Cloud Computing
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 task-level CPU data for the streaming engines Apache Flink, Apache Spark Structured Streaming and Apache Spark Continous Processing.
This data was collected via our task-level performance benchmark: https://github.com/rankj/YSB-task-level
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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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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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This data set was generated and used in determining the workability of a homemade Intelligent IoT Weather Station Using an Embedded System.
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This file provides the main descriptive information about this services dependency graphs dataset to model web services compositions.
This dataset represents the services dependency graphs (SDGs) generated by our developed Mutual Information-based Services Dependency (MISD) model for 4 public services datasets.
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This dataset is collected from a running Decentralized Application (DApp). When the DApp receives transaction requests stably, we add system pressures with stress-ng, such as I/O pressure to inject anomalies manually. We increase disk pressure for 20 minutes every hour. We keep monitoring the DApp for 12 hours and collect data every 15 seconds, resulting in 3237 samples and 229 resource-related metrics for our experiments. In addition, an important metric that represents the number of transaction failures can be seen as the anomaly indicator of the DApp.
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