Szczegóły publikacji
Opis bibliograficzny
Adaptive context-aware energy optimization for services on mobile devices with use of machine learning considering security aspects / Piotr NAWROCKI, Bartłomiej ŚNIEŻYŃSKI, Joanna Kołodziej, Paweł Szynkiewicz // W: CCGrid 2020 [Dokument elektroniczny] : 20th IEEE/ACM international symposium on Cluster, Cloud and Internet Computing : 11–14 May 2020, Melbourne, Australia : proceedings / eds. Laurent Lefevre, [et al.]. — Wersja do Windows. — Dane tekstowe. — Los Alamitos, Washington, Tokyo : IEEE, cop. 2020. — e-ISBN: 978-1-7281-6095-5. — S. 708–717. — Wymagania systemowe: Adobe Reader. — Bibliogr s. 717, Abstr.
Autorzy (4)
- AGHNawrocki Piotr
- AGHŚnieżyński Bartłomiej
- Kołodziej Joanna
- Szynkiewicz Paweł
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 129478 |
|---|---|
| Data dodania do BaDAP | 2020-07-31 |
| Tekst źródłowy | URL |
| DOI | 10.1109/CCGrid49817.2020.00-19 |
| Rok publikacji | 2020 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing |
Abstract
In this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the device and the cloud, which is especially important in mobile systems. Decisions are made taking the context into account (e.g. network connection type, location, potential time and cost of executing the application or service). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that due to the experience gathered and the learning process performed, the decision module has consequently become more efficient in assigning the task to either the mobile device or cloud resources. In face of presented improvements, the security issues inherent within the context of mobile application and cloud computing are further discussed. As threats associated with mobile data offloading are a serious concern, often preventing the utilization of cloud services, we propose a more security focused approach for our solution, preferably without hindering the performance. © 2020 IEEE.