SECURE : Efficient resource scheduling by swarm in cloud computing

Singh, Harvinder; Bhasin, Anshu; Kaveri, Parag Ravikant

doi:10.1080/09720529.2019.1576334

Cited by 22 publications

(10 citation statements)

References 8 publications

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“…In [16], the multi-objective workflow optimization strategy (MOWOS) employs the tasks splitting mechanism to split large tasks into sub-tasks to reduce their scheduling length. The simulation results showed that, the MOWOS algorithm had less execution cost and, better execution MS, and also it utilized the resources better than the existing HSLJF [15] and SECURE [19] algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Different kinds of optimization criteria were addressed, e.g., MS, cost, budget, deadline, TET, RU, throughput, load balancing, and energy efficiency. General, these optimization criteria are categorized into two types based on cloud service: cloud users' type and cloud service provider's type [1,12,18,19]. The details of simulation by data values parameters are shown in Tab.…”

Section: The Performance Evaluationmentioning

confidence: 99%

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Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud

Mokhtari¹,

Motameni²,

Bayat³

2022

Computer Systems Science and Engineering

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In this study, a new feature is added to the smart message passing interface (SMPI) approach (SMPIA) based on the prioritization method, which can completely eliminate the task starvation and lack of sufficient resources problems through prioritizing the tasks. The proposed approach is based on prioritizing the tasks and the urgency of implementation. Tasks are prioritized based on execution time, workload, the task with a more sensitive priority is executed earlier by the free source. The idea of demand-bound functions (DBFs) was extended to the SMPIA setting based on partitions and caps. For each task, two DBFs are constructed, DBFLO and DBFHI, for the LO and HI criticality modes, respectively. The simulation results returned by MATLAB showed that with the optimized SMPIA (O-SMPIA), the parameters of maximum service execution time, response time, delay time, and throughput improved in this work. In addition, the results confirmed that the reduction of execution time, completion time, and resource consumption time did not affect the response time and throughput of workflow tasks and did not cause inefficient use of resources in virtual machines (VMs) and data centers (DCs). The evaluation of performance metrics showed that the delay, response time of the Greedy algorithm was less than that of Max-Min and Min-Min. At the same time, the execution time of Max-Min was less than the others and the throughput of the Greedy was longer. The effect and throughput of O-SMPIA became more obvious as change to the job count and the number of cloud workloads increased. It is also worth mentioning that one of the main advantages of the O-SMPIA to other methods is the efficient use of time to execute all the defined tasks by CPU.

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Section: Related Workmentioning

confidence: 99%

Section: The Performance Evaluationmentioning

confidence: 99%

Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud

Mokhtari¹,

Motameni²,

Bayat³

2022

Computer Systems Science and Engineering

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“…The techniques and methods for execution are not given. A cloud-based ecommerce design has been introduced, which addresses critical e-commerce problems such as connectivity and flexibility [27].A 3G-based, cloud-based, mobile e-commerce system with 2D-Barcode technologies is provided to increase application efficiency. The investigation shows that the technique is interesting concerning ease and security and does not examine diverse information accurate measures [28].…”

Section: Background To the E-commerce Systemsmentioning

confidence: 99%

Cloud Computing based E-commerce Management Ontransaction Security Concepts

Chi

Sui

Alazab

et al. 2021

Preprint

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The way e-commerce companies do commerce is transformed by cloud computing. Although the usage of cloud technology in e-commerce has grown suddenly, the advantages of cloud services platforms have not yet been exploited, especially for e-commerce applications. For decision-makers, it is vital that the optimized cloud-based computing solution like software as a service (SaaS), platform as a service (PaaS), or infrastructure as a service(IaaS) model is adopted as a multi-criteria decision-making (MCDM) issue and is thus dependent on a trustworthy and secured online shop. A new Cloud computing-based E-Commerce Management (CCECM) approach is provided to tackle the MCDM issue, a multiple-criteria group approach based on an ordering preference strategy by an idealized solution,and relying on the structure for a list of requirements. A small-to-medium firm uses the suggested system to ease the assessment and decision-making of the aspects connected with cloud e-commerce with security.The results show that small and moderate-sized e-commerce companies have higher secured and efficient communication. SaaS is asuitable alternative for difficulty, dependability, safety and protection, organizational preparedness, and corporate size. At the same time, adaptability and scaling can be enhanced in the choosing of PaaS or IaaS.

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“…In work [28][29][30][31], researchers found that business process management history information could be used to improve the efficiency of cloud resource allocation during business process management execution. Russell et al [32,33] introduced a new pattern that integrated work items and scheduling resources based on the previous execution history of cloud resources. Xu et al and Priya et al [34,35] put forward the event of logging method to study the allocation law, which made the resource allocation work in the execution phase more effective.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised human resource scheduling based on deep presentation in the cloud

Lin

Wang

2020

J Wireless Com Network

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Traditional competitive human resource allocation is no longer suitable for contemporary requirements. To improve the effectiveness of human resource management and the degree of matching between jobs and staffs, we propose a novel auto-encoder neural network-based method in the cloud environment, which is a semi-automatic manner in the business process of human resource allocation. The proposed method is based on deep learning architecture by using appropriate cloud resources and takes into account the similarities and deep presentation between staff modules. The construction between human resource network is combined with a priori information about the human resource and set up by the evaluation index system of human resource planning. Our proposed method enables the modeling of semi-automatic human resource allocation process and can be used to facilitate optimized human resource allocation. Experimental results show that our method can bring significant improvements to personnel position matching and effectively enhance the efficiency of human resource allocation based on the cloud environment.

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SECURE : Efficient resource scheduling by swarm in cloud computing

Cited by 22 publications

References 8 publications

Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud

Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud

Cloud Computing based E-commerce Management Ontransaction Security Concepts

Semi-supervised human resource scheduling based on deep presentation in the cloud

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