Ripandeep Kaur

Bio: Ripandeep Kaur is an academic researcher from Chandigarh University. The author has contributed to research in topics: Scheduling (computing) & Autonomic computing. The author has an hindex of 1, co-authored 1 publications receiving 8 citations.

Proactive Scheduling in Cloud Computing

Abstract: Autonomic fault aware scheduling is a feature quite important for cloud computing and it is related to adoption of workload variation. In this context, this paper proposes an fault aware pattern matching autonomic scheduling for cloud computing based on autonomic computing concepts. In order to validate the proposed solution, we performed two experiments one with traditional approach and other other with pattern recognition fault aware approach. The results show the effectiveness of the scheme.

Improving Classification Accuracy Using Clustering Technique

Abstract: Product classification is the key issue in e-commerce domains. Many products are released to the market rapidly and to select the correct category in taxonomy for each product has become a challenging task. The application of classification model is useful to precisely classify the products. The study proposed a method to apply clustering prior to classification. This study has used a large-scale real-world data set to identify the efficiency of clustering technique to improve the classification model. The conventional text classification procedures are used in the study such as preprocessing, feature extraction and feature selection before applying the clustering technique. Results show that clustering technique improves the accuracy of the classification model. The best classification model for all three approaches which are classification model only, classification with hierarchical clustering and classification with K-means clustering is K-Nearest Neighbor (KNN) model. Even though the accuracy of the KNN models are the same across different approaches but the KNN model with K-means clustering had the shortest time of execution. Hence, applying K-means clustering prior to KNN model helps in reducing the computation time.

Peak load scheduling in smart grid using cloud computing

Abstract: In this paper present peak, energy management attainable is feasible by monitoring real-time readings of whole loads within the college premises victimization this schedule loads so energy saving is possible. Currently, cloud computing technology offer on-line real-time monitoring knowledge, we have a tendency to create project supported cloud computing application for energy management that is employed for monitoring real time consumption of electricity and load planning. With respect to monitoring knowledge, we have a tendency to be able to plot the load curves so it'll be useful in achieving optimum energy consumption for educational institute.

The enhancement of logging system accuracy for infrastructure as a service cloud

Abstract: We introduce the novel technical results of the enhanced logging system for customer virtual machines (VMs) in an Infrastructure as a Service (IaaS) cloud. The main contribution is that the enhanced system can work with a better system's accuracy and speed, with the simplicity of the design and implementation. We measure the accuracy of the unenhanced logging system, then find a quick solution to enhance the system based on the results of the measurement. To measure and enhance the unenhanced system, we increase the main memory and CPU cores of the VMs then collect the accuracy results from each increment configuration. We analyze the results and propose to use the taskset tool to enhance the accuracy of the system. Found three main findings include: firstly, the accuracy of the enhanced system is about 20% on maximum better than the unenhanced one; the enhanced system accuracy becomes 100%; lastly, the enhanced system can detect a file with the smaller file size as almost 12% smaller. The findings can be a basis to design the logging systems in an IaaS cloud, to decrease hardware and energy investment. To the best of our knowledge, the contribution and findings are not in the literature.

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

Abstract: Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads.