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L. Iacono, J.L. Vazquez-Poletti, C. García Garino, I.M. Llorente. A Model to Calculate Amazon EC2 Instance Performance in Frost Prediction Applications. In 1st HPCLATAM-CLCAR Joint Conference (CARLA2014), Volume 485, Pages 68-82, 2014.


Frosts are one of the main causes of economic losses in the Province of Mendoza, Argentina. Although it is a phenomenon that hap- pens every year, frosts can be predicted using Agricultural Monitoring Systems (AMS). AMS provide information to start and stop frosts de- fense systems and thus reduce economic losses. In recent years, the emer- gence of infrastructures called Sensor Clouds improved AMS in several aspects such as, scalability, reliability, fault tolerance, etc. Sensor Clouds use Wireless Sensor Networks (WSN) to collect data in the ?eld and Cloud Computing to store and process these data. Currently, Cloud providers like Amazon o?er di?erent instances to store and process data in a pro?table way. Moreover, due to the variety of o?ered instances arises the need for tools to determine which is the most appropriate in- stance type, in terms of execution time and economic costs, for running frost prediction applications. In this paper we present a model targeted to estimate the execution time and economic cost of Amazon EC2 in- stances for frosts prediction applications. The main contribution of this work is to provide a model that allows users to select the most suitable instance for frost prediction applications


[ Tin2012-31518 ] [ Cloud ]


Jose Luis Vazquez-Poletti
Ignacio M. Llorente

BibTex Reference

   Author = {Iacono, L. and Vazquez-Poletti, J.L. and García Garino, C. and Llorente, I.M.},
   Title = {A Model to Calculate Amazon EC2 Instance Performance in Frost Prediction Applications},
   BookTitle = {1st HPCLATAM-CLCAR Joint Conference (CARLA2014)},
   Volume = {485},
   Pages = {68--82},
   Publisher = {Communications in Computer and Information Science (CCIS) of Springer},
   Year = {2014}

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