On-demand service hosting on production grid infrastructures

Wang, Lizhe; Kurze, Tobias; Tao, Jie; Kunze, M.; Laszewski, Gregor von

doi:10.1007/s11227-011-0666-5

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…The cloud environment [10] provides a highly stable and less expensive service, thus increasing numbers of applications are being built in the cloud [11,12]. The advances in wireless communications mean that not all data need to be stored locally [13] and various data can be obtained via the cloud environment.…”

Section: Introductionmentioning

confidence: 99%

Mobile cloud-based depression diagnosis using an ontology and a Bayesian network

Chang

Fan

et al. 2015

Future Generation Computer Systems

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Section: Introductionmentioning

confidence: 99%

Mobile cloud-based depression diagnosis using an ontology and a Bayesian network

Chang

Fan

et al. 2015

Future Generation Computer Systems

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“…Cloud computing has been identified as a potential solution to address some of the big data challenges in remote sensing [7,8,9,10] and big data computing [11,12,13,14], such as allowing massive remote sensing data storage and complex data processing, providing on-demand services [15,16,17], and improving the timeliness of remote sensing information service delivery. For example, Lv, Hu, Zhong, Wu, Li and Zhao [18] demonstrated the feasibility of using MapReduce and parallel K-means clustering for remote sensing image storage and processing.…”

Section: Introductionmentioning

confidence: 99%

A cloud-based remote sensing data production system

Yan

Wang

et al. 2018

Future Generation Computer Systems

Self Cite

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The data processing capability of existing remote sensing system has not kept pace with the amount of data typically received and need to be processed. Existing product services are not capable of providing users with a variety of remote sensing data sources for selection, either. Therefore, in this paper, we present a product generation programme using multisource remote sensing data, across distributed data centers in a cloud environment, so as to compensate for the low productive efficiency, less types and simple services of the existing system. The programme adopts "master-slave" architecture. Specifically, the master center is mainly responsible for the production order receiving and parsing, as well as task and data scheduling, results feedback, and so on; the slave centers are the distributed remote sensing data centers, which storage one or more types of remote sensing data, and mainly responsible for production task execution. In general, each production task only runs on one data center, and the data scheduling among centers adopts a "minimum data transferring" strategy. The logical workflow of each production task is organized based on knowledge base, and then turned into the actual executed workflow by Kepler. In addition, $ Fully documented templates are available in the elsarticle package on CTAN.

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Analysis of performance measures in cloud-based ubiquitous SaaS CRM project systems

Chen

Chu

et al. 2017

J Supercomput

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On-demand service hosting on production grid infrastructures

Cited by 3 publications

References 2 publications

Mobile cloud-based depression diagnosis using an ontology and a Bayesian network

Mobile cloud-based depression diagnosis using an ontology and a Bayesian network

A cloud-based remote sensing data production system

Analysis of performance measures in cloud-based ubiquitous SaaS CRM project systems

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