scholarly journals Virtual Machine Selection Algorithm Based on User Requirements in Mobile Cloud Computing Environment

2018 ◽  
Vol 17 (2) ◽  
pp. 7335-7349
Author(s):  
Rashid Alakbarov
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Mobile Cloud Computing ◽  
Virtual Machines ◽  
Mobile User ◽  
Base Stations ◽  
Mobile Cloud ◽  
Software Applications ◽  
Wireless Metropolitan Area Networks ◽  
Cloud Technologies

The article analyzes the advantages of mobile cloud technologies and problems emerging during the use of those. The network infrastructure created based on cloudlets at the second level of mobile cloud computing with hierarchical structure is analyzed. At the same time, the article explores the issues of satisfaction of demand of mobile equipment for computing and memory resources by using these technologies. The article presents one solution for the allocation of mobile user requests in virtual machines created in cloudlets located near base stations of wireless metropolitan area networks (WMAN) in a balanced way by considering the technical capacity of those. Alongside, the article considers the solution of user problem during designated time and the issue of determining virtual machines satisfying other requirements. For this purpose, different characteristics of the stated problem, virtual machines, as well as communication channels between a user and virtual machines are considered. By using possible values determining the importance of cloudlets, conditions for loading software applications of a user to a virtual machine are explored and an appropriate method is proposed.

A study on virtual machine deployment for application outsourcing in mobile cloud computing

2012 ◽  
Vol 63 (3) ◽  
pp. 946-964 ◽  
Author(s):  
Muhammad Shiraz ◽  
Saeid Abolfazli ◽  
Zohreh Sanaei ◽  
Abdullah Gani
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Mobile Cloud Computing ◽  
Mobile Cloud

scholarly journals A Comparison of Cloud Execution Mechanisms Fog, Edge, and Clone Cloud Computing

2018 ◽  
Vol 8 (6) ◽  
pp. 4646
Author(s):  
T. Francis
Keyword(s):  
Cloud Computing ◽  
Comparative Study ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Fog Computing ◽  
Mobile Technologies ◽  
Smart Devices ◽  
Mobile Cloud ◽  
Attractive Feature ◽  
Cloud Technologies

Cloud computing is a technology that was developed a decade ago to provide uninterrupted, scalable services to users and organizations. Cloud computing has also become an attractive feature for mobile users due to the limited features of mobile devices. The combination of cloud technologies with mobile technologies resulted in a new area of computing called mobile cloud computing. This combined technology is used to augment the resources existing in Smart devices. In recent times, Fog computing, Edge computing, and Clone Cloud computing techniques have become the latest trends after mobile cloud computing, which have all been developed to address the limitations in cloud computing. This paper reviews these recent technologies in detail and provides a comparative study of them. It also addresses the differences in these technologies and how each of them is effective for organizations and developers.

Explore virtual machine deployment to mobile cloud computing for multi-tenancy and energy conservation in wireless network

2017 ◽  
Vol 20 (4) ◽  
pp. 3263-3274 ◽  
Author(s):  
Yan Ding ◽  
Gaochao Xu ◽  
Chunyi Wu ◽  
Liang Hu ◽  
Yunan Zhai ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Conservation ◽  
Wireless Network ◽  
Virtual Machine ◽  
Mobile Cloud Computing ◽  
Mobile Cloud

Cloud resource optimization based on poisson linear deep gradient learning for mobile cloud computing

2021 ◽  
Vol 40 (1) ◽  
pp. 787-797
Author(s):  
G. Saravanan ◽  
N. Yuvaraj
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Service Providers ◽  
Mobile User ◽  
Mobile Users ◽  
Mobile Cloud ◽  
Queuing Model ◽  
Gradient Learning

Mobile Cloud Computing (MCC) addresses the drawbacks of Mobile Users (MU) where the in-depth evaluation of mobile applications is transferred to a centralized cloud via a wireless medium to reduce load, therefore optimizing resources. In this paper, we consider the resource (i.e., bandwidth and memory) allocation problem to support mobile applications in a MCC environment. In such an environment, Mobile Cloud Service Providers (MCSPs) form a coalition to create a resource pool to share their resources with the Mobile Cloud Users. To enhance the welfare of the MCSPs, a method for optimal resource allocation to the mobile users called, Poisson Linear Deep Resource Allocation (PL-DRA) is designed. For resource allocation between mobile users, we formulate and solve optimization models to acquire an optimal number of application instances while meeting the requirements of mobile users. For optimal application instances, the Poisson Distributed Queuing model is designed. The distributed resource management is designed as a multithreaded model where parallel computation is provided. Next, a Linear Gradient Deep Resource Allocation (LG-DRA) model is designed based on the constraints, bandwidth, and memory to allocate mobile user instances. This model combines the advantage of both decision making (i.e. Linear Programming) and perception ability (i.e. Deep Resource Allocation). Besides, a Stochastic Gradient Learning is utilized to address mobile user scalability. The simulation results show that the Poisson queuing strategy based on the improved Deep Learning algorithm has better performance in response time, response overhead, and energy consumption than other algorithms.

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.

A provable and secure mobile user authentication scheme for mobile cloud computing services

2019 ◽  
Vol 32 (14) ◽  
pp. e3980 ◽  
Author(s):  
Azeem Irshad ◽  
Shehzad Ashraf Chaudhry ◽  
Muhammad Shafiq ◽  
Muhammad Usman ◽  
Muhammad Asif ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
User Authentication ◽  
Mobile User ◽  
Authentication Scheme ◽  
Mobile Cloud ◽  
Computing Services ◽  
Cloud Computing Services ◽  
User Authentication Scheme

Performance analysis of Mobile Cloud Computing Architectures for mHealth app

2020 ◽  
Vol 2020 (3) ◽  
pp. 335-1-335-7
Author(s):  
D. Inupakutika ◽  
D. Akopian ◽  
P. Chalela ◽  
A. G. Ramirez
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Mobile User ◽  
Battery Life ◽  
Mobile Cloud ◽  
Medical Monitoring ◽  
Power And Energy ◽  
Analysis Of Performance

Mobile Health (mHealth) applications (apps) are being widely used to monitor health of patients with chronic medical conditions with the proliferation and the increasing use of smartphones. Mobile devices have limited computation power and energy supply which may lead to either delayed alarms, shorter battery life or excessive memory usage limiting their ability to execute resource-intensive functionality and inhibit proper medical monitoring. These limitations can be overcome by the integration of mobile and cloud computing (Mobile Cloud Computing (MCC)) that expands mobile devices' capabilities. With the advent of different MCC architectures such as implementation of mobile user-side tools or network-side architectures it is hence important to decide a suitable architecture for mHealth apps. We survey MCC architectures and present a comparative analysis of performance against a resource demanding representative testing scenario in a prototype mHealth app. This work will compare numerically the mobile cloud architectures for a case study mHealth app for Endocrine Hormonal Therapy (EHT) adherence. Experimental results are reported and conclusions are drawn concerning the design of the prototype mHealth app system using the MCC architectures.

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