Effects of the "spawn start" of the Monte Carlo serverless function that simulates Pi.
The functions are orchestrated as a workflow and executed with the xAFCL enactment engine (https://doi.org/10.1109/TSC.2021.3128137) on three regions (US, EU, Asia) of three cloud providers AWS Lambda, Google Cloud Functions, and IBM Cloud Functions.
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Logs from running Monte Carlo simulation as serverless functions on Frankfurt, North Virginia, Tokyo regions of four FaaS systems (AWS, Google, IBM, Alibaba).
Each execution is repeated 5 times (all are warm start).
The conducted analysis is a part of a submitted manuscript to IEEE TSC.
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This dataset is a result of my research production into machine learning in android security. The data was obtained by a process that consisted to map a binary vector of permissions used for each application analyzed {1=used, 0=no used}. Moreover, the samples of malware/benign were devided by "Type"; 1 malware and 0 non-malware.
When I did my research, the datasets of malware and benign Android applications were not available, then I give to the community a part of my research results for the future works.
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