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4 results for “Auto-scaling”
Archival bundle of the data used for "Predictive Auto-scaling with OpenStack Monasca" (UCC 2021)
<p>This archive contains the data used for the paper</p> <p><strong>Predictive Auto-scaling with OpenStack Monasca</strong><br> <a href="mailto:giacomo.lanciano@sns.it">Giacomo Lanciano</a>*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella<br> 2021 IEEE/ACM 14th International Conference on Utility and Cloud Computing (UCC)<br> <a href="https://doi.org/10.1145/3468737.3494104">10.1145/3468737.3494104</a></p> <p>Follow the instructions provided in the <a href="https://github.com/giacomolanciano/UCC2021-predictive-auto-scaling-openstack">companion repo</a> to automatically download and decompress the archive. The following files are included:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> <p>amphora-x64-haproxy.qcow2</p> </td> <td> <p>Image used to create Octavia amphorae</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>.log</p> </td> <td> <p>distwalk run log</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-pred.json</p> </td> <td> <p>Predictive metric data exported from Monasca DB</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-real.json</p> </td> <td> <p>Actual metric data exported from Monasca DB</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-times.csv</p> </td> <td> <p>Client-side response time for each request sent during a run</p> </td> </tr> <tr> <td> <p>model_dumps/*</p> </td> <td> <p>Dumps of the models and data scalers used for the validation</p> </td> </tr> <tr> <td> <p>predictor.log</p> </td> <td> <p>monasca-predictor log</p> </td> </tr> <tr> <td> <p>predictor-times.log</p> </td> <td> <p>monasca-predictor` log (timing info only)</p> </td> </tr> <tr> <td> <p>predictor-times-{lin,mlp,rnn}.{csv,log}</p> </td> <td> <p>monasca-predictor log (timing info only, group by predictor)</p> </td> </tr> <tr> <td> <p>super_steep_behavior.csv</p> </td> <td> <p>Dataset used to train MLP and RNN models</p> </td> </tr> <tr> <td> <p>test_behavior_02_distwalk-6t_last100.dat</p> </td> <td> <p>distwalk load trace</p> </td> </tr> <tr> <td> <p>ubuntu-20.04-min-distwalk.img</p> </td> <td> <p>Image used to create Nova instances for the scaling group</p> </td> </tr> </tbody> </table> <p>* <em>contact author</em></p>
Energy consumption, execution time and fail requests rate of a proactive energy-aware auto-scaling solution for edge-based infrastructures applied to real-world workload.
<p>Spreadsheet of the results obtained with our horizontal auto-scaling proposal presented in "A proactive energy-aware auto-scaling solution for edge-based infrastructures". In that research, we present a proactive horizontal auto-scaling framework for edge infrastructures, which considers both the base (idle) and dynamic (due to application execution) energy consumption of edge nodes and the node scaling mechanism. Simulations were performed with the EdgeCloudSim simulator with a workload provided by Shanghai Telecom and the results show up to a 92.5% decrease in energy consumption, a failed request rate of up to 0%, and reasonable execution times of the auto-scaling process for different problem sizes.</p> <p>Proactive auto-scaling mechanisms in edge-based infrastructures can anticipate user service requests by allocating computing resources while supporting the quality of service needed by a vast range of applications requiring, e.g., a low latency or response time. </p> <p>This work is supported by the European Union's H2020 research and innovation program under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 (MCI/AEI) and RHEA P18-FR-1081.</p>
Auto-scaling dataset based on the gym-hpa framework
<p>The implemented gym-hpa is a custom <a href="https://gym.openai.com/">OpenAi Gym</a> environment for the training of Reinforcement Learning (RL) agents for auto-scaling research in the Kubernetes (K8s) platform.</p> <p>Two environments exist based on the <a href="https://github.com/bitnami/charts/tree/master/bitnami/redis-cluster">Redis Cluster</a> and <a href="https://github.com/GoogleCloudPlatform/microservices-demo">Online Boutique</a> applications.</p> <p>Two collected datasets are shared here. The code has been released here: https://github.com/jpedro1992/gym-hpa</p> <p>Related Publication: Santos, J. et al. "gym-hpa: Efficient auto-scaling via reinforcement learning for complex microservice-based applications in Kubernetes." <em>NOMS2023, the IEEE/IFIP Network Operations and Management Symposium</em>. 2023.</p>
Application Deployment using Containers with Auto-scaling for Microservices in Cloud Environment
<p>This dataset release supports the results presented in the paper "Application Deployment using Containers with Auto-scaling for Microservices in Cloud Environment" by S. N. Srirama, M. Adhikari, and S. Paul, accepted in Journal of Network and Computer Applications, ISSN: 1084-8045, vol. 160, pp. 1-20, 2020. Elsevier.</p> <p>When referring to the dataset please cite the paper above.</p>
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International Brain Laboratory public data
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OpenNeuro
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