An Architectural Schema for Performance Prediction using Machine Learning in the Fog-to-Cloud Paradigm
| Autor: | Xavi Masip-Bruin, Jordi Garcia, Andres Prieto-Gonzalez, Souvik Sengupta |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CRAAX - Centre de Recerca d'Arquitectures Avançades de Xarxes |
| Rok vydání: | 2019 |
| Předmět: |
Internet of things
Informàtica::Intel·ligència artificial::Aprenentatge automàtic [Àrees temàtiques de la UPC] Computació en núvol Internet de les coses Fog-to-Cloud (F2C)M Computer science Performance prediction media_common.quotation_subject Cloud computing Performance forecasting 02 engineering and technology Machine learning computer.software_genre Domain (software engineering) 0202 electrical engineering electronic engineering information engineering Resource management Function (engineering) Informàtica::Arquitectura de computadors [Àrees temàtiques de la UPC] media_common business.industry 020206 networking & telecommunications 020207 software engineering Core (game theory) Architecture framework Resource allocation Artificial intelligence business computer Internet-of-Things (IoT) |
| Zdroj: | UEMCON UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| DOI: | 10.5281/zenodo.3562035 |
| Popis: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Fog-to-Cloud (F2C) paradigm is emerging to both provide higher functional efficiency for latency-sensitive services and also help modern computing systems to be more intelligent. As it is still in its infancy, the biggest challenge for this domain is to build a proper resource allocation technique as part of an efficient resource management module. The diversified and distributed nature of that paradigm creates some additional hurdles for choosing the appropriate resources for executing some tasks. Significantly, efficient resource consumption estimation and performance forecasting are core issues in the design and development of a proper and smart resource management mechanism for F2C systems. Considering this fact, in this paper, we aim at designing an architectural framework for a prediction-based resource management mechanism for F2C systems. The performance prediction is based on supervised machine learning technology. The proposal has been evaluated and validated by predicting the performance and resources usage of F2C resources through several tests. Primarily, we have run an image recognition application on different F2C resources and collected performance-related information and resource consumption information. Then, by adopting the multivariate regression methodology, we perform some standard machine learning techniques to predict the performance and estimate the resource consumption of the F2C resources. Finally, to justify the effectiveness of our proposal, we calculated the value of a cost function between estimated values and the real measured values. This work has been supported by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (FEDER) under contract RTI2018- 094532-B-I00, and by the H2020 European Union mF2C project with reference 730929. |
| Databáze: | OpenAIRE |
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