A context-aware and self-adaptive offloading decision support model for mobile cloud computing system

2018 ◽  
Vol 9 (5) ◽  
pp. 1561-1572 ◽  
Author(s):  
Flávio Akira Nakahara ◽  
Delano Medeiros Beder
Keyword(s):  
Cloud Computing ◽  
Decision Support ◽  
Mobile Cloud Computing ◽  
Computing System ◽  
Mobile Cloud ◽  
Context Aware ◽  
Decision Support Model ◽  
Cloud Computing System ◽  
Support Model ◽  
Self Adaptive

scholarly journals Experimental Framework for Mobile Cloud Computing System

2015 ◽  
Vol 52 ◽  
pp. 1147-1152 ◽  
Author(s):  
Muhannad Quwaider ◽  
Yaser Jararweh ◽  
Mahmoud Al-Alyyoub ◽  
Rehab Duwairi
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Computing System ◽  
Mobile Cloud ◽  
Cloud Computing System

scholarly journals ERMO2 algorithm: an energy efficient mobility management in mobile cloud computing system for 5G heterogeneous networks

2019 ◽  
Vol 9 (3) ◽  
pp. 1957
Author(s):  
L. Pallavi ◽  
A. Jagan ◽  
B. Thirumala Rao
Keyword(s):  
Cloud Computing ◽  
Heterogeneous Networks ◽  
Mobility Management ◽  
Packet Loss ◽  
Energy Efficient ◽  
Mobile Cloud Computing ◽  
Computing System ◽  
Mobile Users ◽  
Mobile Cloud ◽  
Cloud Computing System

Recently, mobile devices are becoming the primary platforms for every user who always roam around and access the cloud computing applications. Mobile cloud computing (MCC) combines the both mobile and cloud computing, which provides optimal services to the mobile users. In next-generation mobile environments, mainly due to the huge number of mobile users in conjunction with the small cell size and their portable information‟s, the influence of mobility on the network performance is strengthened. In this paper, we propose an energy efficient mobility management in mobile cloud computing (E2M2MC2) system for 5G heterogeneous networks. The proposed E2M2MC2 system use elective repeat multi-objective optimization (ERMO2) algorithm to determine the best clouds based on the selection metrics are delay, jitter, bit error rate (BER), packet loss, communication cost, response time, and network load. ERMO2 algorithm provides energy efficient management of user mobility as well as network resources. The simulation results shows that the proposed E2M2MC2 system helps in minimizing delay, packet loss rate and energy consumption in a heterogeneous network.

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