Adaptive application offloading decision and transmission scheduling for mobile cloud computing

Wang, Junyi; Peng, Jie; Wei, Yanheng; Liu, Didi; Fu, Jielin

doi:10.1109/icc.2016.7510721

Cited by 15 publications

(9 citation statements)

References 9 publications

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“…In the above references [9]- [14], the authors only considered the performance of processing a single task, nevertheless, for applications like multi-media and file backup, etc., the coupling among the random task arrivals should not be neglected, so long-term performance metrics and stochastic task models are more suitable. Reference [15] studied offloading decision optimization to minimize the average execution cost. The authors in [16] considered the joint optimization of offloading policy, the local CPU speed control, and network interface selection to minimize the time-averaged expected total average energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Computation Offloading and Resource Allocation in Vehicular Networks Based on Dual-Side Cost Minimization

Chu

et al. 2019

IEEE Trans. Veh. Technol.

256

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Section: Related Workmentioning

confidence: 99%

Computation Offloading and Resource Allocation in Vehicular Networks Based on Dual-Side Cost Minimization

Chu

et al. 2019

IEEE Trans. Veh. Technol.

256

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“…In a single-user scenario, the offloading strategy optimization problem is often converted into an optimal programming problem. Wang et al developed a low-complexity adaptive offloading decision-transmission scheduling scheme based on the Lyapunov optimization theory for mobile devices, optimizing the average execution time and average energy consumption of tasks [19]. Liu et al used the Markov decision process to develop a task offloading strategy with minimum delay under power constraints and proposed an effective one-dimensional search algorithm to find the optimal task scheduling strategy.…”

Section: Related Workmentioning

confidence: 99%

A Game-Theoretic Approach to Computation Offloading in Satellite Edge Computing

et al. 2020

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Mobile edge computing (MEC) is proposed as a new paradigm to meet the ever-increasing computation requirements, which is caused by the rapid growth of the Internet of Things (IoT) devices. As a supplement to the terrestrial network, satellites can provide communication to terrestrial devices in some harsh environments and natural disasters. Satellite edge computing is becoming an emerging topic and technology. In this paper, a game-theoretic approach to the optimization of computation offloading strategy in satellite edge computing is proposed. The system model for computation offloading in satellite edge computing is established, considering the intermittent terrestrial-satellite communication caused by satellites orbiting. We conduct a computation offloading game framework and compute the response time and energy consumption of a task based on the queuing theory as metrics of optimizing performance. The existence and uniqueness of the Nash equilibrium is theoretically proved, and an iterative algorithm is proposed to find the Nash equilibrium. Simulation results validate the proposed algorithm and show that the game-based offloading strategy can greatly reduce the average cost of a device.

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“…Generally speaking, the existing task migration strategies are divided into three categories: the whole application is executed in the cloud [13]; the whole application is migrated to the cloud to execute or to the mobile to execute according to the power saving situation; part of the migrable tasks are migrated to the cloud to execute, and the rest are completed in the mobile end [14]. However, the existing migration strategies do not take into account the multi-dependence between subtasks, and the mobile terminal battery development speed cannot meet the growing demand for energy consumption of mobile terminals [15].…”

Section: Related Workmentioning

confidence: 99%

“…At present, the existing task migration strategy is to make the migration decision under the premise that the migration service node has been established [4]. It does not take into account the scenarios when the multi-service nodes are available, and cannot give full play to the characteristics and advantages of mobile edge computing.…”

Section: Introductionmentioning

confidence: 99%

An energy saving based on task migration for mobile edge computing

Wang

Zhu

Hei

et al. 2019

J Wireless Com Network

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Mobile edge computing (MEC), as the key technology to improve user experience in a 5G network, can effectively reduce network transmission delay. Task migration can migrate complex tasks to remote edge servers through wireless networks, solving the problems of insufficient computing capacity and limited battery capacity of mobile terminals. Therefore, in order to solve the problem of "how to realize low-energy migration of complex dependent applications," a subtask partitioning model with minimum energy consumption is constructed based on the relationship between the subtasks. Aiming at the problem of execution time constraints, the genetic algorithm is used to find the optimal solution, and the migration decision results of each subtask are obtained. In addition, an improved algorithm based on a genetic algorithm is proposed to dynamically adjust the optimal solution obtained by genetic algorithm by determining the proportion of task energy consumption and mobile phone residual power. According to the experimental results, it can be concluded that the fine-grained task migration strategy combines the advantages of mobile edge computing, not only satisfies the smooth execution of tasks, but also reduces the energy consumption of terminal mobile devices. In addition, experiments show that the improved algorithm is more in line with users' expectations. When the residual power of mobile devices is reduced to a certain value, tasks are migrated to MEC server to prolong standby time.

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Adaptive application offloading decision and transmission scheduling for mobile cloud computing

Cited by 15 publications

References 9 publications

Computation Offloading and Resource Allocation in Vehicular Networks Based on Dual-Side Cost Minimization

Computation Offloading and Resource Allocation in Vehicular Networks Based on Dual-Side Cost Minimization

A Game-Theoretic Approach to Computation Offloading in Satellite Edge Computing

An energy saving based on task migration for mobile edge computing

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