Untitled

Martino, Beniamino Di; Mancini, Emilio; Rak, Massimiliano; Torella, Roberto; Villano, Umberto

doi:10.1002/cpe.v19:11

Cited by 17 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This ESD signal can be easily passed to a third party to investigate the execution behavior. This way is different from traditional performance measurements which use application instrumentation to capture performance data such as communication frequency or instruction/second [14,20,23,27]. We use the human utility value to measure the performance rather than using specific performance metrics.…”

Section: Execution Satisfaction Degree (Esd)mentioning

confidence: 99%

“…The performance data is recorded and used either during the execution or later in post-mortem analysis to determine performance bottlenecks and opportunities for optimization. Examples are such as MATE [23], where an application is monitored, its performance bottlenecks are detected, and their solutions are given and the application code is modified on the fly to improve its performance. This software is dedicated to PVM applications and aims to change the application code but not the resource environment to guarantee the performance.…”

Section: Performance Measurement and Tuningmentioning

confidence: 99%

See 1 more Smart Citation

On-line feedback-based automatic resource configuration for distributed applications

Líu

Sorensen

2010

Cluster Comput

View full text Add to dashboard Cite

A key problem in executing performance critical applications on distributed computing environments (e.g. the Grid) is the selection of resources. Research related to "automatic resource selection" aims to allocate resources on behalf of users to optimize the execution performance. However, most of current approaches are based on the static principle (i.e. resource selection is performed prior to execution) and need detailed application-specific information. In the paper, we introduce a novel on-line automatic resource selection approach. This approach is based on a simple control theory: the application continuously reports the Execution Satisfaction Degree (ESD) to the middleware Application Agent (AA), which relies on the reported ESD values to learn the execution behavior and tune the computing environment by adding/replacing/deleting resources during the execution in order to satisfy users' performance requirements. We introduce two different policies applied to this approach to enable the AA to learn and tune the computing environment: the Utility Classification policy and the Desired Processing Power Estimation (DPPE) policy. Each policy is validated by an iterative application and a non-iterative application to demonstrate that both policies are effective to support most kinds of applications.

show abstract

Section: Execution Satisfaction Degree (Esd)mentioning

confidence: 99%

Section: Performance Measurement and Tuningmentioning

confidence: 99%

On-line feedback-based automatic resource configuration for distributed applications

Líu

Sorensen

2010

Cluster Comput

View full text Add to dashboard Cite

show abstract

“…As with NetLogger, the source code is required, as annotations are used to generate events. iPath [9] is a dynamic instrumentation tool with some interesting properties for a distributed setting. It was conceived and works on distributed systems.…”

Section: Related Workmentioning

confidence: 99%

X-Ray: Monitoring and Analysis of Distributed Database Queries

Guimarães

Pereira

2015

Distributed Applications and Interoperable Systems

View full text Add to dashboard Cite

“…When MATE is used, the run-time changes of the application, for both the monitoring and tuning processes, are implemented via the dynamic instrumentation library DynInst [18]. MATE and its approach have been previously presented in detail in [12,14].…”

Section: Matementioning

confidence: 99%

“…The main differences among them are related to the monitoring and tuning technology, and the knowledge representation used to analyze the performance of the application: fuzzy logic, heuristics, history, or performance models. In this paper we focus on MATE (Monitoring, Analysis and Tuning Environment) [12][13][14], a dynamic and automatic monitoring, analysis and tuning environment based on performance models. MATE provides certain advantages when tuning an application: the application does not have to be reimplemented with extra source code for the monitoring purposes since MATE automatically instruments the application on the fly.…”

Section: Introductionmentioning

confidence: 99%

A methodology for transparent knowledge specification in a dynamic tuning environment

et al. 2012

View full text Add to dashboard Cite

SUMMARY The increasing use of parallel/distributed applications demands a continuous support to take significant advantages from parallel power. This includes the evolution of performance analysis and tuning tools which automatically allows for obtaining a better behavior of the applications. Different approaches and tools have been proposed and they are continuously evolving to cover the requirements and expectations of users. One such tool is MATE (Monitoring Analysis and Tuning Environment), which provides automatic and dynamic tuning for parallel/distributed applications. The knowledge used by MATE to analyze and take decisions is based on performance models which include a set of performance parameters and a set of mathematical expressions modeling the solution of the performance problem. These elements are used by the tuning environment to conduct the monitoring and analysis steps, respectively. The tuning phase depends on the results of the performance analysis. This paper presents a methodology to specify performance models. Each performance model specification can be automatically and transparently translated into a piece of software code encapsulating the knowledge to be straightforwardly included in MATE. Applying this methodology, the user does not have to be involved in the implementation details of MATE, which makes the usage of the tool more transparent. Copyright © 2011 John Wiley & Sons, Ltd.

show abstract

Untitled

Cited by 17 publications

References 0 publications

On-line feedback-based automatic resource configuration for distributed applications

On-line feedback-based automatic resource configuration for distributed applications

X-Ray: Monitoring and Analysis of Distributed Database Queries

A methodology for transparent knowledge specification in a dynamic tuning environment

Contact Info

Product

Resources

About