Dynamic Task Offloading and Resource Allocation for Mobile-Edge Computing in Dense Cloud RAN

Zhang, Qi; Gui, Lin; Hou, Fen; Chen, Jiacheng; Zhu, Shichao; Tian, Feng

doi:10.1109/jiot.2020.2967502

Cited by 167 publications

(75 citation statements)

References 40 publications

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“…Zhang et al 30 have studied the dynamic task offloading and resource provisioning problem to achieve a trade‐off between the network energy and service delay in mobile edge computing systems. They used the mixed‐integer nonlinear programming to formulate the joint the task offloading and elastic resource allocation and proposed a Lyapunov optimization‐based approach to decompose the original problem into resource scheduling, transmit power allocation, and task offloading decision subproblems which are addressed by matching game mechanisms.…”

Section: Related Workmentioning

confidence: 99%

Joint computation offloading and resource provisioning for edge‐cloud computing environment: A machine learning‐based approach

Shahidinejad

Ghobaei‐Arani

2020

Softw Pract Exp

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Summary In recent years, the usage of smart mobile applications to facilitate day‐to‐day activities in various domains for enhancing the quality of human life has increased widely. With rapid developments of smart mobile applications, the edge computing paradigm has emerged as a distributed computing solution to support serving these applications closer to mobile devices. Since the submitted workloads to the smart mobile applications changes over the time, decision making about offloading and edge server provisioning to handle the dynamic workloads of mobile applications is one of the challenging issues into the resource management scope. In this work, we utilized learning automata as a decision‐maker to offload the incoming dynamic workloads into the edge or cloud servers. In addition, we propose an edge server provisioning approach using long short‐term memory model to estimate the future workload and reinforcement learning technique to make an appropriate scaling decision. The simulation results obtained under real and synthetic workloads demonstrate that the proposed solution increases the CPU utilization and reduces the execution time and energy consumption, compared with the other algorithms.

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Section: Related Workmentioning

confidence: 99%

Joint computation offloading and resource provisioning for edge‐cloud computing environment: A machine learning‐based approach

Shahidinejad

Ghobaei‐Arani

2020

Softw Pract Exp

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“…The co-deployment of MEC and cloud-RAN (C-RAN) technology takes advantage of network function virtualization (NFV) and is beneficial in terms of cost and scalability from the mobile network operator's (MNO) point of view [102] but it requires overcoming some technical challenges such as network management, especially in HetNets. A MEC-enabled C-RAN in a UDN was proposed in [103] to optimize the EE by joint task offloading and resource allocation. The optimization was formulated as a stochastic MINLP problem and, based on the Lyapunov optimization theory, the problem was solved by a dual decomposition method and matching game.…”

Section: F Cloud Ran and Multi-access Edge Computingmentioning

confidence: 99%

Survey of Radio Resource Management in 5G Heterogeneous Networks

et al. 2020

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Radio resource management (RRM) for future fifth-generation (5G) heterogeneous networks (HetNets) has emerged as a critical area due to the increased density of small-cell networks and radio access technologies. Recent research has mostly concentrated on resource management, including spectrum utilization and interference mitigation, but the complexities of these resources have been given little attention. This paper provides an overview of the issues arising from future 5G systems and highlights their importance. The different approaches used in recently published surveys categorizing RRM schemes are discussed, and the survey method is presented. We report on a survey of HetNet RRM schemes that have been studied recently, with a focus on the joint optimization of radio resource allocation with other mechanisms. These RRM schemes were subcategorized according to their optimization metrics and qualitatively analyzed and compared. An analysis of the complexity of RRM schemes in terms of implementation and computation is presented. Several potential scopes of research for future RRM in 5G HetNets are also identified.

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“…In order to minimize the energy consumption of smart devices, it was necessary to jointly optimize offloading options for wireless resource allocation and computing resource allocation [16]. Reference [17] proposed a random mixed integer nonlinear programming problem. This problem was based on joint optimization of task distribution decision-making, flexible computing resource scheduling and radio resource allocation.…”

Section: Related Workmentioning

confidence: 99%

Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles

Wang

Li³

et al. 2020

IEEE Access

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With the rapid increase of vehicles, the explosive growth of data flow and the increasing shortage of spectrum resources, the performance of existing task offloading scheme is poor, and the onboard terminal can't achieve efficient computing. Therefore, this paper proposes a task offload strategy based on reinforcement learning computing in edge computing architecture of Internet of vehicles. Firstly, the system architecture of Internet of vehicles is designed. The Road Side Unit receives the vehicle data in community and transmits it to Mobile Edge Computing server for data analysis, while the control center collects all vehicle information. Then, the calculation model, communication model, interference model and privacy issues are constructed to ensure the rationality of task offloading in Internet of vehicles. Finally, the user cost function is minimized as objective function, and double-layer deep Q-network in deep reinforcement learning algorithm is used to solve the problem for real-time change of network state caused by user movement. The results show that the proposed offloading strategy can achieve fast convergence. Besides, the impact of user number, vehicle speed and MEC computing power on user cost is the least compared with other offloading schemes. The task offloading rate of our proposed strategy is the highest with better performance, which is more suitable for the scenario of Internet of vehicles.

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Dynamic Task Offloading and Resource Allocation for Mobile-Edge Computing in Dense Cloud RAN

Cited by 167 publications

References 40 publications

Joint computation offloading and resource provisioning for edge‐cloud computing environment: A machine learning‐based approach

Joint computation offloading and resource provisioning for edge‐cloud computing environment: A machine learning‐based approach

Survey of Radio Resource Management in 5G Heterogeneous Networks

Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles

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