A multi-objective task offloading based on BBO algorithm under deadline constrain in mobile edge computing

Li, Hongjian; Zheng, Pengwu; Wang, Tiantian; Wang, Jingjing; Liu, Tongming

doi:10.1007/s10586-022-03809-7

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…Gao et al [3] proposed a cloud-edge cooperative architecture based on Lyapunov optimization theory, which reduces the problem to a constrained optimization problem by establishing a dynamic queueing model of cloud computing servers and edge computing servers, and combining the system power function to form a drift plus penalty function framework. Li et al [4] proposed a multi-objective strategy based on Biogeographic Optimization (BBO) algorithm and constructs a time-energy model and a cost model for task offloading, based on which the BBO algorithm is introduced into task offloading for MEC to solve the multi-objective optimization problem. Zhang et al [5] proposed a multi-user, multi-computing task offloading scheme based on mobile cloud computing and mobile edge computing that minimizes energy consumption and latency through differential evolutionary algorithms and provides optimal computational task offloading decisions.…”

Section: Task Offloading Relatedmentioning

confidence: 99%

“…Yilin et al [13] carried out in-depth research on the research progress of computational offloading in mobile edge computing, summarizes and generalizes two types of traditional task methods and intelligent methods based on online learning, and analyzes and compares the traditional computational offloading based on heuristic algorithms from the minimization of latency time, minimization of energy consumption, and trade-off between time and energy consumption with three different optimization objectives. Li et al [14] proposed a new multi-objective strategy based on the biogeography-based optimization (BBO) algorithm. In this strategy, a time-energy consumption model and a cost model are constructed for task offloading firstly.…”

Section: Task Offloading Relatedmentioning

confidence: 99%

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SRA-E-ABCO: Terminal Task Offloading for Cloud-Edge-End Environments

Jiao,

Wang,

Luo

2024

Preprint

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With the rapid development of Internet technology, the cloud-edge-end computing model has gradually become an essential new computing model. Under this model, terminal task offloading is crucial to task allocation and processing. Existing terminal task offloading solutions mainly focus on optimizing the offloading strategy to minimize system delay, transmission energy consumption, and computation energy cost. However, there are some problems such as the lack of a basis for judging whether to offload or not, the lack of analysis on the attributes of the terminal devices and the edge nodes, and the lack of optimization of load balancing. To address these issues, a Service Reliability Analysis and Elite-Artificial Bee Colony Offloading model (SRA-E-ABCO) is proposed for cloud-edge-end environments. Specifically, a Service Reliability Analysis (SRA) method is proposed to assist in predicting the offloading necessity of terminal tasks and analyzing the attributes of terminal devices and edge nodes. Subsequently, a set of vectors is derived based on the analysis. An Elite Artificial Bee Colony Offloading (E-ABCO) method is proposed, which optimizes offloading decisions by combining elite populations with improved fitness formulas, position update formulas, and population initialization methods. Experiments show that the proposed model has better performance in convergence, delay and energy consumption.

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Section: Task Offloading Relatedmentioning

confidence: 99%

Section: Task Offloading Relatedmentioning

confidence: 99%

SRA-E-ABCO: Terminal Task Offloading for Cloud-Edge-End Environments

Jiao,

Wang,

Luo

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…To enhance data processing and privacy protection in IIoT systems, empowering access points with mobile edge computing (MEC) capability is under consideration [4], [5]. MEC enables low-latency operations by extending cloud computing and services to the network edge [6]- [10]. It also enhances the ability to protect private data, as data processing occurs exclusively within the plant's network.…”

Section: A Backgroundmentioning

confidence: 99%

Computation Rate Maximization for Wireless-Powered Edge Computing With Multi-User Cooperation

Li,

Zhang,

Lei

et al. 2024

IEEE Open J. Commun. Soc.

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The combination of mobile edge computing (MEC) and radio frequency-based wireless power transfer (WPT) presents a promising technique for providing sustainable energy supply and computing services at the network edge. This study considers a wireless-powered mobile edge computing system that includes a hybrid access point (HAP) equipped with a computing unit and multiple Internet of Things (IoT) devices. In particular, we propose a novel muti-user cooperation scheme to improve computation performance, where collaborative clusters are dynamically formed. Each collaborative cluster comprises a source device (SD) and an auxiliary device (AD), where the SD can partition the computation task into various segments for local processing, offloading to the HAP, and remote execution by the AD with the assistance of the HAP. Specifically, we aims to maximize the weighted sum computation rate (WSCR) of all the IoT devices in the network. This involves jointly optimizing collaboration, time and data allocation among multiple IoT devices and the HAP, while considering the energy causality property and the minimum data processing requirement of each device. Initially, an optimization algorithm based on the interior-point method is designed for time and data allocation. Subsequently, a priority-based iterative algorithm is developed to search for a near-optimal solution to the multi-user collaboration scheme. Finally, a deep learning-based approach is devised to further accelerate the algorithm's operation, building upon the initial two algorithms. Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.INDEX TERMS Mobile edge computing, multi-user collaboration, wireless power transfer, time and data allocation, deep learning.

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“…DL can be an accurate tool for making task offloading decisions, based on the resource usage of the processing Edge nodes, the workload, and the quality of services (QoS) constraints defined in the Service Level Agreement (SLA) [41]. In [42], a new multi-objective strategy based on biogeographybased optimization (BBO) algorithm for Mobile Edge Computing (MEC) offloading was proposed to satisfy multiple user requirements (execution time, power consumption, energy, and cost). In [43], a task offloading model based on dynamic priority adjustment was proposed.…”

Section: Introductionmentioning

confidence: 99%

Task Offloading in Edge Computing Using GNNs and DQN

Garmendia-Orbegozo,

Nunez-Gonzalez,

Anton

2024

CMES

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In a network environment composed of different types of computing centers that can be divided into different layers (clod, edge layer, and others), the interconnection between them offers the possibility of peer-to-peer task offloading. For many resource-constrained devices, the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity. In this scenario, it is worth considering transferring these tasks to resource-rich platforms, such as Edge Data Centers or remote cloud servers. For different reasons, it is more exciting and appropriate to download various tasks to specific download destinations depending on the properties and state of the environment and the nature of the functions. At the same time, establishing an optimal offloading policy, which ensures that all tasks are executed within the required latency and avoids excessive workload on specific computing centers is not easy. This study presents two alternatives to solve the offloading decision paradigm by introducing two well-known algorithms, Graph Neural Networks (GNN) and Deep Q-Network (DQN). It applies the alternatives on a well-known Edge Computing simulator called PureEdgeSim and compares them with the two default methods, Trade-Off and Round Robin. Experiments showed that variants offer a slight improvement in task success rate and workload distribution. In terms of energy efficiency, they provided similar results. Finally, the success rates of different computing centers are tested, and the lack of capacity of remote cloud servers to respond to applications in real-time is demonstrated. These novel ways of finding a download strategy in a local networking environment are unique as they emulate the state and structure of the environment innovatively, considering the quality of its connections and constant updates. The download score defined in this research is a crucial feature for determining the quality of a download path in the GNN training process and has not previously been proposed. Simultaneously, the suitability of Reinforcement Learning (RL) techniques is demonstrated due to the dynamism of the network environment, considering all the key factors that affect the decision to offload a given task, including the actual state of all devices.

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A multi-objective task offloading based on BBO algorithm under deadline constrain in mobile edge computing

Cited by 7 publications

References 38 publications

SRA-E-ABCO: Terminal Task Offloading for Cloud-Edge-End Environments

SRA-E-ABCO: Terminal Task Offloading for Cloud-Edge-End Environments

Computation Rate Maximization for Wireless-Powered Edge Computing With Multi-User Cooperation

Task Offloading in Edge Computing Using GNNs and DQN

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