Energy Aware Mobile Cloud Computing Algorithm for Android Smartphones

2017 ◽  
pp. 621-631
Author(s):  
Samar A. Said ◽  
Sameh A. Salem ◽  
Samir G. Sayed
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Energy Aware ◽  
Computing Algorithm

Energy-Aware Issues for Handling Big Data in Mobile Cloud Computing

2017 ◽  
pp. 233-256
Author(s):  
Chhabi Rani Panigrahi ◽  
Rajesh Kumar Verma ◽  
Joy Lal Sarkar ◽  
Bibudhendu Pati
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Energy Aware

scholarly journals Mobile Cloud Computing: Taxonomy and Challenges

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Ahmed Aliyu ◽  
Abdul Hanan Abdullah ◽  
Omprakash Kaiwartya ◽  
Syed Hamid Hussain Madni ◽  
Usman Mohammed Joda ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Cost Effective ◽  
Qualitative Assessment ◽  
The Internet ◽  
Mobile Cloud ◽  
Energy Aware ◽  
Research Issues ◽  
Delivery Platform ◽  
Cloud Users

Mobile cloud computing (MCC) holds a new dawn of computing, where the cloud users are attracted to multiple services through the Internet. MCC has a qualitative, flexible, and cost-effective delivery platform for providing services to mobile cloud users with the aid of the Internet. Due to the advantage of the delivery platform, several studies have been conducted on how to address different issues in MCC. The issues include energy efficiency in MCC, secured MCC, user-satisfied applications and Quality of Service-aware MCC (QoS). In this context, this paper qualitatively reviews different proposed MCC solutions. Therefore, taxonomy for MCC is presented considering major themes of research including energy-aware, security, applications, and QoS-aware developments. Each of these themes is critically investigated with comparative assessments considering recent advancements. Analysis of metrics and implementation environments used for evaluating the performance of existing techniques are presented. Finally, some open research issues and future challenges are identified based on the critical and qualitative assessment of literature for researchers in this field.

Energy-aware task scheduling in mobile cloud computing

2018 ◽  
Vol 36 (3) ◽  
pp. 529-553 ◽  
Author(s):  
Chaogang Tang ◽  
Mingyang Hao ◽  
Xianglin Wei ◽  
Wei Chen
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Energy Aware

scholarly journals Energy Aware Task Assignment with Cost Optimization in Mobile Cloud Computing

2018 ◽  
Vol 11 (08) ◽  
pp. 175-185 ◽  
Author(s):  
Irfan Ali Jamali ◽  
Abdullah Lakhan ◽  
Abdul Rasheed Mahesar ◽  
Dileep Kumar Sajnani
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Cost Optimization ◽  
Task Assignment ◽  
Mobile Cloud ◽  
Energy Aware

Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing

2016 ◽  
Vol 59 ◽  
pp. 46-54 ◽  
Author(s):  
Keke Gai ◽  
Meikang Qiu ◽  
Hui Zhao ◽  
Lixin Tao ◽  
Ziliang Zong
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Green Computing ◽  
Mobile Cloud ◽  
Energy Aware ◽  
Computing Model

scholarly journals MeReg: Managing Energy-SLA Tradeoff for Green Mobile Cloud Computing

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Rahul Yadav ◽  
Weizhe Zhang
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Large Scale ◽  
Data Centers ◽  
Mobile Cloud Computing ◽  
Virtual Machines ◽  
Mobile Cloud ◽  
Energy Aware ◽  
Cpu Utilization ◽  
Selection Algorithms

Mobile cloud computing (MCC) provides various cloud computing services to mobile users. The rapid growth of MCC users requires large-scale MCC data centers to provide them with data processing and storage services. The growth of these data centers directly impacts electrical energy consumption, which affects businesses as well as the environment through carbon dioxide (CO2) emissions. Moreover, large amount of energy is wasted to maintain the servers running during low workload. To reduce the energy consumption of mobile cloud data centers, energy-aware host overload detection algorithm and virtual machines (VMs) selection algorithms for VM consolidation are required during detected host underload and overload. After allocating resources to all VMs, underloaded hosts are required to assume energy-saving mode in order to minimize power consumption. To address this issue, we proposed an adaptive heuristics energy-aware algorithm, which creates an upper CPU utilization threshold using recent CPU utilization history to detect overloaded hosts and dynamic VM selection algorithms to consolidate the VMs from overloaded or underloaded host. The goal is to minimize total energy consumption and maximize Quality of Service, including the reduction of service level agreement (SLA) violations. CloudSim simulator is used to validate the algorithm and simulations are conducted on real workload traces in 10 different days, as provided by PlanetLab.

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