Grid Computing

Zhou, Lihua; Yun, Zhifeng; Allen, Gabrielle

doi:10.1201/9781420067682

Cited by 26 publications

(10 citation statements)

References 21 publications

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“…All these resources typically exist within nodes that are geographically distributed, and span multiple administrative domains. The virtual machine is constituted of a set of resources taken from a resource pool [34]. It is obvious that existed HPC parallel computers (supercomputers etc.)…”

Section: Grid Systemsmentioning

confidence: 99%

“…could be a member of such Grid systems too [31]. In general Grids represent a new way of managing and organizing of computer networks and mainly of their deeper resource sharing [34]. Grid systems are expected to operate on a wider range of other resources as processors (CPU), like storages, data modules, network components, software (typical resources) and atypical resources like graphical and audio input/output devices, sensors and so one (Fig.…”

Section: Grid Systemsmentioning

confidence: 99%

“…This term define massive parallel computer (supercomputer, Grid) with following basic characteristics [8,34] wide area network of integrated free computing resources. It is a massive number of interconnected networks, which are connected through high speed connected networks during which time whole massive system is controlled with network operation system, which makes an illusion of powerful computer system (virtual supercomputer) grants a function of meta computing that means computing environment, which enables to individual applications a functionality of all system resources system combines distributed parallel computation with remote computing from user workstations.…”

Section: Meta Computersmentioning

confidence: 99%

“…On the level of motherboard there is a multiple using of cores and processors in building multicore or multiprocessors system as SMP (symmetrical multiprocessor system) as powerful computation node, where such computation node is SMP parallel computer too [1]. On the level of individual computers the dominant trend is to use multiple number of high performed workstations based on single personal computers (PC) or SMP, which are connected in the network of workstations (NOW) or in a high integrated way named as Grid systems [34].…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Parallel Computers Based on Network of Computing

Hanuliak¹

2014

AJNC

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Abstract:The optimal resource allocation to satisfy such demands and the proper settlement of contention when demands exceed the capacity of the resources, constitute the problem of being able to understand and to predict system behavior. To this analysis we can use both analytical and simulation methods. Modeling and simulation are methods, which are commonly used by performance analysts to represent constraints and to optimize performance. Principally analytical methods represented first of all by queuing theory belongs to the preferred method in comparison to the simulation method, because of their potential ability of general analysis and also of their ability to potentially analyze also massive parallel computers. But these arguments supposed to develop and to verify suggested analytical models. This article goes further in applying the achieved analytical results in queuing theory for complex performance evaluation in parallel computing [9,14]. The extensions are mainly in extending derived analytical models to whole range of parallel computers including massive parallel computers (Grid, meta computer). The article therefore describes standard analytical model based on M/M/m, M/D/m and M/M/1, M/D/1 queuing theory systems. Then the paper describes derivation of the correction factor for standard analytical model, based on M/M/m and M/M/1 queuing systems, to study more precise their basic performance parameters (overhead latencies, throughput etc.). All the derived analytical models were compared with performed simulation results in order to estimate the magnitude of improvement. Likewise they were tested under various ranges of parameters, which influence the architecture of the parallel computers and its communication networks too. These results are very important in practical use.

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Section: Grid Systemsmentioning

confidence: 99%

Section: Grid Systemsmentioning

confidence: 99%

Section: Meta Computersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling of Parallel Computers Based on Network of Computing

Hanuliak¹

2014

AJNC

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“…Parallel computing and distributed computing have traditionally evolved as two separate research disciplines. Parallel computing has addressed problems of communication-intensive computation on highly -coupled processors [21,22] while distributed computing has been concerned with coordination, availability, timeliness, etc., of more likely coupled computations [28,34]. Workstations could be connected using different network technologies such as off the shelf devices like Ethernet to specialized high speed communication networks (Infiniband, Quadrics, Myrinet) [35].…”

Section: Introductionmentioning

confidence: 99%

Unified Analytical Models of Parallel and Distributed Computing

Hanuliak¹

2014

AJNC

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Abstract:The optimal resource allocation satisfies the needed capacity of the used resources. To such analysis we can use both analytical and simulation methods. Principally analytical methods (AM) belong to the preferred method in comparison to the simulation method, because of their potential ability of more general analysis and also of ability to analyze massive parallel computers. This article goes further in developing AM based on queuing theory results in relation to our published paper in [9]. The extensions are in extending derived AM to whole range of parallel computers and also to sum up public acceptance of our published paper. The article therefore describes deriving of correction factor of standard AM based on M/M/m and M/M/1queuing theory systems. In detail the paper describes derivation of a correction factor for standard AM to study more precise their performance. The paper contributions are in unified AM and in deriving correction factor in order to take into account real non-exponential nature of the inputs to the computing nodes and node's communication channels. The derived analytical results were compared with performed simulation results in order to estimate the magnitude of improvement. Likewise the corrected AM were tested under various ranges of parameters, which influence the architecture of the parallel computers and its communication networks too. These results are very important in practical use.

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