approach, etc.
For example, a web server cluster may assign different queries to different nodes, so the overall response time will be optimized. Computer clusters are used for computation-intensive purposes, rather than handling IO-oriented operations such as web service or databases. Very tightly coupled computer clusters are designed for work that may approach " supercomputing ".
They operate by having redundant nodes , which are then used to provide service when system components fail. 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. Clusters are primarily designed with performance in mind, but installations are based on many other factors.
Fault tolerance the ability for a system to continue working with a malfunctioning node allows for scalability , and in high performance situations, low frequency of maintenance routines, resource consolidation e. RAID , and centralized management. Advantages include enabling data recovery in the event of a disaster and providing parallel data processing and high processing capacity.
In terms of scalability, clusters provide this in their ability to add nodes horizontally. This means that more computers may be added to the cluster, to improve its performance, redundancy and fault tolerance. This can be an inexpensive solution for a higher performing cluster compared to scaling up a single node in the cluster. This property of computer clusters can allow for larger computational loads to be executed by a larger number of lower performing computers.
When adding a new node to a cluster, reliability increases because the entire cluster does not need to be taken down. A single node can be taken down for maintenance, while the rest of the cluster takes on the load of that individual node. If you have a large number of computers clustered together, this lends itself to the use of distributed file systems and RAID , both of which can increase the reliability and speed of a cluster.
One of the issues in designing a cluster is how tightly coupled the individual nodes may be. For instance, a single computer job may require frequent communication among nodes: this implies that the cluster shares a dedicated network, is densely located, and probably has homogeneous nodes.
The other extreme is where a computer job uses one or few nodes, and needs little or no inter-node communication, approaching grid computing. In a Beowulf cluster , the application programs never see the computational nodes also called slave computers but only interact with the "Master" which is a specific computer handling the scheduling and management of the slaves. However, the private slave network may also have a large and shared file server that stores global persistent data, accessed by the slaves as needed.
A special purpose node DEGIMA cluster is tuned to running astrophysical N-body simulations using the Multiple-Walk parallel treecode, rather than general purpose scientific computations.
Due to the increasing computing power of each generation of game consoles , a novel use has emerged where they are repurposed into High-performance computing HPC clusters.
Another example of consumer game product is the Nvidia Tesla Personal Supercomputer workstation, which uses multiple graphics accelerator processor chips. Besides game consoles, high-end graphics cards too can be used instead. The use of graphics cards or rather their GPU's to do calculations for grid computing is vastly more economical than using CPU's, despite being less precise.
However, when using double-precision values, they become as precise to work with as CPU's and are still much less costly purchase cost. Computer clusters have historically run on separate physical computers with the same operating system. With the advent of virtualization , the cluster nodes may run on separate physical computers with different operating systems which are painted above with a virtual layer to look similar.
As the computer clusters were appearing during the s, so were supercomputers. One of the elements that distinguished the three classes at that time was that the early supercomputers relied on shared memory. To date clusters do not typically use physically shared memory, while many supercomputer architectures have also abandoned it. However, the use of a clustered file system is essential in modern computer clusters.
PVM must be directly installed on every cluster node and provides a set of software libraries that paint the node as a "parallel virtual machine". PVM provides a run-time environment for message-passing, task and resource management, and fault notification.
MPI emerged in the early s out of discussions among 40 organizations. Rather than starting anew, the design of MPI drew on various features available in commercial systems of the time.
The MPI specifications then gave rise to specific implementations. One of the challenges in the use of a computer cluster is the cost of administrating it which can at times be as high as the cost of administrating N independent machines, if the cluster has N nodes. When a large multi-user cluster needs to access very large amounts of data, task scheduling becomes a challenge. In a heterogeneous CPU-GPU cluster with a complex application environment, the performance of each job depends on the characteristics of the underlying cluster.
When a node in a cluster fails, strategies such as " fencing " may be employed to keep the rest of the system operational. There are two classes of fencing methods; one disables a node itself, and the other disallows access to resources such as shared disks.
For instance, power fencing uses a power controller to turn off an inoperable node. The resources fencing approach disallows access to resources without powering off the node.
Load balancing clusters such as web servers use cluster architectures to support a large number of users and typically each user request is routed to a specific node, achieving task parallelism without multi-node cooperation, given that the main goal of the system is providing rapid user access to shared data. However, "computer clusters" which perform complex computations for a small number of users need to take advantage of the parallel processing capabilities of the cluster and partition "the same computation" among several nodes.
Automatic parallelization of programs remains a technical challenge, but parallel programming models can be used to effectuate a higher degree of parallelism via the simultaneous execution of separate portions of a program on different processors.
The development and debugging of parallel programs on a cluster requires parallel language primitives as well as suitable tools such as those discussed by the High Performance Debugging Forum HPDF which resulted in the HPD specifications. Application checkpointing can be used to restore a given state of the system when a node fails during a long multi-node computation.
Checkpointing can restore the system to a stable state so that processing can resume without having to recompute results. Linux Virtual Server , Linux-HA - director-based clusters that allow incoming requests for services to be distributed across multiple cluster nodes. Although most computer clusters are permanent fixtures, attempts at flash mob computing have been made to build short-lived clusters for specific computations. However, larger-scale volunteer computing systems such as BOINC -based systems have had more followers.
From Wikipedia, the free encyclopedia. Not to be confused with data cluster or grid computing. For the journal, see Cluster Computing journal.
Main article: History of computer clusters. See also: History of supercomputing. Main article: Message passing in computer clusters.
Stack Overflow. Retrieved 2 June Georgia Tech College of Computing. Archived from the original on Retrieved The Telegraph. Retrieved 18 Jun Morgan Kaufmann Publishers. Hargrove, Forrest M. Hoffman and Thomas Sterling August 16,
The course aimed to help researchers to understand the Grid key concepts and the role of Grid Computing in computationally intensive problems. This included working with portals, workflow management systems and the Grid middleware. The mooc participants were provided with student accounts and a preconfigured Virtual Machine VM with all the necessary Grid tools installed. If you want to run the examples presented in the video lectures below you will have to request your personal Grid account, see Prerequisites. Please contact helpdesk surfsara. We have prepared a set of animations to display the basic usage of Grid Infrastructure.
Abstract: An in-depth study of these computing environments Cluster, Grid and Cloud computing , based on these findings the following data placement issues are identified: storage discovery, storage allocation, data replication, data consistency control, reliable file transfer, job-aware data placement optimization, data security and transactions. One of the major concerns is data security in cloud computing environment. Cloud computing is a flexible ,cost effective and proven delivery platform for providing business or consumer IT services over the internet. Many computer resources such as hardware and software are collected into the resource pool which can be assessed by the users via the internet through web browsers or desktops or mobile devices. In this paper, we are going to compare the performance of all the technologies which leads to the emergence of cloud computing . We have experienced a tremendous change in computing from earlier times till now. Earlier, large computers are kept in large closed spaces and only the professional are allowed to operate them .
A computer cluster is a set of loosely or tightly connected computers that work together so that, in many aspects, they can be viewed as a single system. Unlike grid computers , computer clusters have each node set to perform the same task, controlled and scheduled by software. The components of a cluster are usually connected to each other through fast local area networks , with each node computer used as a server running its own instance of an operating system. Clusters are usually deployed to improve performance and availability over that of a single computer, while typically being much more cost-effective than single computers of comparable speed or availability. Computer clusters emerged as a result of convergence of a number of computing trends including the availability of low-cost microprocessors, high-speed networks, and software for high-performance distributed computing.
PDF | The Department of Computer Science and Software Engineering at the University of Melbourne, Australia, is offering a master's-level.
Grid and Cluster Computing are becoming the two main approaches for building large-scale and cost-effective computing infrastructures. Cluster computing relies on the gathering of just a few, or up to thousands, of inexpensive personal computers combined into a single machine, i. However, despite that cluster computing is a more economically viable approach for building a supercomputing platform, the effective programming of such a computing infrastructure remains difficult. Grid computing is pushing this idea further by clustering a large variety of geographically distributed resources like computer, storage, data and other services, at a wider scale. Grids rely heavily on the Internet to connect all these resources together and offer them to the users as transparently as possible.
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