scholarly journals An Energy-Aware Task Offloading Mechanism in Multiuser Mobile-Edge Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Lan Li ◽  
Xiaoyong Zhang ◽  
Kaiyang Liu ◽  
Fu Jiang ◽  
Jun Peng
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Mobile Devices ◽  
Access Point ◽  
Mobile User ◽  
Nonlinear Integer Programming ◽  
Integer Programming Problem ◽  
Energy Aware ◽  
Energy Efficiency Improvement ◽  
Task Offloading

Mobile-edge cloud computing, an emerging and prospective computing paradigm, can facilitate the complex application execution on resource-constrained mobile devices by offloading computation-intensive tasks to the mobile-edge cloud server, which is usually deployed in close proximity to the wireless access point. However, in the multichannel wireless interference environment, the competition of mobile users for communication resources is not conducive to the energy efficiency of task offloading. Therefore, how to make the offloading decision for each mobile user and select its suitable channel become critical issues. In this paper, the problem of the offloading decision is formulated as a 0-1 nonlinear integer programming problem under the constraints of channel interference threshold and the time deadline. Through the classification and priority determination for the mobile devices, a reverse auction-based offloading method is proposed to solve this optimization problem for energy efficiency improvement. The proposed algorithm not only achieves the task offloading decision but also gives the facility of resource allocation. In the energy efficiency performance aspects, simulation results show the superiority of the proposed scheme.

scholarly journals Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
VanDung Nguyen ◽  
Tran Trong Khanh ◽  
Tri D. T. Nguyen ◽  
Choong Seon Hong ◽  
Eui-Nam Huh
Keyword(s):  
Cloud Computing ◽  
Augmented Reality ◽  
Mobile Devices ◽  
Computing System ◽  
Mobile User ◽  
Computation Offloading ◽  
Context Aware ◽  
Computing Systems ◽  
Iot Applications ◽  
Service Times

AbstractIn the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

Mobile and Wireless Computing towards Sustainable and Ubiquitous Clouds Services

2013 ◽  
Vol 5 (3) ◽  
pp. 1-5 ◽  
Author(s):  
Ching-Hsien Hsu ◽  
Emmanuel Udoh
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Critical Role ◽  
Cloud Services ◽  
Future Research ◽  
Mobile Cloud ◽  
Extended Version ◽  
Special Issue ◽  
Energy Aware ◽  
Wireless Computing

The ever-growing cloud computing and services provide dynamic intelligence and play an increasingly critical role in all aspects of our lives. By taking advantage of virtualized resources, cloud computing services presents an attractive means to address the challenges while realizing the potential of Mobile and Wireless Computing (MWC). The MWC paradigm can be generalized to include mobile devices, which not only incorporate sophisticated methods for users to interact with the online world through numerous applications in their devices, but are endowed with multiple sensors that enable them to contribute data as nodes in the IoT. In this context, mobile cloud services that enable widespread data collection through mobile devices and collaborative use of mobile devices to enhance existing and realize new applications are very much of interest. As such, the MWC has come to the picture seeking solutions for computing and IT infrastructures to be energy efficient and environmentally friendly. This special issue is in response to the increasing convergence between MWC and cloud services, while different approaches exist, challenges and opportunities are numerous in this context. The research papers selected for this special issue represent recent progresses in the field, including works on services computing and modeling, mobile cloud, U-Care cloud, vehicle networks, energy-aware architectures, and wireless sensor network technologies and applications. This special issue includes four extended version of the selected paper originally presented at the 17th Mobile Computing Workshop (MC 2012) and the 8th Workshop on Wireless, Ad Hoc and Sensor Networks (WASN 2012), held at Taipei, Taiwan; one extended version of the selected paper originally presented at the 4th IEEE International Conference on Cloud Computing Technology and Science (IEEE CloudCom 2012), held at Taipei, Taiwan; and one regular submission with 20% average acceptance rate for 2012 submissions in IJGHPC. The papers selected for this issue not only contribute valuable insights and results but also have particular relevance to the mobile, wireless and cloud computing community. All of them present high quality results for tackling problems arising from the ever-growing mobile and cloud services. We believe that this special issue provides novel ideas and state-of-the-art techniques in the field, and stimulates future research in the mobile and wireless services in clouds.

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.

scholarly journals Constraints Based Heuristic Approach for Task Offloading In Mobile Cloud Computing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Raj Kumari, Sakshi Kaushal
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Computation Time ◽  
Mobile Cloud ◽  
Delay Sensitive ◽  
Communication Time ◽  
Delay Tolerant ◽  
Task Offloading

Mobile devices are supporting a wide range of applications irrespective of their configuration. There is a need to make the mobile applications executable on mobile devices without concern of battery life. For optimizing mobile applications computational offloading is highly preferred. It helps to overcome the severity of scarce resources constraint mobile devices. In offloading, which part of the application to be offloaded, on which processor and what is available bandwidth rate are the main crucial issues. As subtasks of mobile applications are interdependent, efficient execution of application requires research of favorable wireless network conditions before to take the offloading decision. Broadly in mobile cloud computing the applications is either delay sensitive or delay tolerant. For delay sensitive applications completion time has the highest priority whereas for delay tolerant type of applications depending on the network conditions decision of offloading can be taken. Sometimes, computation time on a cloud server is less but it consumes high communication time which ultimately gives inefficient offloading results. To address this issue, we have proposed a heuristic based level wise task offloading (HTLO). It includes computation time, communication time and maximum energy available on the mobile device to take the decision of offloading. For simulation study, a mobile application is considered as a directed graph and all the tasks are executed on the basis of their levels. The overall results of the proposed heuristic approach are compared with state-of-the-art K-M LARAC algorithm and results show the improvement in execution time, communication time, mobile device energy consumption and total energy consumption.

scholarly journals Approaches to Address the Operational Limitations of MANETs through Ad Hoc Mobile Cloud Computing Paradigm

2020 ◽  
Vol 14 (09) ◽  
pp. 153
Author(s):  
Muralidhar Kurni ◽  
Madhavi K
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Ad Hoc ◽  
Mobile User ◽  
Battery Life ◽  
Computing Platform ◽  
Computing Paradigm ◽  
Cloud Computing Service ◽  
Mobile Ad Hoc ◽  
Computing Platforms

Mobile Ad hoc Networks (MANETs) are getting essential to wireless communications because of the growing popularity of mobile devices. However, mobile devices face several challenges in their resources (eg., battery life, storage, and bandwidth) and communication (e.g., mobility and, security). Limited resources considerably impede the improvement of service qualities. MCC permits resources in cloud computing platforms to be used to overcome the dearth of native resources in mobile devices. However, this hinders a mobile user from taking part in a cloud computing service if a connection to the cloud computing platform is both unobtainable or too dear to afford. Therefore, an initial solution will be to use resources from nearby devices instantly. Such a paradigm is known as mobile ad hoc cloud computing where each mobile device can use the services and resources of its neighbor devices. This paper shortly explains the contributions done by us to overcome the three vital operational limitations of mobile devices namely connectivity, storage and, processing capability through the Mobile Ad Hoc Cloud Computing Paradigm. The potential promise of the proposed approaches is evaluated through simulations. Our proposals, taken together intend to increase the operational efficiency of MANETs.

scholarly journals MCA-MAC: Modified Cooperative Access MAC Protocol in Wireless Sensor Networks

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
Aya Hossam ◽  
Tarek Salem ◽  
Anar Abdel Hady ◽  
Sherine Abd El-Kader
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Mac Protocol ◽  
Relay Node ◽  
Packet Delay ◽  
Access Point ◽  
Wireless Sensor ◽  
System Throughput ◽  
Energy Aware

Throughput, energy efficiency and average packet delivery delay are some of the most crucial metrics that should be considered in Wireless Sensor Networks (WSNs). This paper proposes a modified Medium Access Control (MAC) protocol for WSNs, called (MCA-MAC). MCA-MAC aims to improve the previous metrics and thus the overall performance of WSNs through using cooperative communication. It enables source nodes from using intermediate nodes as relays to send their data through them to the access point. MCA-MAC protocol is also acting as a cross layer protocol where the best end-to-end path between the source and destination is found through an efficient algorithm. Mathematical analysis demonstrates that MCA-MAC protocol can determine the optimal relay node that has the minimum transmission time for the given source-destination pair. Using Multi-Paradigm Programming Language (MATLAB) simulation environment, this paper estimates MCA-MAC protocol performance in terms of system throughput, energy efficiency and delay. The results show that MCA-MAC protocol outperforms the existing scheme called Throughput and Energy aware Cooperative MAC protocol (TEC-MAC) protocol under ideal and dynamic channel conditions. Under ideal conditions, MCA-MAC protocol achieved throughput, and energy efficiency improvements of 12%, and 50% respectively, more than TEC-MAC protocol. While the packet delay through using MCA-MAC has been decreased by about 48% less than TEC-MAC protocol.

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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