Non-dominated Ranking Biogeography Based Optimization Algorithm for Virtual Machine Placement in Cloud Computing

Bouhank, Asma; Daoudi, M.

doi:10.1007/978-3-030-80119-9_25

Cited by 3 publications

(4 citation statements)

References 24 publications

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“…Classroom equipped with smart devices refers to taking modern information technology as the support, aiming at improving students' comprehensive literacy and promoting students' intelligent development, seamlessly connecting online and offline, in class and extracurricular teaching resources, constantly innovating teaching and learning methods, integrating information technology means into the teaching process in classroom, and improving the efficient interaction between the members participate in the procedure of teaching, so as to sufficiently promote students' autonomous learning ability. The cultivation of inquiry ability and wisdom ability will build a threedimensional and intelligent new classroom [7]. Classroom equipped with smart devices has the characteristics of diversified teaching decision-making, mobile teaching process, intelligent resource push, three-dimensional communication and interaction, real-time evaluation feedback, and so on.…”

Section: Connotation Of a Classroom Equipped With Smartmentioning

confidence: 99%

A Chinese Intelligent Teaching Platform for Colleges Based on Cloud Computing

Qi¹,

Huang

2022

Mobile Information Systems

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In China, the Chinese subject is called “the mother of the encyclopedia.” Learning Chinese well is the basic condition for learning other subjects well. Therefore, Chinese education has always undertaken the important task of teaching the mother tongue and cultural inheritance and has a great influence on the development process of China’s education. Society is facing new opportunities and challenges since the 21st century, and all the mobile Internet, cloud computing, and Internet of Things achieve great development. Using the increasingly mature technology of data information collection, analysis, and processing can realize a more comprehensive evaluation of students in teaching activities. In the current upsurge of big data, it is more and more widely used in the field of education, such as learning analysis, network education platform, education information management platform, education AP, and smart campus. In this context, Chinese teaching needs more open courses, which requires us to organically integrate the existing information technology and Chinese teaching, and on this basis, integrate modern educational data sets and learning analysis technology into Chinese teaching and learning, to promote the improvement of the quality of language teaching and learning. The development of information technology in the intelligent era provides an opportunity for the establishment and application of an intelligent classroom. This paper constructs an intelligent and efficient new classroom equipped with smart devices teaching mode based on defining the concept and connotation of a classroom equipped with smart devices. The proposed model is combined with the current situation and the development of classrooms equipped with smart devices. It also analyzes the difficulties and pain points of traditional College Chinese teaching. The research results can provide a reference for the implementation of intelligent classroom teaching mode in College Chinese and other general courses in higher vocational colleges.

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Section: Connotation Of a Classroom Equipped With Smartmentioning

confidence: 99%

A Chinese Intelligent Teaching Platform for Colleges Based on Cloud Computing

Qi¹,

Huang

2022

Mobile Information Systems

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“…A final comparison was made using first fit, virtual machine placement ant colony system (VMPACS) and Modified Best Fit Decreasing (MBFD). Bouhank and Daoudi [ 16 ] use placing VMs on appropriate servers in a cloud context for NP-hard issue. They propose a Non-dominated Ranking Biogeography Based Optimization (NRBBO) algorithm to decrease the overall resource waste and power consumption across all servers in a VM installation.…”

Section: Literature Surveysmentioning

confidence: 99%

Section: Literature Surveysmentioning

confidence: 99%

“…(1) for each host in active state acth i ∈ ACTH then do (2) for each VM actv ij ∈ acth i then do (3) if actv ij in idle state then (4) Deallocate actv ij resources to acth i (5) end if loop (6) end for loop (7) end if loop (5) end for loop (6) StatusofVM � 0 (7) for each host in active state acth i ∈ ACTH from acth k to acth 1 then do (8) for each VM actv ij ∈ acth i then do (9) if migration of actv ij to ACTH-acth i then (10) StatusofVM � StatusofVM + 1 (11) migration ij ← Host ID which is being targeted (12) end if loop (13) end for loop (14) if StatusofVM � j − 1 then (15) migrate all VMs using migration ij (16)…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

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An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

Srivastava

Tiwari

Srivastava

et al. 2022

Computational Intelligence and Neuroscience

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One of the important and challenging tasks in cloud computing is to obtain the usefulness of cloud by implementing several specifications for our needs, to meet the present growing demands, and to minimize energy consumption as much as possible and ensure proper utilization of computing resources. An excellent mapping scheme has been derived which maps virtual machines (VMs) to physical machines (PMs), which is also known as virtual machine (VM) placement, and this needs to be implemented. The tremendous diversity of computing resources, tasks, and virtualization processes in the cloud causes the consolidation method to be more complex, tedious, and problematic. An algorithm for reducing energy use and resource allocation is proposed for implementation in this article. This algorithm was developed with the help of a Cloud System Model, which enables mapping between VMs and PMs and among tasks of VMs. The methodology used in this algorithm also supports lowering the number of PMs that are in an active state and optimizes the total time taken to process a set of tasks (also known as makespan time). Using the CloudSim Simulator tool, we evaluated and assessed the energy consumption and makespan time. The results are compiled and then compared graphically with respect to other existing energy-efficient VM placement algorithms.

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Towards Sustainable Cloud Computing: Load Balancing with Nature-Inspired Meta-Heuristic Algorithms

Li,

Wang,

Tian

et al. 2024

Electronics

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Cloud computing is considered suitable for organizations thanks to its flexibility and the provision of digital services via the Internet. The cloud provides nearly limitless computing resources on demand without any upfront costs or long-term contracts, enabling organizations to meet their computing needs more economically. Furthermore, cloud computing provides higher security, scalability, and reliability levels than traditional computing solutions. The efficiency of the platform affects factors such as Quality of Service (QoS), congestion, lifetime, energy consumption, dependability, and scalability. Load balancing refers to managing traffic flow to spread it across several channels. Asymmetric network traffic results in increased traffic processing, more congestion on specific routes, and fewer packets delivered. The paper focuses on analyzing the use of the meta-optimization algorithm based on the principles of natural selection to solve the imbalance of loads in cloud systems. To sum up, it offers a detailed literature review on the essential meta-heuristic algorithms for load balancing in cloud computing. The study also assesses and analyses meta-heuristic algorithm performance in load balancing, as revealed by past studies, experiments, and case studies. Key performance indicators encompass response time, throughput, resource utilization, and scalability, and they are used to assess how these algorithms impact load balance efficiency.

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Non-dominated Ranking Biogeography Based Optimization Algorithm for Virtual Machine Placement in Cloud Computing

Cited by 3 publications

References 24 publications

A Chinese Intelligent Teaching Platform for Colleges Based on Cloud Computing

A Chinese Intelligent Teaching Platform for Colleges Based on Cloud Computing

An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

Towards Sustainable Cloud Computing: Load Balancing with Nature-Inspired Meta-Heuristic Algorithms

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