Deployment of Cloud Using Open-Source Virtualization: Study of VM Migration Methods and Benefits

Rastogi, Garima; Narayan, Satya; Krishan, Gopal; Sushil, Rama

doi:10.1007/978-981-10-6620-7_53

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…In agriculture, machine learning allows for more accurate disease diagnosis while conserving energy and avoiding false data. Farmers can upload field images captured by intelligent systems like satellites, unmanned aerial vehicles (UAVs), land-based rovers, smart phones, and tools such as the Climate Field View platform, which can identify potential farm issues and recommend a management plan [48][49][50][51][52].…”

Section: Recommendation System For Renting and Sharingmentioning

confidence: 99%

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Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Rakhra

Bhargava

et al. 2022

Journal of Food Quality

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Several industries have recently seen the replacement of human labor by automated machinery and equipment. Across the globe, farmers’ attitudes on the use of technology in agriculture are divergent. However, although some people are excited and ready to embrace technology, others are cautious and wary of trying new technologies for the first time. The third category is particularly prevalent in underdeveloped nations such as India, owing to a lack of competence, a lack of effective translation, and most crucially, a lack of financial resources. It is fruitless for the government to attempt to resolve these difficulties due to the fact that they do not take into consideration the changing circumstances and input needs of each agricultural group. Smart Tillage is a cutting-edge framework that was developed to solve the challenges listed above. In India, a decision-based smart engine for the rental and sharing of tools and equipment has been developed, which leverages machine learning methods to proceed towards a selection of tools and equipment. The option is entirely reliant on a variety of input variables, including crop kind, harvest time/month, crop equipment needed, harvest type, and the amount of money available for rental. Additionally, an ideal recommendation engine driven by content and collaborative-based filtering will provide the farmer’s requirements depending on their requirements. In terms of escalation, the proposals would be cost-effective and excellent since they would need little changes in training, technique improvements, and resource management via a new rent-share model similar to that used by Uber. In this work, demand and supply algorithms are used to define market equilibrium, and the results are shown in graphs. This includes discussion of a variety of demand and supply parameters, their impact on market equilibrium prices and quantities, and their effect on shifting demand and supply curves. The many sorts of elasticities (demand, cross-price, supply, income, and so on) are examined, as well as the ramifications for pricing systems that may result from these elasticities.

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Section: Recommendation System For Renting and Sharingmentioning

confidence: 99%

“…CPL approaches are referred to be memory-based and model-based. Using text classification, we may propose complicated objects like movies without needing "knowledge" of the object itself [50,51]. Many optimization devices have been used in recommender system evaluations.…”

Section: Collaborative Filteringmentioning

confidence: 99%

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Rakhra

Bhargava

et al. 2022

Journal of Food Quality

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“…ese changes are necessary for the future of agriculture to enhance productivity and save time [31,32]. By using remote sensing, the smart farming system saves money, boosts output, and enables better resource management.…”

Section: Smart Farming Systemsmentioning

confidence: 99%

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Rakhra

Sanober

Naveed

et al. 2022

Journal of Food Quality

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Farmers’ physical labor and debt are reduced as a result of agricultural automation, which emphasizes efficient and effective use of various machines in farming operations with the purpose of reducing physical labor and debt. It is a revolutionary idea in agriculture to create custom hiring centers, which are intended to make it easier for like-minded farmers to embrace technology/machinery for enhanced resource management practices. The study in question examines the significance of tool renting and sharing in the workplace. Rental and sharing equipment are two approaches that might be used to enable farmers to borrow equipment at a cheaper cost than they would otherwise have to pay for it. The following is a manual pilot study of 562 farmers in India to address the numerous challenges farmers face when looking for tools and equipment, as well as to determine their strong interest in the process of renting and sharing equipment. The study was conducted to address the numerous challenges farmers face when looking for tools and equipment and to determine their strong interest in the process of renting and sharing equipment. Farmers are divided into three groups according to the results of this poll: small, moderate, and large. Training and testing splits were used on the same data set in order to get a better understanding of the target variables. The data set for the survey was standardized in order to remove ambiguity. In this research, three different machine learning models were utilized: nearest neighbors, logistic regression, and decision trees. K-nearest neighbors was the most often used model, followed by logistic regression and decision trees. In order to get the best possible result, a comparison of the aforementioned algorithm models was carried out, which revealed that the decision tree is the better model among the others in this regard. Because the decision tree model is completely reliant on a large number of input factors, such as the kind of crop, the time/month of harvest, and the type of equipment necessary for the crops, it has the potential to have a social and economic impact on farmers and their livelihoods.

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“…All the tasks are distributed to VMs as per the load balancing algorithm, and the algorithm does not consider any new task or VM is coming at runtime. These methods do not require any extra monitoring for evaluation [12], [13], [14]. Various algorithms are used for static load balancing, such as First Come First Serve (FCFS), the improvement version of FCFS is roundrobin (RR) [15], [16].…”

Section: Traditional Load Balancing Methodsmentioning

confidence: 99%

Efficient Load Balancing in Cloud Environment Using Improved Spider Monkey Optimization

Verma

2022

Preprint

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Cloud computing provides a shared resource pool in a distributed environment. Users can access these resources anywhere anytime as per the provider’s policy. Whenever tasks are submitted on the cloud for execution, they needs to be scheduled appropriately in the cloud environment as many Virtual Machines (VM) are available at the backend. The performance of the entire system depends on the scheduling algorithm. A good scheduling algorithm distributes given tasks among all VMs so that the load of each VM is balanced. This problem is called a load balancing problem and comes under the category of NP-Hard problems. This paper uses the Spider Monkey Optimization algorithm for more efficient load balancing. The proposed optimization algorithm aims to enhance the performance by selecting the minimum loaded VM for balancing. Results of simulation show that proposed algorithm performance is improved compared to the existing algorithms with respect to load balancing, response time, make span, and resource utilization.

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Deployment of Cloud Using Open-Source Virtualization: Study of VM Migration Methods and Benefits

Cited by 13 publications

References 9 publications

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Efficient Load Balancing in Cloud Environment Using Improved Spider Monkey Optimization

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