Virtualization in Mobile Cloud Computing (VMCC) Environments

2021 ◽  
pp. 404-411
Author(s):  
Raghvendra Kumar ◽  
Prasant Kumar Pattnaik ◽  
Priyanka Pandey
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
User Experience ◽  
Mobile Device ◽  
Mobile Cloud Computing ◽  
Personal Computers ◽  
Mobile Cloud ◽  
Cloud Networking

Unfortunately, most of the widely used protocols for remote desktop access on mobile devices have been designed for scenarios involving personal computers. Furthermore, their energy consumption at the mobile device has not been fully characterized. In this chapter, we specially address energy consumption of mobile cloud networking realized through remote desktop technologies. In order to produce repeatable experiments with comparable results, we design a methodology to automate experiments with a mobile device. Furthermore, we develop an application that allows recording touch events and replaying them for a certain number of times. Moreover, we analyze the performance of widely used remote desktop protocols through extensive experiments involving different classes of mobile devices and realistic usage scenarios. We also relate the energy consumption to the different components involved and to the protocol features. Finally, we provide some considerations on aspects related to usability and user experience.

Virtualization in Mobile Cloud Computing (VMCC) Environments

2017 ◽  
pp. 106-114
Author(s):  
Raghvendra Kumar ◽  
Prasant Kumar Pattnaik ◽  
Priyanka Pandey
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
User Experience ◽  
Mobile Device ◽  
Mobile Cloud Computing ◽  
Personal Computers ◽  
Mobile Cloud ◽  
Cloud Networking

Unfortunately, most of the widely used protocols for remote desktop access on mobile devices have been designed for scenarios involving personal computers. Furthermore, their energy consumption at the mobile device has not been fully characterized. In this chapter, we specially address energy consumption of mobile cloud networking realized through remote desktop technologies. In order to produce repeatable experiments with comparable results, we design a methodology to automate experiments with a mobile device. Furthermore, we develop an application that allows recording touch events and replaying them for a certain number of times. Moreover, we analyze the performance of widely used remote desktop protocols through extensive experiments involving different classes of mobile devices and realistic usage scenarios. We also relate the energy consumption to the different components involved and to the protocol features. Finally, we provide some considerations on aspects related to usability and user experience.

scholarly journals Resource Intensification for Mobile Devices Using the Approximate Computing Entities

2020 ◽  
Vol 2 (1) ◽  
pp. 26-36
Author(s):  
Dr. Suma V
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Approximate Computing ◽  
Local Resources ◽  
And Storage ◽  
High Storage

The mobile devices are termed to highly potential due to their capability of rendering services without being plugged to the electric grid. These device are becoming highly prominent due to their constant progress in computing as well as storing capacities and as they are very much closer to the users. Despites its advantages it still faces many problems due to the load balancing and energy consumption due to its limited battery limited and storage availability as some applications or the video downloading requires high storage facilities consuming majority of the energy in turn reducing the performance of the mobile devices. So as to improve the performance and the capability of the mobile devices the mobile cloud computing that integrates the mobile devices with the cloud paradigm has emerged as a promising paradigm. This enables the augmentation of the local resources for the mobile devices to enhance its capabilities in order to improve its functioning. This is basically done by proper offloading and resource allocation. The proposed method in the paper utilizes the optimal offloading strategy (Single and double strand offloading) and follows an Ant colony optimization based resource allocation for improving the functioning the mobile devices in terms of energy consumption and storage.

Data Storage Security in Mobile Cloud Computing (MCC) using Improved Blowfish Algorithm

2021 ◽  
pp. 100-111
Author(s):  
Seada Abdu Wakene ◽  
Sisay Muleta Hababa ◽  
Gutema Seboka Daba ◽  
K S Ananda Kumar
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Storage ◽  
Mobile Device ◽  
Data Security ◽  
Mobile Cloud Computing ◽  
Data Access ◽  
Security And Privacy ◽  
Mobile Cloud ◽  
Technical Requirements

Mobile cloud computing (MCC) combines cloud computing and mobile computing to deliver vast computational resources to mobile consumers, network operators, and cloud computing providers. You may access your data from anywhere in the globe using any mobile device that is linked to the Internet. Cloud computing provides access to data in real-time whenever and wherever want. Any conventional mobile device can benefit from MCC's infrastructure, computational capacity, software, and platform services. Network security, web application security, data access, authentication, authorization, data confidentiality, and data breach are all concerns of MCC's security. Because mobile devices lack sufficient storage and processing power, their data storage capacity is limited. Users of mobile devices may inadvertently provide sensitive information over the network or through the application. Therefore, data security is the main concern for mobile device users. The objective of this paper is to find a solution that can enhance technical requirements with relation to user’s data security and privacy in mobile cloud computing. To achieve this improved blowfish encryption algorithm is used to encrypt each user’s data security and where the shared secret key is hash down using message digest called secured hash function. Hashing can increase the integrity and privacy of user data. The proposed algorithm is evaluated with a normal blowfish algorithm and 3DES with different parameters. Improved blowfish algorithm shows better performance than normal blowfish algorithm and 3DES. In this work, we have developed web-based application where the Amazon MySQL RDS database is used for data storage.

scholarly journals An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4527
Author(s):  
Abid Ali ◽  
Muhammad Munawar Iqbal ◽  
Harun Jamil ◽  
Faiza Qayyum ◽  
Sohail Jabbar ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Task Scheduling ◽  
Time Management ◽  
Mobile Cloud Computing ◽  
Fog Computing ◽  
Mobile Cloud ◽  
Computational Performance ◽  
Task Offloading

Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device’s decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.

A Survey of Mobile Cloud Computing for Extending Energy of Mobile Devices

2014 ◽  
Vol 573 ◽  
pp. 549-555
Author(s):  
P. Thanapal ◽  
M.A. Saleem Durai
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Mobile Devices ◽  
Present State ◽  
Mobile Device ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Mobile Cloud

Mobile cloud computing will wear down gaining quality among users, the researchers predicts these troubles by execution of mobile applications on application suppliers external to the mobile device. During this paper, we have a tendency to gift a wide survey of mobile cloud computing, whereas prominence the particular considerations in mobile cloud computing square measure as follows. (a) Highlights the present state in Application of cloud computing usage in real time world. (b) Identifies the problems in testing bandwidth and (c) provides a optimizing of the offloading that saves energy

Fuzzy simplified swarm optimization for multisite computational offloading in mobile cloud computing

2020 ◽  
Vol 39 (6) ◽  
pp. 8285-8297
Author(s):  
V. Meena ◽  
Obulaporam Gireesha ◽  
Kannan Krithivasan ◽  
V.S. Shankar Sriram
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Swarm Optimization ◽  
Computational Offloading ◽  
Smart Mobile ◽  
Smart Mobile Devices

Mobile Cloud Computing (MCC)’s rapid technological advancements facilitate various computational-intensive applications on smart mobile devices. However, such applications are constrained by limited processing power, energy consumption, and storage capacity of smart mobile devices. To mitigate these issues, computational offloading is found to be the one of the promising techniques as it offloads the execution of computation-intensive applications to cloud resources. In addition, various kinds of cloud services and resourceful servers are available to offload computationally intensive tasks. However, their processing speeds, access delays, computation capability, residual memory and service charges are different which retards their usage, as it becomes time-consuming and ambiguous for making decisions. To address the aforementioned issues, this paper presents a Fuzzy Simplified Swarm Optimization based cloud Computational Offloading (FSSOCO) algorithm to achieve optimum multisite offloading. Fuzzy logic and simplified swarm optimization are employed for the identification of high powerful nodes and task decomposition respectively. The overall performance of FSSOCO is validated using the Specjvm benchmark suite and compared with the state-of-the-art offloading techniques in terms of the weighted total cost, energy consumption, and processing time.

scholarly journals Challenges of Mobile Devices’ Resources and in Communication Channels and their Solutions

2021 ◽  
Vol 13 (1) ◽  
pp. 39-46
Author(s):  
Rashid G. Alakbarov ◽  
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Saving ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Communication Channels ◽  
Mobile Cloud ◽  
Memory Resources ◽  
And Storage

The article is dedicated to the development of cloudlet based mobile cloud computing (MCC) to address the restrictions that occur in the resources of mobile devices (energy consumption, computing and memory resources, etc.) and the delays occurring in communication channels. The architecture offered in the article more efficiently ensures the demand of mobile devices for computing and storage and removes the latency that occur in the network. At the same time, the tasks related to energy saving and eliminating delays in communication channels by solving the problems that require complex computing and memory resources in the cloudlets located nearby the user were outlined in the article.

scholarly journals Enabling Efficient Decentralized and Privacy Preserving Data Sharing in Mobile Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jiawei Zhang ◽  
Ning Lu ◽  
Teng Li ◽  
Jianfeng Ma
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Mobile Devices ◽  
Data Sharing ◽  
Mobile Cloud Computing ◽  
High Efficiency ◽  
Mobile Cloud ◽  
User Privacy ◽  
Access Policy ◽  
Fine Grained

Mobile cloud computing (MCC) is embracing rapid development these days and able to provide data outsourcing and sharing services for cloud users with pervasively smart mobile devices. Although these services bring various conveniences, many security concerns such as illegally access and user privacy leakage are inflicted. Aiming to protect the security of cloud data sharing against unauthorized accesses, many studies have been conducted for fine-grained access control using ciphertext-policy attribute-based encryption (CP-ABE). However, a practical and secure data sharing scheme that simultaneously supports fine-grained access control, large university, key escrow free, and privacy protection in MCC with expressive access policy, high efficiency, verifiability, and exculpability on resource-limited mobile devices has not been fully explored yet. Therefore, we investigate the challenge and propose an Efficient and Multiauthority Large Universe Policy-Hiding Data Sharing (EMA-LUPHDS) scheme. In this scheme, we employ fully hidden policy to preserve the user privacy in access policy. To adapt to large scale and distributed MCC environment, we optimize multiauthority CP-ABE to be compatible with large attribute universe. Meanwhile, for the efficiency purpose, online/offline and verifiable outsourced decryption techniques with exculpability are leveraged in our scheme. In the end, we demonstrate the flexibility and high efficiency of our proposal for data sharing in MCC by extensive performance evaluation.

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