A Weighted PageRank-Based Algorithm for Virtual Machine Placement in Cloud Computing

Yao, Wenbin; Shen, Yue; Wang, Dongbin

doi:10.1109/access.2019.2957772

Cited by 14 publications

(4 citation statements)

References 24 publications

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“…By categorizing web pages and locating websites, web mining contributes to the improvement of web search engine performance and the acquisition of usable information from the World Wide Web. Web structure mining, web content mining then web usage mining are the three types that arise [10] [13]. One of the techniques Google employs to assess the significance or relevance of a page is PageRank.…”

Section: Weighted Pagerank Algorithmmentioning

confidence: 99%

Optimizing the Eﬃciency of Web Mining through Comparative Web Ranking Algorithms

Khalil,

Rehan,

Syed

et al. 2023

VFAST trans. softw. eng.

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Millions of web pages carrying massive amounts of data make up the World Wide Web. Real-time data has been generated on a wide scale on the websites. However, not every piece of data is relevant to the user. While scouring the web for information, a user may come upon a web page that contains irrelevant or incomplete information. As a response, search engines can alleviate this issue by displaying the most relevant pages. Two web page ranking algorithms are proposed in this study along with the Dijkstra algorithm; the PageRank algorithm and the Weighted PageRank algorithm. The algorithms are used to evaluate a web page's importance or relevancy within a network, such as the Internet. PageRank evaluates a page's value based on the quantity and quality of links leading to it. It is commonly utilized by nearly all search engines around the world to rank web pages in order of relevance. This algorithm is used by Google, the most widespread Internet search engine. In the process of Web mining, page rank is quite weighty. The most important component of marketing is online use mining, which investigates how people browse and operate a business on a company's website. The study presents two proposed models that try to optimize web links and improve search engine results relevancy for users.

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Section: Weighted Pagerank Algorithmmentioning

confidence: 99%

Optimizing the Eﬃciency of Web Mining through Comparative Web Ranking Algorithms

Khalil,

Rehan,

Syed

et al. 2023

VFAST trans. softw. eng.

View full text Add to dashboard Cite

show abstract

“…e proposed method is compared with the Flow Shop Scheduling Problem and Traveling Salesman Problem. Yao et al [11] introduced a VM placement procedure based on Weighted PageRank. ey focused on minimizing the number of active physical machines and also increased resource utilization of all hosts in the data centers.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Approach for Resource Allocation in Cloud Infrastructure Using Random Forest and Genetic Algorithm

Madhusudhan

T²,

Mustapha

et al. 2021

Scientific Programming

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In cloud computing, the virtualization technique is a significant technology to optimize the power consumption of the cloud data center. In this generation, most of the services are moving to the cloud resulting in increased load on data centers. As a result, the size of the data center grows and hence there is more energy consumption. To resolve this issue, an efficient optimization algorithm is required for resource allocation. In this work, a hybrid approach for virtual machine allocation based on genetic algorithm (GA) and the random forest (RF) is proposed which belongs to a class of supervised machine learning techniques. The aim of the work is to minimize power consumption while maintaining better load balance among available resources and maximizing resource utilization. The proposed model used a genetic algorithm to generate a training dataset for the random forest model and further get a trained model. The real-time workload traces from PlanetLab are used to evaluate the approach. The results showed that the proposed GA-RF model improves energy consumption, execution time, and resource utilization of the data center and hosts as compared to the existing models. The work used power consumption, execution time, resource utilization, average start time, and average finish time as performance metrics.

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“…Virtual machine integration between physical machines 32 . An adaptive resource management system is proposed based on hierarchical multiagent architecture in edge computing 33 . The authors considered the competition between the same types of virtual machines, referring to CPU, memory, disk intensive and their hybrid requirements, and proposed a new virtual machine integration method to optimize virtual machines on physical machines.…”

Section: Related Workmentioning

confidence: 99%

An energy efficient and resource‐constrained scheduling framework for smart city application

Jing

Miao

Song

et al. 2020

Trans Emerging Tel Tech

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Summary With the development of smart Internet of things devices, intelligent applications are expected to lead further innovation in smart city. However, although cloud computing infrastructure can be used to meet traditional challenges, the scheduling model for new big data intelligent application has still not matured. In this work, we proposed a two‐stage scheduling framework for smart city intelligent application. In the first stage, we propose a virtual machine selection algorithm for edge computing to enhance relative migration benefits. The algorithm defines the invalid virtual machine migration and relative migration benefits from the change in the overall computing resources of the cloud data center after the virtual machine migration. In the second stage, we proposed an energy efficient and resource‐constrained scheduling framework for edge computing. The historical data of the cloud and edge computing workload of the computing node are processed in a sliding window manner, and the median absolute deviation of the historical data is used as the base of the physical reserved resource constraint when the base also changes as the workload changes. The experimental results show that energy‐efficient and resource‐constrained can make the computer resource provide high‐quality services for users in a low‐energy state.

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A Weighted PageRank-Based Algorithm for Virtual Machine Placement in Cloud Computing

Cited by 14 publications

References 24 publications

Optimizing the Eﬃciency of Web Mining through Comparative Web Ranking Algorithms

Optimizing the Eﬃciency of Web Mining through Comparative Web Ranking Algorithms

Hybrid Approach for Resource Allocation in Cloud Infrastructure Using Random Forest and Genetic Algorithm

An energy efficient and resource‐constrained scheduling framework for smart city application

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