The Design of a Performance Steering System for Component-Based Grid Applications

Mayes, Ken; Riley, Graham; Ford, Rupert; Luján, Mikel; Freeman, Len; Addison, Cliff

doi:10.1007/978-1-4615-0361-3_7

Cited by 8 publications

(8 citation statements)

References 4 publications

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“…Also, instead of fixing the rescheduler threshold at 30%, our future work will involve determining the rescheduling threshold dynamically based on the dynamic observation of load behavior on the system resources. We propose to investigate the usefulness of our approach for complex applications involving multiple components and/or written in multi-programming languages similar to the efforts of Mayes et al [35]. Currently, the average of performance ratios is used to determine when a contract monitor will contact the rescheduler for migration.…”

Section: Discussionmentioning

confidence: 98%

Self adaptivity in Grid computing

Vadhiyar

Dongarra

2005

Concurrency and Computation

104

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Optimizing a given software system to exploit the features of the underlying system has been an area of research for many years. Recently, a number of self‐adapting software systems have been designed and developed for various computing environments. In this paper, we discuss the design and implementation of a software system that dynamically adjusts the parallelism of applications executing on computational Grids in accordance with the changing load characteristics of the underlying resources. The migration framework implemented by our software system is aimed at performance‐oriented Grid systems and implements tightly coupled policies for both suspension and migration of executing applications. The suspension and migration policies consider both the load changes on systems as well as the remaining execution times of the applications thereby taking into account both system load and application characteristics. The main goal of our migration framework is to improve the response times for individual applications. We also present some results that demonstrate the usefulness of our migration framework. Published in 2005 by John Wiley & Sons, Ltd.

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Section: Discussionmentioning

confidence: 98%

Self adaptivity in Grid computing

Vadhiyar

Dongarra

2005

Concurrency and Computation

104

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“…Thus the GCF facilitates an approach to performance control based on redeployment of components across platforms with different performance capabilities. A design for a performance control system (PERCO) using component-based application software has been published previously [3]. As shown in Figure 4, the components are arranged in a two-tier hierarchy, with an individual component performance steerer (CPS) attached to each component, and an application performance steerer (APS) linking all the CPSs together to exercise overall control.…”

Section: A Performance Control Systemmentioning

confidence: 99%

Performance control of scientific coupled models in Grid environments

Armstrong

Ford

Gurd

et al. 2005

Concurrency and Computation

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SUMMARYIn recent years, there has been increasing interest in the development of computer simulations of complex biological systems, and of multi-physics and multi-scale physical phenomena. Applications have been developed that involve the coupling together of separate executable models of individual systems, where these models may have been developed in isolation. A lightweight yet general solution is required to problems of linking coupled models, and of handling the incompatibilities between interacting models that arise from their diverse origins and natures. Many such models require high-performance computers to provide acceptable execution times, and there is increasing interest in utilizing Grid technologies. However, Grid applications need the ability to cope with heterogeneous and dynamically changing execution environments, particularly where run-time changes can affect application performance. A general coupling framework (GCF) is described that allows the construction of flexible coupled models. This approach results in a component-based implementation of a coupled model application. A semi-formal presentation of GCF is given. Components under GCF are separately deployable and coupled by simple data flows, making them appropriate structures for dynamic execution platforms such as the Grid. The design and initial implementation of a performance control system (PERCO) is reviewed. PERCO acts by redeploying components, and is thus appropriate for controlling GCF coupled model applications. Redeployment decisions in PERCO require performance prediction capabilities. A proof-of-concept performance prediction algorithm is presented, based on the descriptions of GCF and PERCO. Copyright

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“…The basic architecture reflects that of PerCo (Mayes et al 2004). PERCOSIM performs combined continuous/discrete event simulation, with distinct mechanisms of simulation for the Scheduler, Application Performance Steerer and Component Performance Steerer elements of PerCo.…”

Section: Experimental Studymentioning

confidence: 99%

“…PERCOSIM is a new toolkit that provides a set of core abstractions for modelling Grid scheduling problems and for simulating scheduling algorithms; the details of PERCOSIM will be given in Tellier (2009). The basic architecture reflects that of PerCo (Mayes et al 2004). PERCOSIM performs combined continuous/discrete event simulation, with distinct mechanisms of simulation for the Scheduler, Application Performance Steerer and Component Performance Steerer elements of PerCo.…”

Section: Experimental Studymentioning

confidence: 99%

Improved performance control on the Grid

Tellier

Riley

Freeman

2009

Phil. Trans. R. Soc. A.

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In the 2005 paper by Mayes et al., a threshold-based performance control system (PerCo) was described and an initial experimental evaluation was presented. The objective of the current paper is to investigate the role of the threshold value in PerCo and to place the threshold-based rescheduling heuristic on a more principled footing. Simulation enables us to identify the 'optimal' threshold value for a particular application scenario, and we show that this optimal value results in a 10 per cent improvement in performance for the application considered by Mayes et al. Furthermore, we find that the execution time of this optimal threshold-based schedule is very close (within 0.5%) to the execution time that results from a linear programming optimal schedule.

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The Design of a Performance Steering System for Component-Based Grid Applications

Cited by 8 publications

References 4 publications

Self adaptivity in Grid computing

Self adaptivity in Grid computing

Performance control of scientific coupled models in Grid environments

Improved performance control on the Grid

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