Online Allocation of Cloud Resources Based on Security Satisfaction

Halabi, Talal; Bellaïche, Martine; Abusitta, Adel

doi:10.1109/trustcom/bigdatase.2018.00063

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…e purpose is to avoid the text similarity due to the complexity of the time-type data mining objects, including sequence rules, periodic patterns, and sequences mode [12]. erefore, to reduce the calculation time, we try to find out the frequent item sets in the index data for the optimal allocation of the intelligent construction resources of the track and field team training venues and generate strong association rules based on the frequent item sets in these data.…”

Section: Mining Indexes For Optimal Allocation Of Resourcesmentioning

confidence: 99%

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Optimized Allocation of Resources for Intelligent Construction of Training Venues for Track and Field Teams

Qin

Wen

2021

Mobile Information Systems

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Because of the problems of low allocation efficiency and accuracy in the traditional resource allocation model for the intelligent construction of track and field team training venues, this paper proposes a new intelligent construction resource allocation model for track and field team training venues. A distributed data collection method is adopted to select the index data for the intelligent construction of the track and field team training venues to optimize the allocation of resources and the mined resource allocation index through extracting the optimized index characteristics. The goal of the optimization problem is transformed into a single-objective solving problem. The suggested approach is based on the two-level programming concept. The simulation experimental results show higher efficiency and better allocation effect of the proposed scheme compared to other state-of-the-art approaches. Our evaluation and observations suggest that the accuracy of the intelligent construction of track and field team training venues is between 90% and 99% which is higher than two other benchmarked models. Furthermore, the proposed model can quickly reach to an optimal decision.

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Section: Mining Indexes For Optimal Allocation Of Resourcesmentioning

confidence: 99%

“…e single objective is expressed as follows by equation (12) and the constraints of the problem are given by equation ( 13):…”

Section: Construction Of Resource Optimization Allocation Modelmentioning

confidence: 99%

Optimized Allocation of Resources for Intelligent Construction of Training Venues for Track and Field Teams

Qin

Wen

2021

Mobile Information Systems

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“…A particle swarm optimization method is used to solve the problem. A cloud resource allocation model to improve security is proposed by Halabi et al [19]. The authors designed the allocation problem as an optimization problem and propose a heuristic for the solution.…”

Section: Related Workmentioning

confidence: 99%

“…At first, a random chromosome population of size C is generated and the algorithm calculates the fitness of each of them. After the fitness being evaluated, based on the fitness value selection, crossover, and mutation are applied and a new set of chromosomes are generated [40] (lines [12][13][14][15][16][17][18][19][20].…”

Section: Inputmentioning

confidence: 99%

A Resource Allocation Model Based on Trust Evaluation in Multi-Cloud Environments

2021

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Allocating and managing resources while considering the quality of service is considered a fundamental and complex research problem in a cloud environment. An optimal resource allocation optimizes several parameters such as optimizing cost and resource utilization or maximizing any quality parameters. However, to ensure better customer service, Cloud Service Providers (CSPs) should consider most of the quality attributes while allocating resources to the cloud infrastructures. Existing research does not evaluate trust as a quantitative attribute, thus a trade-off between trust and performance in resource allocation is also absent in the research area. We propose a model to consider both trust and delay in this paper. The trust of a CSP is quantitatively estimated through some attributes and metrics. Availability, reliability, data integrity, and efficiency are considered to estimate the trust. The objective is to maximize the trust of the allocation while minimizing the communication delay. The proposed joint optimization model combines the previous credentials of the CSPs and the present resource constraints. To solve the problem heuristically, a genetic algorithm is applied. The model uses a number of parameters that provide the flexibility to adapt several service requirements. The effectiveness and applicability of the proposed approach are demonstrated through experiments. The results ensure that the effectiveness in the estimation of trust evaluation for different CSPs with the proposed attributes. Moreover, integrating trust in the resource allocation model allocates appropriate resources while enhancing the trust and reducing the communication delay in the overall allocation.

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“…They developed a CIA-based optimization function to identify CSPs with minimal user frustration for CIA. Halabi et al 64 addressed online Cloud services allocations in view of global safety satisfaction. A linear optimization technique is used to formalize the resource allocation problem in relation to global security requirements.…”

Section: Related Literaturementioning

confidence: 99%

Rapid health data repository allocation using predictive machine learning

Uddin

Stranieri

Gondal

et al. 2020

Health Informatics J

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Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used.

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Online Allocation of Cloud Resources Based on Security Satisfaction

Cited by 8 publications

References 12 publications

Optimized Allocation of Resources for Intelligent Construction of Training Venues for Track and Field Teams

Optimized Allocation of Resources for Intelligent Construction of Training Venues for Track and Field Teams

A Resource Allocation Model Based on Trust Evaluation in Multi-Cloud Environments

Rapid health data repository allocation using predictive machine learning

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