Cloud Computing

Performance interference experiments produced by Storiks 0.4 on the following storage devices:

  • Samsung 980PRO 250GB
  • Samsung 970EVO Plus 250GB
  • Samsung 970EVO 500GB

 

 

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The files contain the raw data and corresponding analysis for the related IEEE paper

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This work aims to identify anomalous patterns that could be associated with performance degradation and failures in datacenter nodes, such as Virtual Machines or Virtual Machines clusters. The early detection of anomalies can enable early remediation measures, such as Virtual Machines migration and resource reallocation before losses occur. One way to detect anomalous patterns in datacenter nodes is using monitoring data from the nodes, such as CPU and memory utilization.

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This ZIP contains the raw data and performance analysis for the related IEEE TDPS paper

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Please cite the following paper when using this dataset:

N. Thakur and C.Y. Han, “An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection,” Journal of COVID, 2022, Volume 5, Issue 3, pp. 1026-1049

Abstract

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Emulating a RT task and measuring the response latency of its thread by means of the high-resolution testing tool Cyclictest. The thread was clocked at 10ms, and a FIFO scheduling policy was used, with the thread being assigned the highest priority. Measurements were performed in distinct testing environments, some of which had best effort concurrent threads competing for the machine resources. For this purpose, the workload generator tool stress was used.

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This dataset is used to illustrate an application of the "klm-based profiling and preventing security attack (klm-PPSA)" system. The klm-PPSA system is developed to profile, detect, and then prevent known and/or unknown security attacks before a user access a cloud. This dataset was created based on “a.patrik” user logical attempts scenarios when accessing his cloud resources and/or services. You will find attached the CSV file associated with the resulted dataset. The dataset contains 460 records of 13 attributes (independent and dependent variables).

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Deployment of serverless functions depends on various factors. This dataset presents deployment time of a Python serverless function with various deployment package size, deployed on 6 regions of AWS and 6 regions of IBM. Deployment scripts are executed from Innsbruck, Austria.

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