Seamless computation offloading for mobile applications using an online learning algorithm
Yazarlar (2)
Doç. Dr. Mahir KAYA
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Doç. Dr. Yasemin ÇETİN KAYA
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | COMPUTING |
| Dergi ISSN | 0010-485X Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 05-2021 |
| Cilt No | 103 |
| Sayı | 5 |
| Sayfalar | 771 / 799 |
| DOI Numarası | 10.1007/s00607-020-00873-y |
| Makale Linki | http://dx.doi.org/10.1007/s00607-020-00873-y |
| Özet |
| Although recent developments in the hardware of mobile devices, such as processor and memory capacity have increased their capabilities, they are still not comparable to cloud servers. The capacity constraints of mobile devices can be overcome by having the computing intensive work of mobile applications performed on powerful local or cloud servers. One of the important aspects of computation offloading is the decision process; this is determined by the costs of running the computation intensive components at run time on the server or at the local. This study proposes a novel hybrid model. An object dependency graph was created by gathering data from the mobile device at run time. This graph was partitioned with a novel model to determine the offloadable parts, which were then sent to the server using an online learning algorithm. Mobile applications were implemented on Android OS to verify the … |
| Anahtar Kelimeler |
| Code offloading | Mobile cloud computing | Machine learning | Mobile applications | Application partitioning | Graph-based model |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 7 |
| Google Scholar | 12 |