A Particle Swarm Optimization With Lévy Flight for Service Caching and Task Offloading in Edge-Cloud Computing

Gao, Tieliang; Tang, Qigui; Li, Jiao; Zhang, Yi; Li, Yiqiu; Zhang, Jingya

doi:10.1109/access.2022.3192846

Cited by 17 publications

(6 citation statements)

References 28 publications

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“…In simulated EECC systems, where the simulation parameters are set referring to [18], [21], [23], [15] and reality, there are ten devices, five ES, and ten types of CS. The core number of devices, ES, and CS are set randomly in ranges of [2,8], [4,32], and [1,8], respectively.…”

Section: A Experiments Environmentmentioning

confidence: 99%

“…Therefore, some works used meta-heuristics to pursue the global best offloading solution. Both Wang et al [18] and Gao et al [23] applied PSO with same solution representation method to this paper. In addition, to improve exploration ability, Gao et al [23] used Lévy Flight movement pattern for updating particle positions.…”

Section: Related Workmentioning

confidence: 99%

“…Both Wang et al [18] and Gao et al [23] applied PSO with same solution representation method to this paper. In addition, to improve exploration ability, Gao et al [23] used Lévy Flight movement pattern for updating particle positions. Wang et al [15] used GA for optimizing user satisfaction and resource efficiency.…”

Section: Related Workmentioning

confidence: 99%

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An Improved Genetic Algorithm with Chromosome Replacement and Rescheduling for Task Offloading

Fu,

Li,

Han

et al. 2023

IJACSA

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End-Edge-Cloud Computing (EECC) has been applied in many fields, due to the increased popularity of smart devices. But the cooperation of end devices, edge and cloud resources is still challenge for improving service quality and resource efficiency in EECC. In this paper, we focus on the task offloading to address the challenge. We formulate the offloading problem as mixed integer nonlinear programming, and solve it by Genetic Algorithm (GA). In the GA-based offloading algorithm, each chromosome is the code of a offloading solution, and the evolution is to iteratively search the global best solution. To improve the performance of GA-based task offloading, we integrate two improvement schemes into the algorithm, which are the chromosome replacement and the task rescheduling, respectively. The chromosome replacement is to replace the chromosome of every individual by its better offspring after every crossing, which substitutes the selection operator for population evolution. The task rescheduling is rescheduling each rejected task to available resources, given offloading solution from every chromosome. Extensive experiments are conducted, and results show that our proposed algorithm can improve upto 32% user satisfaction, upto 12% resource efficiency, and upto 35.3% processing efficiency, compared with nine classical and up-to-date algorithms.

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Section: A Experiments Environmentmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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An Improved Genetic Algorithm with Chromosome Replacement and Rescheduling for Task Offloading

Fu,

Li,

Han

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Furthermore, meta-heuristics do not require big training data set. For instance, the work in [11] employs the simulated annealing algorithm to determine caching allocations, and the research in [12] uses particle swarm optimization. Another kind of widely-used meta-heuristics in the proactive caching decision is the genetic algorithm (GA) [13,14].…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm-based Proactive Caching Policy in Edge Networks

Yuan,

Wang,

Wang

et al. 2023

J. Phys.: Conf. Ser.

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The incredible growth in mobile traffic lays down a challenge to network design. Therefore, proactive caching technology has been proposed as an effective method to reduce network congestion and improve the quality of service. In this paper, we focus on the caching position problem in edge networks and use a mixed integer linear programming (MILP) model to provide the optimal selection. Due to the curse of dimensionality, a genetic algorithm-based caching policy is given to make a balance between performance and time complexity. Numerical investigations reveal that the proposed approach can reduce the total cost by around 20% compared with other widely-used caching methods.

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“…The introducing of the heuristic method can improve both the quality of chromosomes and the convergence velocity for GA. Different from existing related works [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], the main advantages of our work include the awareness of the heterogeneity between edge and cloud resources, providing the joint solution of the service caching and task offloading, and the integration of the dual-stage heuristic into GA.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic Algorithm for Service Caching and Task Offloading in Edge-Cloud Computing

Li¹,

Sun²,

Wang³

2022

IJACSA

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Edge-cloud computing is increasingly prevalent for Internet-of-Thing (IoT) service provisioning by combining both benefits of edge and cloud computing. In this paper, we aim to improve the user satisfaction and the resource efficiency by service caching and task offloading for edge-cloud computing. We propose a hybrid heuristic method to combine the global search ability of the genetic algorithm (GA) and heuristic local search ability, to improve the number of satisfied requests and the resource utilization. The proposed method encodes the service caching strategies into chromosomes, and evolves the population by GA. Given a caching strategy from a chromosome, our method exploits a dual-stage heuristic method for the task offloading. In the first stage, the dual-stage heuristic method pre-offloads tasks to the cloud, and offloads tasks whose requirements cannot be satisfied by the cloud to edge servers, aiming at satisfying as many tasks' requirements as possible. The second stage re-offloads tasks from the cloud to edge servers, to get the utmost out of limited edge resources. Experimental results demonstrate the competitive edges of the proposed method over multiple classical and stateof-the-art techniques. Compared with five existing scheduling algorithms, our method achieves 11.3% to 23.7% more accepted tasks and 1.86% to 18.9% higher resource utilization.

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A Particle Swarm Optimization With Lévy Flight for Service Caching and Task Offloading in Edge-Cloud Computing

Cited by 17 publications

References 28 publications

An Improved Genetic Algorithm with Chromosome Replacement and Rescheduling for Task Offloading

An Improved Genetic Algorithm with Chromosome Replacement and Rescheduling for Task Offloading

A Genetic Algorithm-based Proactive Caching Policy in Edge Networks

A Hybrid Genetic Algorithm for Service Caching and Task Offloading in Edge-Cloud Computing

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