%0 Journal Article %1 noauthororeditor %A Srinivas1, D B %D 2023 %J International Journal of Distributed and Parallel systems (IJDPS) %K aware myown %N 1 %P 14 %T REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING %U file:///C:/Users/nabin/OneDrive/Pictures/Saved%20Pictures/IJDPS%207.pdf %V 10 %X Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The increased availability of the cloud models and allied developing models creates easier computing cloud environment. Energy consumption and effective energy management are the two important challenges in virtualized computing platforms. Energy consumption can be minimized by allocating computationally intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling (DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the required QoS. However, they do not control the internal and external switching to server frequencies, which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm minimizes consumption of energy and time during computation, reconfiguration and communication. Our proposed model confirms the effectiveness of its implementation, scalability, power consumption and execution time with respect to other existing approaches.

@article{noauthororeditor, abstract = {Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The increased availability of the cloud models and allied developing models creates easier computing cloud environment. Energy consumption and effective energy management are the two important challenges in virtualized computing platforms. Energy consumption can be minimized by allocating computationally intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling (DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the required QoS. However, they do not control the internal and external switching to server frequencies, which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm minimizes consumption of energy and time during computation, reconfiguration and communication. Our proposed model confirms the effectiveness of its implementation, scalability, power consumption and execution time with respect to other existing approaches.}, added-at = {2023-04-26T14:58:15.000+0200}, author = {Srinivas1, D B}, biburl = {https://www.bibsonomy.org/bibtex/2ad95fda49c650c3ae6332943cf1aab2c/ijasa_journal}, description = {The growth of Internet and other web technologies requires the development of new algorithms and architectures for parallel and distributed computing. International journal of Distributed and parallel systems is a bi monthly open access peer-reviewed journal aims to publish high quality scientific papers arising from original research and development from the international community in the areas of parallel and distributed systems. IJDPS serves as a platform for engineers and researchers to present new ideas and system technology, with an interactive and friendly, but strongly professional atmosphere.}, interhash = {6662a1981401f48aa86323b6d8f7826e}, intrahash = {ad95fda49c650c3ae6332943cf1aab2c}, journal = { International Journal of Distributed and Parallel systems (IJDPS)}, keywords = {aware myown}, number = 1, pages = 14, timestamp = {2023-04-26T14:58:15.000+0200}, title = {REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING}, url = {file:///C:/Users/nabin/OneDrive/Pictures/Saved%20Pictures/IJDPS%207.pdf}, volume = 10, year = 2023 }