Energy-saving scheduling on IaaS HPC cloud environments based on a multi-objective genetic algorithm

Vila, Sergi; Guirado, Fernando; Lérida, Josep L.; Cores, Fernando

doi:10.1007/s11227-018-2668-z

Cited by 28 publications

(11 citation statements)

References 15 publications

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“…Among them, the MD and MP values obtained by the RAA-PI-NSGAII algorithm are smaller than those of the SPEA2 and NSGA-II algorithms, which demonstrates that the RAA-PI-NSGAII algorithm is more effective than other algorithms in regard to matching the optimal physical resources for VMs and reducing resource fragments. Figures 7,8,9 show the effectiveness of different algorithms in regard to improving resource utilization. It is noted that the curves of NSGA-II and RAA-PI-NSGAII methods are offset by 2 units along the Y-axis to make them clear.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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A multi-objective optimization for resource allocation of emergent demands in cloud computing

Jing

Xiao

2021

J Cloud Comp

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Cloud resource demands, especially some unclear and emergent resource demands, are growing rapidly with the development of cloud computing, big data and artificial intelligence. The traditional cloud resource allocation methods do not support the emergent mode in guaranteeing the timeliness and optimization of resource allocation. This paper proposes a resource allocation algorithm for emergent demands in cloud computing. After building the priority of resource allocation and the matching distances of resource performance and resource proportion to respond to emergent resource demands, a multi-objective optimization model of cloud resource allocation is established based on the minimum number of the physical servers used and the minimum matching distances of resource performance and resource proportion. Then, an improved evolutionary algorithm, RAA-PI-NSGAII, is presented to solve the multi-objective optimization model, which not only improves the quality and distribution uniformity of the solution set but also accelerates the solving speed. The experimental results show that our algorithm can not only allocate resources quickly and optimally for emergent demands but also balance the utilization of all kinds of resources.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…Another classical method of cloud resource allocation is to model VM placement as a multi-objective optimization mathematical problem [9][10][11]. The main idea is to express a cloud resource allocation problem as a multi-objective mathematical function, and then to use a multi-objective evolutionary algorithm to solve it.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective optimization for resource allocation of emergent demands in cloud computing

Jing

Xiao

2021

J Cloud Comp

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“…Various GA-based scheduling solutions have been proposed in the past, either by applying standard GA method [36], or using improved GA approaches by modifying traditional mutation and crossover operations, and hybrid GA solutions, which integrate traditional GA with other methods [37,38]. Basic versions of SOS and certain modifications to SOS algorithms using chaotic maps and opposition-based learning have been employed by many researchers to achieve significant scheduling performance over stateof-the-art heuristics [39,40].…”

Section: Related Workmentioning

confidence: 99%

“…Initially, the resource allocation dimension (X A ) is represented by VM availability matrix (AM) [38,57,58] consisting of real-value entries representing a BoT application-vmType pair. Each pair entry indicates the fixed percentage of availability of VMs of a particular vmType for allocation to the BoT application.…”

Section: Discretization Of Resource Allocation Vectormentioning

confidence: 99%

“…X *A vector, i.e., the actual discrete allocation of VMs to tasks of BoT application, is obtained by applying the MDeRA (Modified Discrete and Efficient Resource Allocation) heuristic on AM matrix and X *O vector, as shown in the Figures 4 and 5b. MDeRA heuristic (pseudo-code in the Algorithm 2) is an improved version of the published work DeRA heuristic [13] and allocation heuristic [38]. Instead of random BoTs execution-order in the DeRA heuristic, MDeRA initially sorts the BoTs in the increasing order of their expected execution time (Line 1).…”

Section: Discretization Of Resource Allocation Vectormentioning

confidence: 99%

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Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

et al. 2022

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Bag-of-Tasks (BoT) scheduling over cloud computing resources called Cloud Bag-of-Tasks Scheduling (CBS) problem, which is a well-known NP-hard optimization problem. Whale Optimization Algorithm (WOA) is an effective method for CBS problems, which still requires further improvement in exploration ability, solution diversity, convergence speed, and ensuring adequate exploration–exploitation tradeoff to produce superior scheduling solutions. In order to remove WOA limitations, a hybrid oppositional differential evolution-enabled WOA (called h-DEWOA) approach is introduced to tackle CBS problems to minimize workload makespan and energy consumption. The proposed h-DEWOA incorporates chaotic maps, opposition-based learning (OBL), differential evolution (DE), and a fitness-based balancing mechanism into the standard WOA method, resulting in enhanced exploration, faster convergence, and adequate exploration–exploitation tradeoff throughout the algorithm execution. Besides this, an efficient allocation heuristic is added to the h-DEWOA method to improve resource assignment. CEA-Curie and HPC2N real cloud workloads are used for performance evaluation of scheduling algorithms using the CloudSim simulator. Two series of experiments have been conducted for performance comparison: one with WOA-based heuristics and another with non-WOA-based metaheuristics. Experimental results of the first series of experiments reveal that the h-DEWOA approach results in makespan improvement in the range of 5.79–13.38% (for CEA-Curie workloads), 5.03–13.80% (for HPC2N workloads), and energy consumption in the range of 3.21–14.70% (for CEA-Curie workloads) and 10.84–19.30% (for HPC2N workloads) over well-known WOA-based metaheuristics. Similarly, h-DEWOA also resulted in significant performance in comparison with recent state-of-the-art non-WOA-based metaheuristics in the second series of experiments. Statistical tests and box plots also revealed the robustness of the proposed h-DEWOA algorithm.

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Task Scheduling in Cloud Using Improved ANT Colony Algorithm

Pabboju¹,

Adilakshmi²

2022

Smart Intelligent Computing and Applications, Volume 1

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Energy-saving scheduling on IaaS HPC cloud environments based on a multi-objective genetic algorithm

Cited by 28 publications

References 15 publications

A multi-objective optimization for resource allocation of emergent demands in cloud computing

A multi-objective optimization for resource allocation of emergent demands in cloud computing

Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

Task Scheduling in Cloud Using Improved ANT Colony Algorithm

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