Invasive technique for measuring the energy consumption of mobile devices applications in mobile cloud environments

2017 ◽  
Author(s):  
J.S. Silva ◽  
F.A.A. Lins ◽  
E.T.G. Sousa ◽  
H.B. Summer ◽  
C.M. Fernandes
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Invasive Technique ◽  
Mobile Cloud ◽  
Cloud Environments

Designing Mobile Collaborative Applications for Cloud Environments

2016 ◽  
pp. 34-60 ◽  
Author(s):  
Nadir Guetmi ◽  
Abdessamad Imine
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Mobile Devices ◽  
Design Patterns ◽  
Self Control ◽  
Network Coverage ◽  
Collaborative Editing ◽  
Collaborative Applications ◽  
Cloud Environments ◽  
Continuous Use

Mobile devices have experienced a huge progress in the capacity of computing, storage and data visualization. They are becoming the device of choice for operating a large variety of applications while supporting real-time collaboration of people and their mobility. Despite this progress, the energy consumption and the network coverage remain a serious problem against an efficient and continuous use of these mobile collaborative applications and a great challenge for their designers and developers. To address these issues, this chapter describes design patterns that help modelling mobile collaborative applications to support collaboration through the cloud. Two levels are presented: the first level provides self-control to create clones of mobile devices, manage users' groups and recover failed clones in the cloud. The second level supports group collaboration mechanisms in real-time. These design patterns have been used as a basis for the design of a mobile collaborative editing application.

scholarly journals Seamless communication techniques in mobile cloud computing: A survey

2021 ◽  
Vol 2 (2) ◽  
pp. 1-20
Author(s):  
Pagoui Lagabka Constant ◽  
Ahyoung Lee ◽  
Kun Suo ◽  
Donghyun Kim
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Service Provision ◽  
Mobile Cloud Computing ◽  
Base Station ◽  
Proper Function ◽  
Mobile Cloud ◽  
Technological Advances ◽  
Cloud Environments ◽  
Seamless Communication

Achieving seamless communication and smooth service provision between the cloud and end user's mobile device is one of the main challenges existing in mobile cloud environments. Mobile Cloud Computing (MCC) allows cloud environments to mitigate resource limitation problems for mobile devices. The most popular mobile devices such as smartphones, autonomous vehicles, drones, and other smart electronic equipment are in constant motion and frequently change their point of connection (base station or edge) to mobile computing networks. In these situations of mobility, the data being transmitted, and the services being provided to the device should not be interrupted as the proper function of the device depends on these services. Applications that rely heavily on data and services stored in the cloud environment should be available even when the device has moved from one pole to another. Various existing generic surveys emphasize important solutions to some of the challenges faced in MCC. Different solutions were proposed to achieve seamless communication in MCC, presenting the taxonomy of the interworking and mobility techniques and their possibilities. However, they have not provided a clear evaluation of MCC techniques for achieving seamless communication and service provision, and have not taken into consideration current technological advances such as 5G, femtocell, etc. In this paper, we provide a survey of the different solutions proposed to achieve seamless communication in MCC by taking current technological advances into account. Furthermore, some shortcomings associated with the presented methods are outlined, along with the current issues and research challenges faced in MCC. However, for the purposes of data protection and security, previously proposed schemes already achieve the goal of protecting users' attribute privacy and they have the same access policy; some can even achieve full security, but they are just limited in decryption efficiency.

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.

A Social-Oriented Mobile Cloud Scheme for Optimal Energy Conservation

2015 ◽  
pp. 97-121 ◽  
Author(s):  
Constandinos X. Mavromoustakis ◽  
George Mastorakis ◽  
Athina Bourdena ◽  
Evangelos Pallis ◽  
Dimitrios Stratakis ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Conservation ◽  
Mobile Computing ◽  
Mobile Devices ◽  
Mobile Cloud ◽  
Research Approach ◽  
Mobile Nodes ◽  
Energy Usage ◽  
Connectivity Model ◽  
Application Execution

This chapter elaborates on energy usage optimization issues by exploiting a resource offloading process based on a social-oriented mobile cloud scheme. The adoption of the proposed scheme enables for increasing the reliability in services provision to the mobile users by guaranteeing sufficient resources for the mobile application execution. More specifically, this chapter describes the process to improve the energy consumption of the mobile devices through the exploitation of a social-oriented model and a cooperative partial process offloading scheme. This research approach exploits social centrality, as the connectivity model for the resource offloading, among the interconnected mobile devices to increase the energy usage efficiency, the mobile nodes availability, as well as the process of execution reliability. The proposed scheme is thoroughly evaluated to define the validity and the efficiency for the energy conservation increase of future mobile computing devices.

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.

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.

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 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.

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