Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
Search Results
Item Novel energy efficient virtual machine allocation at data center using Genetic algorithm(Institute of Electrical and Electronics Engineers Inc., 2015) Sharma, N.K.; Guddeti, G.Increased resources utilization from clients in a smart computing environment poses a greater challenge in allocating optimal energy efficient resources at the data center. Allocation of these optimal resources should be carried out in such a manner that we can save the energy of data center as well as avoiding the service level agreement (SLA) violation. This paper deals with the design of an energy efficient algorithm for optimized resources allocation at data center using combined approach of Dynamic Voltage Frequency Scaling (DVFS) and Genetic algorithm (GA). The performance of the proposed energy efficient algorithm is compared with DVFS. Experimental results demonstrate that the proposed energy efficient algorithm consumes 22.4% less energy over a specified workload with 0% SLA violation. © 2015 IEEE.Item A novel energy efficient resource allocation using hybrid approach of genetic DVFS with bin packing(Institute of Electrical and Electronics Engineers Inc., 2015) Sharma, N.K.; Guddeti, G.Increased resources utilization from several clients in a smart computing environment poses a key challenge in allocating optimal energy efficient resources at the data center. Allocation of these optimal resources should be carried out in such a manner that we can reduce the energy consumption of the data center and also avoid the service level agreement (SLA) violation. This paper deals with the development of an energy efficient algorithm for optimal resources allocation at the data center using hybrid approach of the Dynamic Voltage Frequency Scaling (DVFS), Genetic algorithm (GA) and Bin Packing techniques. The performance of the proposed hybrid approach is compared with Genetic Algorithm, DVFS with Bin Packing, DVFS without Bin Packing techniques. Experimental results demonstrate that the proposed energy efficient algorithm consumes 22.4% less energy as compared to the DVFS with Bin Packing technique over a specified workload with 0% SLA violation. © 2015 IEEE.Item The effect of software aging on power usage(Institute of Electrical and Electronics Engineers Inc., 2015) Mohan, R.; Guddeti, G.This paper tries to establish relation between the power usage and software aging. Software aging is the performance degradation of long running software due to shrinking in physical memory, increase in swap read and write rate and increase in CPU utilization. This paper tries to establish the relation between the Software aging and the power usage. Experimental results demonstrate that CPU utilization increases over a period of time, when the work load remains the constant. Linear Regression analysis is used for establishing this trend. © 2015 IEEE.