Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment

Tong, Zhao; Deng, Xiaomei; Ye, Feng; Basodi, Sunitha; Xiao, Xueli; Pan, Yi

doi:10.1016/j.ins.2020.05.057

Cited by 80 publications

(20 citation statements)

References 35 publications

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“…In this section, we discuss the results of our simulations, the simulation setup, the dataset used for the simulations, and analysis of our results. Our setup is mainly inspired from the previous work [7,8]. We consider = 300 mobile users distributed uniformly at random with = 12 on a grid network of 300 × 300 area.…”

Section: Resultsmentioning

confidence: 99%

“…This section includes the related research on task offloading and resource allocation in the MEC systems. In the task offloading, the task is assigned to the appropriate MEC server to be executed, which can results in minimum energy consumption of data transmission and thus increase the overall system utility [8]. A cooperative game-based job scheduling [9] is used to obtain the criteria of maximum job execution for meeting the target deadlines in MEC model.…”

Section: Related Workmentioning

confidence: 99%

“…An online approach is proposed to solve the quality-of-service (QoS) task offloading problem and predict user paths in realtime. Machine learning methods like Deep Reinforcement Learning (DRL) based schemes with Q-learning models like DDQN and DRL have been efficiently utilized for task optimization and resource allocation in multi-user MEC systems [8], [30]- [35]. An adaptive task offloading and resource allocation approach based on DRL is given in [8], it decides about the task offloading and allocation of computational resources for these tasks.…”

Section: Related Workmentioning

confidence: 99%

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A User-Centric QoS-Aware Multi-Path Service Provisioning in Mobile Edge Computing

et al. 2021

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Recent development in modern wireless applications and services, such as augmented reality, image processing, and network gaming requires persistent computing on average commercial wireless devices to perform complex tasks with low latency. The traditional cloud systems are unable to meet those requirements solely. In the said perspective, Mobile Edge Computing (MEC) serves as a proxy between the things (devices) and the cloud, pushing the computations at the edge of the network. The MEC provides an effective solution to fulfill the demands of low-latency applications and services by executing most of the tasks within the proximity of users. The main challenge, however, is that too many simultaneous service requests created by wireless access produce severe interference, resulting in a decreased rate of data transmission. In this paper, we made an attempt to overcome the aforesaid limitation by proposing a usercentric QoS-aware multi-path service provisioning approach. A densely deployed base station MEC environment has overlapping coverage regions. We exploit such regions to distribute the service requests in a way that avoid hotspots and bottlenecks. Our approach is adaptive and can tune to different parameters based on service requirements. We performed several experiments to evaluate the effectiveness of our approach and compared it with the traditional Greedy approach. The results revealed that our approach improves the network state by 26.95% and average waiting time by 35.56% as compared to the Greedy approach. In addition, the QoS violations were also reduced by the fraction of 16.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A User-Centric QoS-Aware Multi-Path Service Provisioning in Mobile Edge Computing

et al. 2021

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“…However, the above literature failed to allocate the limited wireless and computing resources reasonably. Tong et al [ 18 ] proposed an adaptive task offloading and resource allocation algorithm in MEC environment. The algorithm used the Deep Reinforcement Learning (DRL) method to determine whether the task needs to be offloaded and allocate computing resources for the task.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Computation Offloading Strategy with Mobile Edge Computing for IoT

Fang

Shi

et al. 2021

Micromachines

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With the rapidly development of mobile cloud computing (MCC), the Internet of Things (IoT), and artificial intelligence (AI), user equipment (UEs) are facing explosive growth. In order to effectively solve the problem that UEs may face with insufficient capacity when dealing with computationally intensive and delay sensitive applications, we take Mobile Edge Computing (MEC) of the IoT as the starting point and study the computation offloading strategy of UEs. First, we model the application generated by UEs as a directed acyclic graph (DAG) to achieve fine-grained task offloading scheduling, which makes the parallel processing of tasks possible and speeds up the execution efficiency. Then, we propose a multi-population cooperative elite algorithm (MCE-GA) based on the standard genetic algorithm, which can solve the offloading problem for tasks with dependency in MEC to minimize the execution delay and energy consumption of applications. Experimental results show that MCE-GA has better performance compared to the baseline algorithms. To be specific, the overhead reduction by MCE-GA can be up to 72.4%, 38.6%, and 19.3%, respectively, which proves the effectiveness and reliability of MCE-GA.

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“…Therefore, it would be a much more feasible solution to deploy certain parts of a deep learning model to the IoT edge device in an effort to assist the cloud in the entire continuous learning process instead of relying completely on the cloud [19]. When a given deep learning model is trained in a distributed manner between two or more devices, this will present certain challenges such as deciding how many and which specific layers of a model must be run on the edge and the cloud (this is known as offloading [20][21][22][23]), dealing with the transmission load between the IoT edge device and the cloud [24] and evaluating whether it is important for the IoT edge device to transmit all of the data to the cloud for model training or whether some of the data be discarded [25]. These challenges have not yet been addressed in a distributed incremental learning scenario which is what this paper attempts to do.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of IoT Stream Sampling and Model Update on the IoT Edge Device for Class Incremental Learning in an Edge-Cloud System

2021

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With the exponential rise of the number of IoT devices, the amount of data being produced is massive. Thus, it is unfeasible to send all the raw data directly to the cloud for processing, especially for data that is high dimensional. Training deep learning models incrementally evolves the model over time and eliminates the need to statically training the models with all the data. However, the integration of class incremental learning and the Internet of Things (IoT) is a new concept and is not yet mature. In the context of IoT and deep learning, the transmission cost of data in the edge-cloud architecture is a challenge. We demonstrate a novel sample selection method that discards certain training images on the IoT edge device that reduces transmission cost and still maintains class incremental learning performance. It can be unfeasible to transmit all parameters of a trained model back to the IoT edge device. Therefore, we propose an algorithm to find only the useful parameters of a trained model in an efficient way to reduce the transmission cost from the cloud to the edge devices. Results show that our proposed methods can effectively perform class-incremental learning in an edge-cloud setting.

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Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment

Cited by 80 publications

References 35 publications

A User-Centric QoS-Aware Multi-Path Service Provisioning in Mobile Edge Computing

A User-Centric QoS-Aware Multi-Path Service Provisioning in Mobile Edge Computing

An Efficient Computation Offloading Strategy with Mobile Edge Computing for IoT

A Novel Approach of IoT Stream Sampling and Model Update on the IoT Edge Device for Class Incremental Learning in an Edge-Cloud System

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