![]() For all services, it is of paramount importance to accurately select the data center to where deploy the applications, with throughput that varies by factors from 2x to 10x. According to collected results, all services show eventual weaknesses related to some workload, with no all-round eligible winner, e.g., some offers providing excellent or poor performance when exchanging large or small files. At last, we complement the analysis by comparing the actual and forecast costs faced when using each service. Each service is analysed as a black box and benchmarked through crafted workloads.We take the perspective of a customer located in Europe, looking for possible service providers and the optimal data center where to deploy its applications. To shed light on storage services from the customer perspective, we propose a benchmarking methodology, apply it to four popular offers (Amazon S3, Amazon Glacier, Windows Azure Blob and Rackspace Cloud Files), and compare their performance. However it is rather difficult to access information about each service architecture, performance, and pricing. Several solutions are available on the market, the most famous being Amazon S3. Cloud storage services are emerging as strong alternative to local storage, allowing customers to save costs of buying and maintaining expensive hardware. The obtained findings showed clearly the importance of this new operator in the context of IoT.ĭata storage is one of today's fundamental services with companies, universities and research centers having the need of storing large amounts of data every day. The researchers validate their approach based on GIPSIT operator by implementing a realistic eHealth based-scenario in a Smart Home. To this end, a new semantics operator which the authors call a gateway for intelligent process scheduling of IoT (GIPSIT) is proposed as a semantic gateway enabling the management of data flows circulating between the connected objects of IoT. Business process management (BPM) is the most adapted way to carry out data management thanks to workflow processes. ![]() The cooperation between these objects is a promising solution to meet this challenge. In this context, one of the main problems is how to schedule the data flow circulating between objects, and between objects applications. The new concept of the Internet of Things (IoT) is bringing new forms of knowledge and applications that rely on smart objects able to sense and process the collected data on a remote workflow server in the perspective to generate automated decisions.
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