Profit-driven Computation Offloading for Mobile Edge Computing in Wireless Metropolitan Area Networks

Huang, Hualong; Peng, Kai; Xu, Xiaolong

doi:10.1109/hpcc-smartcity-dss50907.2020.00041

Cited by 3 publications

(25 citation statements)

References 19 publications

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“…In [13], the authors formulated a multi-objective optimization problem for MEC, taking into consideration user satisfaction, networks operators profit and resource utilization of cloudlets. They designed an algorithm based on MOEA/D (multiobjective evolutionary algorithm based on decomposition) [14] and compared its performance with NSGA-II (Nondominated Sorting Genetic Algorithm II) algorithm [15].…”

Section: Related Workmentioning

confidence: 99%

“…The above optimization problem is a mixed integer non-linear program which belongs to the class of NP hard problem in general. However, as suggested in [19], if we separate the offloading strategy from the resource allocation problem then (13) reduces to a simple linear program problem. So, we at first try to find an approximately optimal offloading matrix A using evolution based methods and then use it as an input to the above optimization problem to find the optimal b * and c * .…”

Section: Profit Functionmentioning

confidence: 99%

“…GA is characterised by binary representation of individuals, fitness proportionate selection, low mutation probability and genetically inspired recombinations. In [19], the authors outlined a spectrum0efficiency-based joint optimization of radio and computational resources strategy for deciding the offloading strategy (SJOORA) to solve (13). Here, we propose a genetic algorithm customized to the problem scenario under consideration to find an approximately optimal offloading strategy and compare its performance with the SJOORA algorithm.…”

Section: Genetic Algorithm Based Offloading Strategymentioning

confidence: 99%

“…Normally, while applying genetic algorithm, where we need to test a random population of large number of candidate solutions, it is preferable that the fitness/objective function be easily computed for these solutions. However, the objective function in (13) is computationally expensive to calculate, even when a randomly generated offloading strategy A is fed as input. It may take a long time to compute the optimal value of the optimization problem (13) many times, if one uses a regular genetic algorithm to find the best possible offloading strategy.…”

Section: Genetic Algorithm Based Offloading Strategymentioning

confidence: 99%

“…To compare with our proposed fast genetic algorithm, as a baseline, we first describe a usual application of genetic algorithm to (13) and call it Simple Genetic Algorithm (SGA).…”

Section: Genetic Algorithm Based Offloading Strategymentioning

confidence: 99%

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Joint Optimization of Computation Offloading and Resource Allocation in C-RAN with Multi-Access Edge Computing using Evolutionary Algorithms

Singh¹

2022

Preprint

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The Mobile Edge computing (now known as multi-access edge computing) has been widely recognized as a key enabler for new latency sensitive applications on resource constrained mobile devices. The objective to offload a computationally intensive task to a cloud server has been extensively researched over the years. These research endeavours have been in general aimed at reducing system's energy consumption and/or latency. In this paper, we attempt to examine how profitable computation offloading is from a service provider's perspective. The joint optimization of radio and computational resources along with offloading decisions result in a mixed integer non-linear optimization problem which belong to the class of NP hard problems. To counter this challenge, we decouple the offloading decision from the resource allocation problem. At first, an approximate optimal offloading decisions are found using evolutionary algorithms like genetic algorithm and binary particle swarm optimization algorithm where the objective function is approximately calculated using machine learning based model rather than actually solving the optimization problem. Then the combination of selected mobile terminals for offloading further goes as input to resource allocation optimization problem. According to the simulations performed, the proposed evolutionary algorithms outperform spectrum efficiency based offloading algorithm in terms of profitability and have relatively lower execution times. Also, the impact of resource availability on profitability of offloading has been examined. <br>

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Section: Related Workmentioning

confidence: 99%

Section: Profit Functionmentioning

confidence: 99%

Section: Genetic Algorithm Based Offloading Strategymentioning

confidence: 99%

Section: Genetic Algorithm Based Offloading Strategymentioning

confidence: 99%

“…To compare with our proposed fast genetic algorithm, as a baseline, we first describe a usual application of genetic algorithm to (13) and call it Simple Genetic Algorithm (SGA).…”

Section: Genetic Algorithm Based Offloading Strategymentioning

confidence: 99%

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Joint Optimization of Computation Offloading and Resource Allocation in C-RAN with Multi-Access Edge Computing using Evolutionary Algorithms

Singh¹

2022

Preprint

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Joint Optimization of Computation Offloading and Resource Allocation in C-RAN With Mobile Edge Computing Using Evolutionary Algorithms

Singh,

Kim

2023

IEEE Access

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Mobile Edge Computing has been widely recognized as a key enabler for new latencysensitive applications on resource constrained mobile devices. The objective to offload a computationally intensive task to a cloud server is in general intended to reduce the system's energy consumption and/or latency. In this paper, we attempt to examine how profitable computation offloading is from a service provider's perspective. The joint optimization of radio and computing resources along with offloading decisions results in a mixed integer nonlinear optimization problem which belongs to the class of NPhard problems. To counter this challenge, we decouple the offloading decision from the resource allocation problem. Initially, approximately optimal offloading decisions are determined using evolutionary algorithms such as genetic algorithms and binary particle swarm optimization algorithms. After several iterations of the evolutionary process to make offloading decisions, the optimal solution is ultimately obtained that performs resource allocation based on exact calculation of the profit value. For faster execution of the evolutionary algorithm, instead of using an optimization solver to find the exact solution, we use a novel approach to seeding the initial population and a regression-based machine learning method to predict the optimal resource allocation values to minimize the objective function evaluation time. According to the simulations performed as part of this study, the proposed evolutionary algorithms outperform existing spectral efficiency-based offloading algorithm in terms of profitability, with shorter execution times as well. The effects of resource availability and the parameters of the algorithm on the profitability of offloading are also examined.

show abstract

Joint Optimization of Computation Offloading and Resource Allocation in C-RAN with Multi-Access Edge Computing using Evolutionary Algorithms

Singh¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

Profit-driven Computation Offloading for Mobile Edge Computing in Wireless Metropolitan Area Networks

Cited by 3 publications

References 19 publications

Joint Optimization of Computation Offloading and Resource Allocation in C-RAN with Multi-Access Edge Computing using Evolutionary Algorithms

Joint Optimization of Computation Offloading and Resource Allocation in C-RAN with Multi-Access Edge Computing using Evolutionary Algorithms

Joint Optimization of Computation Offloading and Resource Allocation in C-RAN With Mobile Edge Computing Using Evolutionary Algorithms

Joint Optimization of Computation Offloading and Resource Allocation in C-RAN with Multi-Access Edge Computing using Evolutionary Algorithms

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