Hybrid Cloud Resource Provisioning (HCRP) Algorithm for Optimal Resource Allocation Using MKFCM and Bat Algorithm

Senthil, Kalaiselvi; Selvi, C. S. Kanimozhi

doi:10.1007/s11277-019-06907-9

Cited by 9 publications

(9 citation statements)

References 21 publications

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“…The basic SLA determines the time at which the service will be provided and the corresponding usage cost. 67 The cloud deployment models are further divided into three main categories: public, private, and hybrid clouds. Cloud computing is an automatic, pool of resources, on-demand service (pay-per-use), secure, economical, and easy maintenance system.…”

Section: Ra Schemes For Rt Services In Cloud Computingmentioning

confidence: 99%

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A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

Qureshi

Fayaz

et al. 2020

International Journal of Distributed Sensor Networks

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An efficient resource allocation scheme plays a vital role in scheduling applications on high-performance computing resources in order to achieve desired level of service. The major part of the existing literature on resource allocation is covered by the real-time services having timing constraints as primary parameter. Resource allocation schemes for the real-time services have been designed with various architectures (static, dynamic, centralized, or distributed) and quality of service criteria (cost efficiency, completion time minimization, energy efficiency, and memory optimization). In this analysis, numerous resource allocation schemes for real-time services in various high-performance computing (distributed and non-distributed) domains have been studied and compared on the basis of common parameters such as application type, operational environment, optimization goal, architecture, system size, resource type, optimality, simulation tool, comparison technique, and input data. The basic aim of this study is to provide a consolidated platform to the researchers working on scheduling and allocating high-performance computing resources to the real-time services. This work comprehensively discusses, integrates, analysis, and categorizes all resource allocation schemes for real-time services into five high-performance computing classes: grid, cloud, edge, fog, and multicore computing systems. The workflow representations of the studied schemes help the readers in understanding basic working and architectures of these mechanisms in order to investigate further research gaps.

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Section: Ra Schemes For Rt Services In Cloud Computingmentioning

confidence: 99%

“…The basic SLA determines the time at which the service will be provided and the corresponding usage cost. 67…”

Section: Ra Schemes For Rt Services In Cloud Computingmentioning

confidence: 99%

A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

Qureshi

Fayaz

et al. 2020

International Journal of Distributed Sensor Networks

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“…In order to give play to the greater benefits of the combination of "medical care" and "maintenance" and meet the elderly's pension needs, it is an urgent problem to allocate pension resources fairly, reasonably, and scientifically [8,9].…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Resources for the Integrated Medical Care and Elderly Care Model under Dynamic Monitoring

Jun-xia

2022

Scientific Programming

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Aiming at the problems of poor fairness of resource allocation and long time-consuming allocation of traditional methods, a method of resource optimization allocation under dynamic monitoring of medical care combined with elderly care mode is proposed. The integrated medical and elderly care model is analyzed, the role of dynamic monitoring in the optimal allocation of resources in the integrated medical and elderly care model is discussed, and a general model for optimal allocation of medical and elderly resources based on the analysis results is established. Based on the criterion of “minimum variance of estimation error,” a combined prediction model is established to obtain the combined prediction results. The investment objects of the optimal allocation of resources in the elderly care model are clustered in advance by using the clustering method, and the investment objects of the optimal allocation of resources in the elderly care model are comprehensively evaluated by using the principal component analysis method. According to the actual results of the comprehensive evaluation, the improved whale algorithm is used to allocate pension resources reasonably, and the fairness and applicability of the allocation results are analyzed experimentally. The results show that the method proposed in this paper has strong rationality and can effectively realize the rational distribution of pensions.

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“…These studies can be divided into four groups (conducted on IoT, fog/edge, cloud computing, and combined IoT‐fog/edge) based on the used platforms for which resource discovery is provided. In the first group, the studies by Guo et al, 15 Basset et al, 16 Thanikaivel et al, 17 and Kalaiselvi et al 18 discovered and allocated resources in cloud computing. These studies examined resource discovery and allocation from different aspects, such as minimizing physical servers and reducing delay and complexity.…”

Section: Introductionmentioning

confidence: 99%

A mathematical optimization model using red deer algorithm for resource discovery in CloudIoT

Goudarzi

Rahmani

Mosleh

2022

Trans Emerging Tel Tech

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The Internet of Things (IoT) and cloud computing are new concepts in revolutionized communication and information technologies. Different technologies, for example, transportation and healthcare, can benefit from Cloud of Things (CloudIoT) for mobile and fixed resource applications with great promises. Fixed and mobile resources are very important items in the CloudIoT paradigm because of the need for an appropriate discovery mechanism. In the present study, a mathematical optimization model is proposed for the minimization of bandwidth, cost, and response time of CloudIoT platforms with a special focus on the role of mobile and fixed resources in resource discovery. Additionally, this study presents a heuristic resource discovery algorithm using a mathematical optimization model (RDMOM). The mixed‐integer non‐linear programming is used to design the discovery mechanism. Furthermore, the optimization problem is solved using the red deer algorithm. Finally, the simulation results show a significant reduction in the latency, resource efficiency, and energy consumption, as well as an improvement in availability and success ratio, compared to previous algorithms. The RDMOM algorithm significantly improves the success ratio, energy consumption, resource efficiency, availability, and latency, respectively, by 18%, 21%, 24%, 17%, and 21% in comparison to other algorithms.

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Hybrid Cloud Resource Provisioning (HCRP) Algorithm for Optimal Resource Allocation Using MKFCM and Bat Algorithm

Cited by 9 publications

References 21 publications

A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

Optimal Allocation of Resources for the Integrated Medical Care and Elderly Care Model under Dynamic Monitoring

A mathematical optimization model using red deer algorithm for resource discovery in CloudIoT

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