2013 5th International Conference on Knowledge and Smart Technology (KST) 2013
DOI: 10.1109/kst.2013.6512785
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Light-weight operation of a failover system for Cloud computing

Abstract: Availability is one of the biggest challenges which slow down the adoption of Cloud computing in the IT industry. A failover system can be designed to use as a backup in case of Cloud failure or unavailability. In this paper, we introduce the method for designing a local failover system for Cloud. Since a failover system has less resource and limited scalability; it cannot handle all workloads previously on the Cloud. We overcome this drawback by adapting the full operation performed in the Cloud into a light-… Show more

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Cited by 4 publications
(2 citation statements)
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“…Afterward, ConvNet was proven to be more accurate than a handcraft feature with traditional machine learning. On the dark side, ConvNet was used to train the spamming bots to recognize those reCaptcha images and break the human verification (Mookdarsanit & Mookdarsanit, 2020a) against authentication (Soimart & Mookdarsanit, 2016b) mechanism that finally made the sever-side system processed a large number of junk jobs as the concurrent workloads (Mookdarsanit & Gertphol, 2013). ConvNet was designed to have highly optimized structures (Mookdarsanit & Mookdarsanit, 2019a) to learn the extraction and abstraction of 2D features.…”
Section: Convolutional Neural Network (Convnet)mentioning
confidence: 99%
“…Afterward, ConvNet was proven to be more accurate than a handcraft feature with traditional machine learning. On the dark side, ConvNet was used to train the spamming bots to recognize those reCaptcha images and break the human verification (Mookdarsanit & Mookdarsanit, 2020a) against authentication (Soimart & Mookdarsanit, 2016b) mechanism that finally made the sever-side system processed a large number of junk jobs as the concurrent workloads (Mookdarsanit & Gertphol, 2013). ConvNet was designed to have highly optimized structures (Mookdarsanit & Mookdarsanit, 2019a) to learn the extraction and abstraction of 2D features.…”
Section: Convolutional Neural Network (Convnet)mentioning
confidence: 99%
“…Customarily, the purpose of "MapReduce approach" [51][52][53] was designed for the actual "dynamic structure" for the "velocity", "volume" and "variety" of non-volatile large-scale datasets (or Big data [1]). In case of a geo-untagged photo, MapReduce indexing filters only the useful geo-tagged photos (that were collected in term of vectors with geo-tagging) from the set of large-scale samples which are similar to some visual contents of geo-untagged photo.…”
Section: Mapreduce Indexingmentioning
confidence: 99%