Optimal dynamic resource allocation to prevent defaults

Ayesta, Urtzi; Erausquin, Martin; Ferreira, Eva; Jacko, Peter

doi:10.1016/j.orl.2016.04.008

Cited by 12 publications

(5 citation statements)

References 18 publications

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“…Ayesta et al used the gap analysis framework as a tool to find out the gaps in human resource allocation in California school districts. After literature analysis and comparison, qualitative research uses formative evaluation methods to simulate the practice of manpower allocation [10]. Witteveen et al used the genetic algorithm (GA) to solve the problem of human resource allocation.…”

Section: Related Workmentioning

confidence: 99%

An Analysis of the Optimal Allocation of Core Human Resources in Family Enterprises Based on the Markov Model

Cao

2022

Journal of Mathematics

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As an important component of family business management, human resource allocation greatly affects all aspects of the company’s strategic direction and organizational structure. Therefore, the optimal allocation of core human resources in family businesses has become a topic of great practical significance. Based on the Markov model, this article is based on the B/S architecture, designed the three-tier architecture of the presentation layer, business logic layer, and data layer adopted by the system, forming the basis of quality management, job management, performance management, and salary management-oriented human resources optimization configuration system. Taking family business A as an example, the Markov chain is used to predict the flow of personnel in the company in the next three years. The results show that with the development of normalization and the improvement of the management level, the demand for personnel in the family business will decrease year by year. Competency analysis of human resources positions found that grassroots employees have higher requirements for competency, but at the same time, lower performance requirements, while company seniors need relatively low requirements for competence indicators and higher requirements for performance. In addition, through data calculation and prediction, it is found that the prediction accuracy of the model algorithm is 81.37% on average. Compared with other model algorithms, the Markov model algorithm has the highest accuracy in predicting the state, and the accurate prediction results make the recruitment work predictable. The system makes up for the shortcomings of traditional human resource management data, such as insufficient data, low accuracy, and inability to achieve homogeneity. It uses a centralized database to comprehensively and organically link human resource management-related information to provide a basis for future family business human resource optimization.

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Section: Related Workmentioning

confidence: 99%

An Analysis of the Optimal Allocation of Core Human Resources in Family Enterprises Based on the Markov Model

Cao

2022

Journal of Mathematics

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“…For resource allocation in distributed scheduling, Xu et al [5] present a nondominated sorting genetic algorithm-based multi-objective method (NSGA-II) [10]. They aimed at minimizing the time and cost in load balancing using resources to achieve Pareto optimal front.…”

Section: Related Workmentioning

confidence: 99%

“…Several methods are available for a multi-objective scheduling problem [4]. The current methods of allocation of resources, such as FIFO [1] and Round-Robin [5] which are used in the cloud, do unfair allocation regardless of priority between tasks.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Resource Allocation in Cloud Computing

Mousavi¹,

Mosavi²,

Varkonyi-Koczy³

et al. 2017

ACTA POLYTECH HUNG

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Utilizing dynamic resource allocation for load balancing is considered as an important optimization process in cloud computing. In order to achieve maximum resource efficiency and scalability in a speedy manner this process is concerned with multiple objectives for an effective distribution of loads among virtual machines. In this realm, exploring new algorithms, as well as development of novel algorithms, is highly desired for technological advancement and continued progress in resource allocation application in cloud computing. Accordingly, this paper explores the application of two relatively new optimization algorithms and further proposes a hybrid algorithm for load balancing which can contribute well in maximizing the throughput of the cloud provider's network. The proposed algorithm is a hybrid of teaching-learning-based optimization algorithm (TLBO) and grey wolves optimization algorithm (GW). The hybrid algorithm performs more efficiently than utilizing every single one of these algorithms. Furthermore, it well balances the priorities and effectively considers load balancing based on time, cost, and avoidance of local optimum traps, which consequently leads to minimal amount of waiting time. To evaluate the effectiveness of the proposed algorithm, a comparison with the TLBO and GW algorithms is conducted and the experimental results are presented.

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“…The main objective of the allocation techniques is to minimize the waiting time and execution time of a submitted job to minimize allocation cost. Popular allocation schemes like FIFO [2] and Round-Robin [7] are implemented in the data centers. However, these techniques are not able to allocate resources (virtual machines) efficiently regardless of task priority [8].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic Algorithm

Singhal,

Gupta,

Berwal

et al. 2023

IEEE Access

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Utility-based computing popularly known as "cloud computing" offers several computing services to the users. Due to the proliferation in the users of cloud computing, there is an unprecedented increase in the demand for computation resources to execute cloud services. Thus, there is a requirement to investigate currently available resources like virtual machines, CPU, RAM, and storage to allocate cloud services. The allocation and QoS of cloud services are highly dependent on allocation schemes. The optimized solutions allocate resources to submitted jobs to reduce the overall cost to the end-users/service provider without degrading the performance of virtual machines. The allocation techniques also consider the harvesting of energy consumption required for running the cloud services. In this paper, we have utilized a Rock Hyrax-based optimization technique to allocate resources to the submitted jobs with reduced energy consumption. The proposed Rock Hyrax algorithm has been simulated on the CloudSim simulator for various scenarios. The performance of the proposed algorithm has been measured over various Quality of Service (QoS) parameters such as makespan, energy efficiency, response time, throughput, and cost. The gathered results validate the proposed algorithm that improves the QoS parameters by 3%-8% as compared to algorithms when both jobs and resources are considered to be dynamic in nature.

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Optimal dynamic resource allocation to prevent defaults

Cited by 12 publications

References 18 publications

An Analysis of the Optimal Allocation of Core Human Resources in Family Enterprises Based on the Markov Model

An Analysis of the Optimal Allocation of Core Human Resources in Family Enterprises Based on the Markov Model

Dynamic Resource Allocation in Cloud Computing

Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic Algorithm

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