Off-Line Time Aware Scheduling of Bag-of-Tasks on Heterogeneous Distributed System

Xuan, Hejun; Wei, Shiwei; Li, Yanling; Guo, Huaping

doi:10.1109/access.2019.2899926

Cited by 11 publications

(9 citation statements)

References 46 publications

(58 reference statements)

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“…Genetic algorithms invented by John Holland [20], have been proven to be an effective technique for many hard problems such as production-distribution planning problems [21], transportation and network design [45], Scientific workflow scheduling [43], task scheduling in cloud computing [34], [44], topology or size optimization [24]. However, it is not suitable to directly apply the aforementioned algorithms to the problems of VONs mapping in EONs, and it is necessary to make some improvements or revisions on them.…”

Section: B Bi-level Mathematical Problem and Genetic Algorithmmentioning

confidence: 99%

A New Bi-Level Mathematical Model and Algorithm for VONs Mapping Problem

et al. 2020

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Elastic optical networks (EONs) virtualization can allow the virtual optical networks (VONs) to utilize all the physical resources of EONs, and can attain a high performance of the networks. However, the optimal scheme for VONs mapping should be determined. To tackle these challenges, a bi-level mathematical model is established. leader's and follower's objectives are to minimize energy consumption and the maximum index of used frequency slots, respectively. The bi-level mathematical model can determine the optimal schemes of VONs mapping. To solve the mathematical model effectively, a uniform design method is applied to generate initial population for the lower level problem. In addition, To solve the whole model effectively, a tailor-made encoding, population initialization, genetic operators and local search operator are designed. An efficient genetic algorithm with local search operator is proposed for the bilevel mathematical model. To evaluate the mathematical model and the designed algorithm, a large number of experiments are performed on three kinds of the widely used networks, and the experimental results indicate that the effectiveness of the proposed bi-level mathematical model and designed algorithms.

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Section: B Bi-level Mathematical Problem and Genetic Algorithmmentioning

confidence: 99%

A New Bi-Level Mathematical Model and Algorithm for VONs Mapping Problem

et al. 2020

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“…Memetic algorithm, which adds the local search operator to genetic algorithm, has been proven to be an effective technique for many hard problems such as production-distribution planning problems [15], transportation and network design [45], Scientific workflow scheduling [28], [30], [48], task scheduling in cloud computing [39], [42], [43]. There are some conceptions, including encoding, decoding, individual, population, crossover operator, mutation operator and local search operator, fitness function etc.…”

Section: B Memetic Algorithmmentioning

confidence: 99%

“…To construct uniform design array, many methods are presented [16], [18], [24], [42]. Not only simple but also efficient method proposed in [24].…”

Section: Overview Of Uniform Designmentioning

confidence: 99%

Bi-Level Programming Model and Algorithm for VNF Deployment With Data Centers Placement

et al. 2019

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Virtual network function (VNF) can provide various network services and is widely deployed in inter-data centers elastic optical networks (Inter-DC EONs). Routing and VNF deployment for VNF service chain (VNF-SC) in Inter-DC EONs is a very important and well-known NP-hard problem. For this problem, if determining the number and locations of data centers is additionally considered, it will be more complicated. In this paper, we investigate a network planning problem in Inter-DC EONs by determining all these factors, i.e, by determining not only the optimal routing and the optimal VNF deployment for VNF-SCs, but also the optimal number and locations of data centers. To achieve this purpose, we first establish a bi-level programming model in which the leader's objective is to minimize the number of data centers and find the best locations of data centers so that we can get a balanced VNF deployment on data centers. To determine the optimal routing and VNF deployment for VNF-SCs, the follower's objective is to minimize the maximum index of used frequency slots and the number of used frequency slots. Then, to solve the proposed model effectively, tailor-made crossover, mutation and local search operators are designed, and based on these operators, an efficient bi-level hybrid memetic algorithm (BiHMA) is proposed. Finally, to test the effectiveness of the proposed model and the efficiency of the proposed algorithm, the simulation experiments are conducted on two widely used networks, and experimental results indicate that the proposed algorithm has a higher efficiency than compared algorithms.INDEX TERMS EONs, data centers placement, bi-level optimization, Memetic algorithm.

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“…To generate points to be uniformly distributed on the experimental domain, a uniform design method was developed. It generates a small number of the uniformly distributed representative points in a domain by using a uniform array U(S, H) = [U i,j ] H×S , where U i,j denotes the level of the j-th factor in the i-th combination with the j-th factor representing the j-th variable and its level being its value [24,25].…”

Section: Population Initializationmentioning

confidence: 99%

Grey Wolf Algorithm and Multi-Objective Model for the Manycast RSA Problem in EONs

et al. 2019

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Manycast routing and spectrum assignment (RSA) in elastic optical networks (EONs) has become a hot research field. In this paper, the mathematical model and high efficient algorithm to solve this challenging problem in EONs is investigated. First, a multi-objective optimization model, which minimizes network power consumption, the total occupied spectrum, and the maximum index of used frequency spectrum, is established. To handle this multi-objective optimization model, we integrate these three objectives into one by using a weighted sum strategy. To make the population distributed on the search domain uniformly, a uniform design method was developed. Based on this, an improved grey wolf optimization method (IGWO), which was inspired by PSO (Particle Swarm Optimization, PSO) and DE (Differential Evolution, DE), is proposed to solve the maximum model efficiently. To demonstrate high performance of the designed algorithm, a series of experiments are conducted using several different experimental scenes. Experimental results indicate that the proposed algorithm can obtain better results than the compared algorithm.

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Off-Line Time Aware Scheduling of Bag-of-Tasks on Heterogeneous Distributed System

Cited by 11 publications

References 46 publications

A New Bi-Level Mathematical Model and Algorithm for VONs Mapping Problem

A New Bi-Level Mathematical Model and Algorithm for VONs Mapping Problem

Bi-Level Programming Model and Algorithm for VNF Deployment With Data Centers Placement

Grey Wolf Algorithm and Multi-Objective Model for the Manycast RSA Problem in EONs

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