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Mobile Cloud Computing
Published in Jithesh Sathyan, Anoop Narayanan, Navin Narayan, K V Shibu, A Comprehensive Guide to Enterprise Mobility, 2016
Jithesh Sathyan, Anoop Narayanan, Navin Narayan, K V Shibu
In future, cloud computing will make computing more collaborative, intelligent, and available. We are witnessing applications of cloud computing that otherwise would never have been possible. So cloud computing is not only a different way of doing things but also a window for newer dimensions of innovation. Mobile cloud computing refers to an IT infrastructure in which the data processing and data storage happen outside the mobile device. The outside mobile infrastructure virtually has infinite processing power and memory, and the mobile device communicates with the cloud using a very high-speed data channel literally making a mobile device capable of processing and rendering heavy applications (refer to Figure 25.1, which shows a mobile cloud application environment). This technology could make applications independent of mobile platforms.
Managing Mobility with SDN: A Practical Walkthrough
Published in Hrishikesh Venkatarman, Ramona Trestian, 5G Radio Access Networks: Centralized RAN, Cloud-RAN, and Virtualization of Small Cells, 2017
Xuan Thuy Dang, Manzoor Ahmed Khan
Future networks will have to cope with unprecedented demands for higher capacity, lower delay, higher QoE, more devices, reduced costs, among other things. In addition, cloud-based service provisioning becomes the first choice for application service providers to take advantage of resource elasticity, cost saving, and reliability. Mobile cloud computing, which enables access to cloud-based applications over a mobile network, requires the interplay between cloud and mobile network infrastructures. Given the limitation of current mobile networks, the full potential of cloud computing cannot be brought to mobile users. Future mobile networks designed for mobile cloud computing need to be more agile, elastic, and efficient.
Smart Analytical Lab
Published in Shampa Sen, Leonid Datta, Sayak Mitra, Machine Learning and IoT, 2018
Subhrodeep Saha, Sourish Sen, Bharti Singh, Shampa Sen
In simple terms, cloud computing basically provides access to data and information from anywhere at any time, all the while reducing the need for bulky hardware. More specifically, mobile cloud computing can be defined as the incorporation of mobile devices (such as smartphones) with cloud computing technology in order to make these devices more resourceful by increasing their storage capacity, computational power, energy efficiency, and context awareness (“smartness”) (Stergiou et al. 2018).
Data Analysis of the Development Status of Basketball National Fitness Based on Fog Computing
Published in Applied Artificial Intelligence, 2023
With the popularity of mobile IoT devices, mobile cloud computing has become an emerging computing model for efficient management of limited resources. Fog computing is considered as a combination of wireless networks, mobile computing, and cloud computing that can provide rich computing resources to end devices. From the user’s point of view, especially when dealing with some computationally intensive applications and tasks, mobile cloud computing overcomes some limitations, such as battery life, computing power, and memory limitations. But mobile cloud computing also has several drawbacks, including low bandwidth, low security and privacy, low service availability, low network compatibility, and limited energy. Unlike fog computing, which sends task requests from end devices to nearby fog nodes for processing, mobile cloud computing computes and stores data from end devices on the cloud and does not process them locally.
Mobile cloud computing apps and information disclosure: the moderating roles of dispositional and behaviour-based traits
Published in Behaviour & Information Technology, 2022
Hamid Reza Nikkhah, Rajiv Sabherwal, Jalal Sarabadani
The use of mobile devices (e.g. smartphones and tablets) has surged over the past few years, which accounts for 57% of online traffic in the US (McLeod 2018). A recent report shows that Americans own multiple mobile devices (Pew Research Center 2018) and use them for an average of 3.5 hours per day (McLeod 2018). This increased reliance on mobile devices has led to a new approach to developing mobile apps that integrates them with cloud computing technology. The resulting mobile cloud computing (MCC) apps are multiplatform and internet-based, can be installed on any mobile device, and automatically transfer users’ data to the cloud (Dinh et al. 2013).1 Such apps store data in the cloud that enables access to information from multiple devices. Most MCC apps provide a web-based version (e.g. Dropbox) that helps users to work with their files without having to access the MCC app itself. Finally, data in the cloud are backed up automatically and replicated in several storages, allaying users’ concerns about data loss.
Application-oriented offloading in heterogeneous networks for mobile cloud computing
Published in Enterprise Information Systems, 2018
Fan-Hsun Tseng, Hsin-Hung Cho, Kai-Di Chang, Jheng-Cong Li, Timothy K. Shih
Mobile cloud computing has been proposed to provide additional resources to mobile users. However, an offloading scheme should be designed according to the types of applications. Moreover, the task offloaded should has its exclusive way to cloud data center. In this paper, we focus on offloading problem in mobile cloud computing with cloud data center. The applications are classified into computation-oriented and network-oriented applications. Based on the classification, the proposed MOTM algorithm selects tasks to migrate to cloud data center through wireless links such as APs and BSs. Then the proposed METC algorithm selects VMs and PMs to execute the applications offloaded based on their orientation. The simulation-based results show that the MOTM algorithm occupies less network bandwidth and minimizes the execution time on mobile device, and the METC algorithm achieves less execution time of cloud data center. In summary, the MOTM and METC algorithms not only minimize the total execution time on both mobile device and data center but also decrease energy consumption compared to other offloading schemes. In the paper we only classify applications into computation and network types. In the future, the types of applications should be classified more carefully and completely, e.g., storage and security applications. At that time, we will try to propose a refined offloading scheme for better performance results.