Search results for "offloading"

Energy Efficient Optimization for Computation Offloading in Fog Computing System

2017

In this paper, we investigate the energy efficient computation offloading scheme in a multi-user fog computing system. We consider the users need to make the decision on whether to offload the tasks to the fog node nearby, based on the energy consumption and delay constraint. In particular, we utilize queuing theory to bring a thorough study on the energy consumption and execution delay of the offloading process. Two queuing models are applied respectively to model the execution processes at the mobile device (MD) and fog node. Based on the theoretical analysis, an energy efficient optimization problem is formulated with the objective to minimize the energy consumption subjects to execution…

A Sequential Game Approach for Computation-Offloading in an UAV Network

2017

International audience; Small drones are currently emerging as versatile nascent technology that can be used in exploration and surveillance missions. However, most of the underlying applications require very often complex and time-consuming calculations. Although, the limited resources available onboard the small drones, their mobility, the computation delays and energy consumption make the operation of these applications very challenging. Nevertheless, computation-offloading solutions provide feasible resolves to mitigate the issues facing these constrained devices. In this context, we address in this paper the problem of offloading highly intensive computation tasks, performed by a fleet…

Adaptive Service Offloading for Revenue Maximization in Mobile Edge Computing With Delay-Constraint

2019

Mobile Edge Computing (MEC) is an important and effective platform to offload the computational services of modern mobile applications, and has gained tremendous attention from various research communities. For delay and resource constrained mobile devices, the important issues include: 1) minimization of the service latency; 2) optimal revenue maximization; 3) high quality-of-service (QoS) requirement to offload the computational service offloading. To address the above issues, an adaptive service offloading scheme is designed to provide the maximum revenue and service utilization to MEC. Unlike most of the existing works, we consider both the delay-tolerant and delay-constraint services i…

Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds

2016

Nowadays, although the data processing capabilities of the modern mobile devices are developed in a fast speed, the resources are still limited in terms of processing capacity and battery lifetime. Some applications, in particular the computationally intensive ones, such as multimedia and gaming, often require more computational resources than a mobile device can afford. One way to address such a problem is that the mobile device can offload those tasks to the centralized cloud with data centers, the nearby cloudlet or ad hoc mobile cloud. In this paper, we propose a data offloading and task allocation scheme for a cloudlet-assisted ad hoc mobile cloud in which the master device (MD) who ha…

Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning Approach

2021

In mobile edge computing (MEC), partial computational offloading can be intelligently investigated to reduce the energy consumption and service delay of user equipment (UE) by dividing a single task into different components. Some of the components execute locally on the UE while the remaining are offloaded to a mobile edge server (MES). In this paper, we investigate the partial offloading technique in MEC using a supervised deep learning approach. The proposed technique, comprehensive and energy efficient deep learning-based offloading technique (CEDOT), intelligently selects the partial offloading policy and also the size of each component of a task to reduce the service delay and energy …

Opportunistic traffic Offloadings Mechanisms for Mobile/4G Networks

In the last few years, it has been observed a drastic surge of data traffic demand from mobile personal devices (smartphones and tablets) over cellular networks [1]. Even though a significant improvement in cellular bandwidth provisioning is expected with LTE-Advanced systems, the overall situation is not expected to change significantly. In fact, the diffusion of M2M and IoT devices is expected to increase at an exponential pace (the share of M2M devices is predicted to increase 5x by 2018 [1]) while the capacity of the cellular network is expected to increase linearly [1]. In order to meet such a high demand and to increase the capacity of the channel, multiple offloading techniques are c…

Opportunistic traffic Offloading Mechanisms for Mobile/4G Networks

2015

Dynamic Computation Offloading Scheme for Fog Computing System with Energy Harvesting Devices

2020

Fog computing is considered as a promising technology to meet the ever-increasing computation requests from a wide variety of mobile applications. By offloading the computation-intensive requests to the fog node or the central cloud, the performance of the applications, such as energy consumption and delay, are able to be significantly enhanced. Meanwhile, utilizing the recent advances of social network and energy harvesting techniques, the system performance could be further improved. In this paper, we take the social relationships of the energy harvesting MDs into the design of computational offloading scheme in fog computing. With the objective to minimize the social group execution cost…

Dynamic Resource Allocation and Computation Offloading for Edge Computing System

2020

In this work, we propose a dynamic optimization scheme for an edge computing system with multiple users, where the radio and computational resources, and offloading decisions, can be dynamically allocated with the variation of computation demands, radio channels and the computation resources. Specifically, with the objective to minimize the energy consumption of the considered system, we propose a joint computation offloading, radio and computational resource allocation algorithm based on Lyapunov optimization. Through minimizing the derived upper bound of the Lyapunov drift-plus-penalty function, the main problem is divided into several sub-problems at each time slot and are addressed sepa…

A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing

2019

Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by applicati…