Due to the rapid growth of mobile devices, and the new generation of mobile technologies and services, terms such as 'mobile charging' and 'billing processes' arise and become a hot area for researchers and mobile telecommunications operators. Supporting the new revenue schemas and different pricing points requires a progressive improvement not only on the level of platform/applications or the physical infrastructure, but also on the level of cloud resources across different layers both IaaS and PaaS. Operators avail such cloud resources to vendors to manage the users charging transaction requires a certain performance management and enhancement. Analytical models and machine learning techniques are employed to manage, analyse the users' data/logs and to get more useful info that can be used in the management of the cloud resources in order to reach the best resources utilization with the highest revenue stream from the services users. Machine learning techniques are used to predict users charging behaviour based on their previous charging history and they are grouped into a set of clusters based on the similarity of the charging logs in a self-adaptive model that learn from old and current charging transactions as well. A detailed experiment is conducted to show how to reduce the number of charging transactions to the minimum that does not affect the revenue stream and at the same time leads to the best resources utilization. Finer tuning on this is made by applying forecasting and prediction techniques on the data to enhance the result. Several prediction techniques are applied to reach the highest accuracy level of prediction. Numerical results serve to confirm the accuracy of the proposed analytical model while providing insight on how the different parameters and designs affect cloud resources performance. Ibrahiem et al: Prediction of users charging time In cloud environment using machine learning
Coriandrum sativum (Linn.) and Petroselinum crispum (Mill.) are the common herbs used for culinary purposes in daily life. The chlorophyll pigment in plants is being identified with various medicinal values, whereas iron is an essential micronutrient for the proper metabolism of the human body. The current research has been aimed at predicting the role of C. sativum and P. crispum in enhancing iron absorption via an in vitro approach. C. sativum and P. crispum have been analyzed for their capability of being a source of chlorophyll and iron concentration. The extracts prepared from solvents like carbinol, petroleum ether, and water were subjected to the identification of phytoconstituents through gas chromatography-mass spectrometry analysis, and the identified compounds were subjected to in silico studies against the iron-binding receptor, transferrin, to depict the binding affinity of the identified compounds. The carbinol extract was then put through in vitro analytical studies in Caco2 cell lines with a concentration of 500 µg/ml. Current research has shown that the leaves of C. sativum and P. crispum are an excellent source of chlorophyll and iron and has also suggested that these herbs efficiently enhance the absorption of iron in human intestinal cells.
Migration time is one of the metric to measure the performance of the algorithm for live migration. In this paper we have introduced a new parameter for live migration of virtual machines (VM) called the ‘Exit Time’ which is defined as the time to eject the state of one or more VMs from the source node. Exit Time defines how rapidly the VM can be taken out from the source node and its resources are freed for reallocating other tasks. We present an Agent Based Live Migration which disconnects the source node from the destination node during migration to reduce the exit time if the destination is slow. The source distributes the memory of VMs to multiple intermediate nodes organized by a middleware. Simultaneously, the destination collects and merges the VMs’ memory from the intermediate nodes. Thus exit from the source node is no longer resisted by the receiving speed of the destination. We support simultaneous live exit of multiple VMs and our ABDM implementation in the CloudSim platform reduces the exit time by a considerable amount against the traditional pre-copy and post-copy migration at the same time keeping the total migration time when the destination node is sluggish than the source
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