Workload Prediction Based Virtual Machine Migration and Optimal Switching Strategy for Cloud Power Management

Pushpalatha, R; Ramesh, Bharathi

doi:10.1007/s11277-021-09157-w

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…However, the migration cost and load on data center is still high. Reference [8] predicts VM migration using chaotic Drosophila Rider neural network, and uses VM migration and optimal switching strategy to optimize power. If the state of predicted load is overloaded, the HHSMO optimization algorithm will be used for VM migration.…”

Section: Related Workmentioning

confidence: 99%

Efficient Smart Grid Load Balancing via Fog and Cloud Computing

Wang

2022

Mathematical Problems in Engineering

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As the cloud data centers size increases, the number of virtual machines (VMs) grows speedily. Application requests are served by VMs be located in the physical machine (PM). The rapid growth of Internet services has created an imbalance of network resources. Some hosts have high bandwidth usage and can cause network congestion. Network congestion affects overall network performance. Cloud computing load balancing is an important feature that needs to be optimized. Therefore, this research proposes a 3-tier architecture, which consists of Cloud layer, Fog layer, and Consumer layer. The Cloud serves the world, and Fog analyzes the services at the local edge of network. Fog stores data temporarily, and the data is transmitted to the cloud. The world is classified into 6 regions on the basis of 6 continents in consumer layer. Consider Area 0 as North America, for which two fogs and two cluster buildings are considered. Microgrids (MG) are used to supply energy to consumers. In this research, a real-time VM migration algorithm for balancing fog load has been proposed. Load balancing algorithms focus on effective resource utilization, maximum throughput, and optimal response time. Compared to the closest data center (CDC), the real-time VM migration algorithm achieves 18% better cost results and optimized response time (ORT). Realtime VM migration and ORT increase response time by 11% compared to dynamic reconFigure with load (DRL) with load. Realtime VM migration always seeks the best solution to minimize cost and increase processing time.

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Section: Related Workmentioning

confidence: 99%

Efficient Smart Grid Load Balancing via Fog and Cloud Computing

Wang

2022

Mathematical Problems in Engineering

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“…We apply efficient ML algorithms to process crime data and make a prediction. Data Science is a trending technology this helps in solving different types of crimes in all types of fields [1]. In this thesis, data science applied to the crime sector to process old crime data or crime data from previous years and to find common types of crime and their correlations.…”

Section: Introductionmentioning

confidence: 99%

Crime Pattern Prediction Using Ml Algorithms

H M

2024

IJSREM

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Today the crimes are increasing rapidly that too in a high population countries. So we require automated application to detect crime and prevention. The system should stop crimes before it begins. As crimes are on the rise, preventive measures should be taken to stop the occurring of crimes. Finding frequent crimes and related correlation is tiresome and a high-effort task in the current crime sector. machine learning or AI, is an emerging technology to solve this problem. Previously effective unsupervised learning algorithms datasets for procedural crime training and frequent crimes and their relationships are identified [5]. Designed system created to reduce crime and applicable to crime sector. We build an application anywhere it is useful for criminal sectors to reduce crime. Currently, none of the applications in criminal activity and we use effective data science algorithms to predict crimes with better results. Keywords: Data Science, Machine Learning, Apriori Algorithm, Apriori TID Algorithm, Crime Types, Patterns

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“…Mental health issues such as depression, stress, fear, and worry are all too widespread among today's students. College stress prediction is one of the most important and difficult responsibilities facing educators today There have been several investigations on how to alleviate student anxiety [6,7].…”

Section: Introductionmentioning

confidence: 99%

Stress Level Prediction of College Students Using ML

2023

IJSREM

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College students' mental health is often neglected, which may have serious consequences for their success in academic records. Mental health issues among college students may have far-reaching consequences for both the campus and the students themselves. General counselling, student management systems, and human advisers are just some of the ineffective approaches that institutions are using to track and avoid student stress. Naive Bayes, K-Nearest Neighbor, and Decision Tree are just a few examples of the Machine Learning Algorithms used by the proposed system to determine whether or not a student is experiencing stress and, if student is in stress, the system provide solution for relieving that stress. Key Words: Machine Learning, Naïve Bayes, K-Nearest Neighbor, Decision Tree, Stress, Stress Free, Prediction

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Workload Prediction Based Virtual Machine Migration and Optimal Switching Strategy for Cloud Power Management

Cited by 6 publications

References 26 publications

Efficient Smart Grid Load Balancing via Fog and Cloud Computing

Efficient Smart Grid Load Balancing via Fog and Cloud Computing

Crime Pattern Prediction Using Ml Algorithms

Stress Level Prediction of College Students Using ML

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