Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing

Kruekaew, Boonhatai; Kimpan, Warangkhana

doi:10.2991/ijcis.d.200410.002

Cited by 60 publications

(33 citation statements)

References 51 publications

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“…x axis = x index ; 25. end while variants, e.g., IFFO [1], MFOA [24], and -MAFOA [16] are used to compare. Besides, three typical papulation-based algorithms are also compared with the proposed QFOA, including PSO [7], DE [4], ABC [8]. All algorithms are coded using Matlab-2009a according to their original references, and no commercial algorithm tools are used.…”

Section: Results For Function Optimizationmentioning

confidence: 99%

“…To solve the complex optimization problems, population-based intelligent optimization techniques, which usually search for the feasible solution in some random fashion with starting from the initial solution set, have emerged and show their advantages in terms of calculate precision and time complexity [4,5]. These intelligent optimization techniques are generally inspired by the biological social behavior, such as ant colony optimization (ACO) [6], particle swarm optimization (PSO) [7], artificial bee colony (ABC) optimization [8], firefly algorithm (FA) [9], etc., and the evolution process in nature, including differential evolution (DE) algorithm [4,10], genetic algorithm (GA) [11], etc., as well as the physical or chemical phenomenon, such as chemical reaction optimization (CRO) algorithm [12], gravitational search algorithm (GSA) [13], biogeographybased optimization (BBO) [14] algorithm, etc.…”

Section: Introductionmentioning

confidence: 99%

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Quantum Behavior-Based Enhanced Fruit Fly Optimization Algorithm with Application to UAV Path Planning

Zhang¹,

Xia²,

Li³

2020

IJCIS

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As a newly developed simple and effective optimization technology, the fruit fly optimization algorithm (FOA) has been successfully applied in many fields. To accelerate the algorithm convergence and avoid the local optimum, the enhanced FOA based on quantum theory called QFOA is proposed in this paper. When establishing the quantum Delta potential well around the location of fruit fly swarm, QFOA introduces the quantum behavior-based searching mechanism into the original osphresis-based search procedure of FOA. In the process that fruit flies find and move toward the food source, fruit flies follow the wave function property of the Delta potential well rather than the Newtonian mechanics. Taking advantage of the probability and uncertainty of quantum theory, the proposed QFOA can effectively overcome the weakness in premature convergence and easy trapping into local optimum. Since there are two popular models of the basic FOA, this paper also develops two corresponding QFOAs. Experimental results on various benchmark functions show that both the two QFOA models has overall better performance compared with the basic FOA as well as other FOA variants and other well-known optimization algorithms. In addition, the proposed QFOAs are also applied to unmanned aerial vehicle (UAV) path planning problem in the three-dimensional environment, and comparative results about the obtained optimal flight path and population convergence process show the effectiveness of QFOAs.

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Section: Results For Function Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantum Behavior-Based Enhanced Fruit Fly Optimization Algorithm with Application to UAV Path Planning

Zhang¹,

Xia²,

Li³

2020

IJCIS

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“…If N denotes the number of processors in a virtual machine,P denotes the million instructions per second (MIPS) for all processors, and Bdenotes the bandwidth available for communication, the capacity of a single virtual machine (Ci) can be determined as follows [12]:…”

Section: Fuzzy Host Selection Methodsmentioning

confidence: 99%

“…In [12], Kruekaew and Kimpan (2020) proposed a new method of heuristic task scheduling with ABC (HABC) by integrating the swarm intelligence algorithm with ABCand heuristic scheduling algorithms. The goal of this algorithm is to reduce makespan and achieve load balancing by providing better VM scheduling for cloud computing in both uniform and non-uniform media.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Load Balancing Approach based on the Integration of QoS and Power Consumption in Cloud Computing

2021

IJATCSE

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Over the past few years, there has been keen research interest in load balancing and task scheduling in the cloud as the extensive amount of data that is stored in the server leads to significantly increased load. This can be resolved by using a hybrid algorithm in which the honeybee behavior algorithm’s advantages are integrated with fuzzy logic to conduct task scheduling and as well as balancing in the cloud. The design of this hybrid algorithm aims to enhance prior approaches. It is developed as per ABC and merges the important QoS factors along with power consumption so that the power that virtual machines (VMs) consume on the host can be precisely assessed, thereby ensuring efficient load balancing algorithm. The present study aims to evaluate the VMs’ power consumption by taking into account crucial QoS factors for selecting which host and virtual machine will be best suited for receiving the task. CloudSim was used to simulate the ILBA_HB algorithm. In terms of makespan, average response time, and degree of imbalance, the performance of the ILBA HB algorithm is compared to that of the LBA HB and HBB-LB algorithms. According to the results, the proposed algorithm outperformed LBA_HB and HBB-LB.

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“…The previous DJS algorithms have been discussed by many researchers. All of the previous works used only continuous variables of the jobs (i.e., memory size) without considering the effect of the job's categorical variables such as Berger model, 53 particle swarm optimization, 54 energy optimization model, 55 firefly algorithm, 56 bees swarm, 57 and other models such as swarm intelligence algorithm [58][59][60] and data location aware model. 67 Moreover, the categorical variables of the jobs are used to make the job unique and special.…”

Section: Problem Statementmentioning

confidence: 99%

Scheduling of workflow jobs based on twostep clustering and lowest job weight

Al-Najjar

Alhady

Mohamad–Saleh

et al. 2021

Concurrency and Computation

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Scheduling is an important technique to improve the productivity of workflow applications in a grid system. Serving workflow applications added overhead on the scheduling system since the scheduler should choose the best zero-dependent job that improves the performance of a grid system. This paper proposes two dependent job scheduling algorithms based on analyzing the effect of categorical and continuous variables of the job, which are used in the calculation of job weight. To find the job weight, twostep clustering is used with 10 groups and the ranking equation. In addition, to verify the ability to apply the proposed algorithms in a real environment, weighted least squares estimation is used. The results showed that the prediction rate is equal to 99.88%, which indicates that the proposed algorithms could be applied in a real grid system with low overhead. Through simulations and after testing the proposed algorithms, the average results showed that the Dependent Job Scheduling (DJS) algorithm outperformed the previous algorithms, in total execution time and an average waiting time with an improvement value of 1.18 and 1.92 times, respectively. While DJS algorithm with weighting factor (DJSJP) outperformed the previous algorithms in total execution time only with an improvement value equal to 1.17 times. The overall results indicated that the proposed algorithms are efficient to be used in a grid system, besides four bits are sufficient to improve the performance of the job scheduling system.

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Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing

Cited by 60 publications

References 51 publications

Quantum Behavior-Based Enhanced Fruit Fly Optimization Algorithm with Application to UAV Path Planning

Quantum Behavior-Based Enhanced Fruit Fly Optimization Algorithm with Application to UAV Path Planning

Hybrid Load Balancing Approach based on the Integration of QoS and Power Consumption in Cloud Computing

Scheduling of workflow jobs based on twostep clustering and lowest job weight

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