Understanding grid resource information management through a synthetic database benchmark/nvorkload

Plale, Beth; Jacobs, C.; Jensen, Scott A.; Liu, Ying; Moad, Charles W.; Parab, R.; Vaidya, Praveenkumar

doi:10.1109/ccgrid.2004.1336578

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…In order to measure the performance of reading / writing from / to a MongoDB database, the author designed three possible scenarios which are a combination of the various ideas found in (Gray, 1992) (Plale, et al, 2004) (Moussa, 2012) (Yingjie Shi, Zhao, Hu, Liu, & Wang, 2010:…”

Section: Working Methodologymentioning

confidence: 99%

A Proposed Validation Method for a Benchmarking Methodology

Tudorica

2014

International Journal of Sustainable Economies Management

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The aim of this paper is to describe a meaning to validate a previously proposed benchmark method targeted at NoSQL databases. The method described here is based on a statistical analysis of the results given by the benchmarking software. The reason for proposing such a validation method is the opinion of the author that any benchmarking methodology should be both based on a rigorous algorithm and verified in a thorough way (by validation and by testing) before practical use.

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Section: Working Methodologymentioning

confidence: 99%

A Proposed Validation Method for a Benchmarking Methodology

Tudorica

2014

International Journal of Sustainable Economies Management

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“…In an effort to isolate and evaluate quantitatively the performance of Grid services, researchers have recently proposed synthetic micro-kernels that target Grid Information and Monitoring Services (GIS) [42,43]. The GIS micro-kernels generate sequences of queries, submitted to different GISs, and report measurements of the query response time and throughput (average number of requests processed by a service per second).…”

Section: Benchmarking Grid Servicesmentioning

confidence: 99%

“…The GIS micro-kernels generate sequences of queries, submitted to different GISs, and report measurements of the query response time and throughput (average number of requests processed by a service per second). Additional metrics supported are the ease of use, which quantifies the work that a client must undertake to obtain the desired information from the Grid service [42], and the average load of the machine running the service [43]. Implementations of the GIS micro-kernels were used to study the performance of alternative database systems for storing Grid information [42], and to compare the performance and scalability of three different GISs [43].…”

Section: Benchmarking Grid Servicesmentioning

confidence: 99%

Grid benchmarking: vision, challenges, and current status

Dikaiakos

2006

Concurrency and Computation

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SUMMARYGrid benchmarking is an important and challenging topic of Grid computing research. In this paper, we present an overview of the key challenges that need to be addressed for the integration of benchmarking practices, techniques, and tools in emerging Grid computing infrastructures. We discuss the problems of performance representation, measurement, and interpretation in the context of Grid benchmarking, and propose the use of ontologies for organizing and describing benchmarking metrics. Finally, we present a survey of ongoing research efforts that develop benchmarks and benchmarking tools for the Grid.

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“…The performance of grid monitoring and information services has been the subject of several experimental studies. Some studies evaluate a single implementation [21,16,15], while others compare several systems [20,19,28,29]. Some of the findings indicate that current hierarchical structures cannot guarantee a consistently low Query Response Time.…”

Section: Background and Related Workmentioning

confidence: 99%

An Importance-Aware Architecture for Large-Scale Grid Information Services

Zanikolas

Sakellariou

2008

Parallel Process. Lett.

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This paper is concerned with the scalability of large-scale grid monitoring and information services, which are mainly used for the discovery of resources of interest. Large-scale grid monitoring systems have to balance between three competing performance metrics: query response time, imposed network overhead, and information freshness. Improving one of the three metrics will affect another; any solution will be based on a trade-off. The paper is motivated by the observation that existing grid monitoring systems can only be manually configured for a trade-off among the three metrics, which applies equally to all monitored resources; this implies that all resources in a grid are considered to be of equal importance. Assuming that in a large-scale grid setting this is unlikely to hold, the paper proposes an importance-based monitoring architecture for large-scale grid information services, based on an adaptation of the web crawling paradigm. The main idea is that, since not all resources are of equal importance, one can vary the trade-off based on the relative importance of the monitored resources. The proposed architecture is described and evaluated based on large-scale deployments of a prototype implementation on PlanetLab.

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Understanding grid resource information management through a synthetic database benchmark/nvorkload

Cited by 8 publications

References 8 publications

A Proposed Validation Method for a Benchmarking Methodology

A Proposed Validation Method for a Benchmarking Methodology

Grid benchmarking: vision, challenges, and current status

An Importance-Aware Architecture for Large-Scale Grid Information Services

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