Offloading Time Optimization via Markov Decision Process in Mobile-Edge Computing

Yang, Guisong; Hou, Ling; He, Xingyu; He, Daojing; Chan, Sammy; Guizani, Mohsen

doi:10.1109/jiot.2020.3033285

Cited by 96 publications

(44 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in most cases, the equipment is selfish and only considers the maximization of its interests. In [22], this work first investigates a MEC system consisting of mobile devices and heterogeneous edge servers that support various radio access technologies. An optimal offloading node selection strategy is formulated as a Markov decision process (MDP) and solved by employing the value iteration algorithm (VIA).…”

Section: Related Workmentioning

confidence: 99%

Balanced Computing Offloading for Selfish IoT Devices in Fog Computing

2022

View full text Add to dashboard Cite

Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. Offloading tasks to the fog that is closer to IoT users for processing has become a means to ensure that tasks are completed quickly. Fog computing can not only reduce the congestion of the backbone network but also ensure that the task is completed within the specified time. Since fog resources are limited, there will be resource competition among IoT devices. How to quickly and efficiently make an optimal computation offloading decision for individual selfish IoT devices is a fundamental research issue. This article regards the process of multiple IoT devices competing for fog devices as a game and proposes a distributed computation offloading algorithm. The goal is to optimize the balance of computation delay, energy consumption, and cost for fog nodes. The competition between IoT nodes eventually reaches an equilibrium point, that is the Nash equilibrium point. We prove the existence of Nash equilibrium by Weighted Potential Game. In addition, if a large number of IoT devices select the same node for offloading, which will cause the fog node to run out of power and make some networks unable to work normally. Further, causing part of the network to be paralyzed. Therefore, the paper considers the fairness of offloading to extend the network life cycle. A calculation rate adjustment algorithm is designed for the fairness of offloading to ensure that fog nodes do not run out of power and fail. This paper not only fully considers the performance of the IoT device, but also considers the fairness of the fog. Numerous experiments proved the effectiveness of the proposed algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

Balanced Computing Offloading for Selfish IoT Devices in Fog Computing

2022

View full text Add to dashboard Cite

show abstract

“…Therefore, it is very important to establish a suitable environment of offloading policy for computation offloading. In [10,11], these studies only paid attention to task offloading without optimizing communication and computing resources. It was assumed that the capacity of cloud computing is unlimited, and some studies only focused on the optimization of communication resources in [12,13].…”

Section: Related Workmentioning

confidence: 99%

“…A lot of works have been devoted to the research of computation offloading. Most of them have only focused on the process of offloading computing tasks from UE to MEC [10][11][12][13][14][15][16][17][18]. Only the optimal offloading decision is considered in [10,11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization Strategy of Task Offloading with Wireless and Computing Resource Management in Mobile Edge Computing

Gan

Chen

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Mobile edge computing (MEC) provides user equipment (UE) with computing capability through wireless networks to improve the quality of experience (QoE). The scenario with multiple base stations and multiple mobile users is modeled and analyzed. The optimization strategy of task offloading with wireless and computing resource management (TOWCRM) in mobile edge computing is considered. A resource allocation algorithm based on an improved graph coloring method is used to allocate wireless resource blocks (RBs). The optimal solution of computing resource is obtained by using KKT conditions. To improve the system utility, a semi-distributed TOWCRM strategy is proposed to obtain the task offloading decision. Theoretical simulations under different system parameters are executed, and the proposed semi-distributed TOWCRM strategy can be completed with finite iterations. Simulation results have verified the effectiveness of the proposed algorithm.

show abstract

“…The Markov decision process (MDP) is used to solve the offloading time optimization problem in [ 16 ]. The authors present computational offloading in MEC as an optimization problem and investigate the optimal offloading MES selection strategy.…”

Section: Introductionmentioning

confidence: 99%

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

Abbas

Ali

Abbas

et al. 2021

Sensors

View full text Add to dashboard Cite

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 consumption of UEs. We use deep learning to find, simultaneously, the best partitioning of a single task with the best offloading policy. The deep neural network (DNN) is trained through a comprehensive dataset, generated from our mathematical model, which reduces the time delay and energy consumption of the overall process. Due to the complexity and computation of the mathematical model in the algorithm being high, due to trained DNN the complexity and computation are minimized in the proposed work. We propose a comprehensive cost function, which depends on various delays, energy consumption, radio resources, and computation resources. Furthermore, the cost function also depends on energy consumption and delay due to the task-division-process in partial offloading. None of the literature work considers the partitioning along with the computational offloading policy, and hence, the time and energy consumption due to task-division-process are ignored in the cost function. The proposed work considers all the important parameters in the cost function and generates a comprehensive training dataset with high computation and complexity. Once we get the training dataset, then the complexity is minimized through trained DNN which gives faster decision making with low energy consumptions. Simulation results demonstrate the superior performance of the proposed technique with high accuracy of the DNN in deciding offloading policy and partitioning of a task with minimum delay and energy consumption for UE. More than 70% accuracy of the trained DNN is achieved through a comprehensive training dataset. The simulation results also show the constant accuracy of the DNN when the UEs are moving which means the decision making of the offloading policy and partitioning are not affected by the mobility of UEs.

show abstract

Offloading Time Optimization via Markov Decision Process in Mobile-Edge Computing

Cited by 96 publications

References 31 publications

Balanced Computing Offloading for Selfish IoT Devices in Fog Computing

Balanced Computing Offloading for Selfish IoT Devices in Fog Computing

Optimization Strategy of Task Offloading with Wireless and Computing Resource Management in Mobile Edge Computing

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

Contact Info

Product

Resources

About