Cloud Computing

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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This dataset is an experimental result of the paper “Performance Evaluation for Geographically Distributed Blockchain-based Services in a Cloud Computing Environment”. The Geographically Distributed Cloud Performance Evaluation Ambassador (GDCPEA) is deployed on each Go Ethereum (Geth) node to measure the elapsed time from the start to the end of the Geth main operations.

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It is now widely known fact that the Cloud computing and Software defined network paradigms have received a wide acceptance from researchers, academia and the industry. But the wider acceptance of cloud computing and SDN paradigms are hampered by increasing security threats. One of the several facts is that the advancements in processing facilities currently available are implicitly helping the attackers to attack in various directions. For example, it is visible that the conventional DoS attacks are now extended to cloud environments as DDoS attacks.

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1849 Views

With the modern day technological advancements and the evolution of Industry 4.0, it is very important to make sure that the problem of Intrusion detection in Cloud , IoT and other modern networking environments is addressed as an immediate concern. It is a fact that Cloud and Cyber Physical Systems are the basis for Industry 4.0. Thus, intrusion detection in cyber physical systems plays a crucial role in Industry 4.0. Here, we provide the an intrusion detection dataset for performance evaluation of machine learning and deep learning based intrusion detection systems.

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This dataset contains measurements of TPC-C benchmark executions in MySQL server deployed in Google Cloud Platform.

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Cloud forensics is different than digital forensics because of the architectural implementation of the cloud. In an Infrastructure as a Service (IaaS) cloud model. Virtual Machines (VM) deployed over the cloud can be used by adversaries to carry out a cyber-attack using the cloud as an environment.

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