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6 results for “Entice”
ENTICE download speed measurements
<p>A sample of a measurements dataset. Monitoring of a general purpose Cloud storages (e.g. such as AWS S3) is a part of ENTICE Pareto-SLA component. This dataset has been obtained by performing downloads of objects with random data of various sizes, ranging from 1 kB to 1 GB. Each file object has been generated by dd tool: dd if=/dev/urandom of=rand-1M.bin bs=1M count=1. On the server side a Minio S3 server has been setup with a Nginx gateway. The client who performed downloads was residing in the same local 1 Gbps ethernet network as the server. On the server side, network QoS has been controlled through a Linux tc netem tool, emulating various QoS conditions for the packet delay, jitter and packet loss.</p> <p>The dataset contains the following headers:</p> <p>Timestamp - the timestamp of a measurement performed.</p> <p>Client IP - the IP address of a client - anonymised.</p> <p>Server IP - the IP address of a server - anonymised.</p> <p>QoS - min|avg|max|stdev|ploss denoting minimum, average and maximum packet round-trip-time (RTT) between the server and the client, respectively, followed by the standard deviation of the RTT and emulated packet loss with the tc tool, expressed as a percentage.</p> <p>Object size [B] - the size of the file object in bytes.</p> <p>Download time [s] - the download time of the file object from the client perspective.</p> <p>Download speed [B/s] - the ratio (Object size[B]) / (Download time[s]).</p>
Entice Optimisation Data for FlexiOPS Use Case
<p>The optimisation data for the project Entice shows the metrics gathered by FlexiOps in their use case. These measurements were taken in the Flexiant Cloud Orchestrator platform and shows how the Entice software optimises virtual machine images and reduces them considerably in size.</p>
ENTICE VM image analysis and optimised fragmentation frequently built images dataset
<p>As part of the evaluation of ENTICE VM image analysis and optimised fragmentation services we have implemented a simulation environment which analyses online software package repositories (e.g. ones offered by the maintainers of the Ubuntu and Debian Linux distributions) and deduces decomposition options as well as expected fragment sizes based on metadata acquired from these repositories. This dataset contains the collected recipes for several frequently built Ubuntu Linux based VMIs (e.g., LAMP, LAPP, LEMP, LLMP, LYME, MEAN/MERN, LTM, etc.) and the calculated fragments and their relations. The dataset is used to analyse and evaluate the behaviour of the fragmentation services. The dataset is in compressed LRZIP format.</p>
ENTICE Multi-objective optimization framework synthetic evaluation data-sets
<p>This dataset contains synthetic usage data for evaluation of the multi-objective redistribution framework for distributed VMI repositories. </p> <p>The data is stored in a Java object and it should be directly loaded. </p> <p> </p>
Project ENTICE Ontology M18
<p>The entire ontology including the testing data was evaluated by first using the HermiT validation reasoner and later the Pellet reasoned too. The focus here is to adequately incorporate the data models in the ontology. The syntax and the standards are the same as in the previous version of the ontology, but the table representation is updated. By doing this, we want to strictly split the data properties (attributes) and the object properties (relational data) to induce a higher expressivity level of the ontology. We devoted special attention to the selection of types in data properties aiming to use only fully supported data types in Resource Description Framework (RDF) and Web Ontology Language (OWL).</p>
Objectives for CSP pilots of WT for ENTICE workbench
<p>Data set for the objectives of ENTICE with WT pilots</p>
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