Sonia Sharma
Research Interests
Sonia Sharma has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
Rajendra Kumar Bharti
3 shared publications- Rajendra Kumar Bharti
Bipin Tripathi Kumaon Institute Of Technology, Dwarahat, India
1 shared publication
Latest Publications
- 1 January 2025
- Published by Totem Publisher, Inc. in International Journal of Performability Engineering
- Vol. 21 (3) , 168
Intelligent Job Allocation and Adaptive Migration in Cloud Environments using a Dynamic Dual-Threshold Strategy
- 1 January 2025
- Published by Totem Publisher, Inc. in International Journal of Performability Engineering
- Vol. 21 (3) , 168
Intelligent Job Allocation and Adaptive Migration in Cloud Environments using a Dynamic Dual-Threshold Strategy
- 30 April 2024
- Published by Springer Nature
- p. 677-686
Assessment of Various MapReduce Scheduling in Heterogeneous Environment
- 30 April 2024
- Published by Springer Nature
- p. 677-686
Assessment of Various MapReduce Scheduling in Heterogeneous Environment
- 16 June 2023
- Published by Springer Nature in The Journal of Supercomputing
- Vol. 79 (18) , 20712-20746
Two phased Yarn scheduling with novel binary hypergraphed PSO
- 16 June 2023
- Published by Springer Nature in The Journal of Supercomputing
- Vol. 79 (18) , 20712-20746
Two phased Yarn scheduling with novel binary hypergraphed PSO
- 25 November 2022
- Published by Wiley in Concurrency and Computation: Practice and Experience
- Vol. 35 (4)
New efficient Hadoop scheduler: Generalized particle swarm optimization and simulated annealing‐dominant resource fairness
Summary: Digital growth during the Corona pandemic has generated massive data. The Hadoop in big data has to be more efficient in resource handling and job scheduling. This article proposes the improved job scheduler which is more efficient in fair job scheduling even with the heterogeneous resources. The faster job execution depends upon the localization too. The nearer the slots are, the faster is the execution. So, this article proposes a hybrid metaheuristic algorithm with fair scheduling and data locality as the two objectives in job scheduling. The dominant resource fairness policy in Hadoop YARN is updated by hybrid generalized particle swarm optimization and simulated annealing for minimum locality and maximum fairness in scheduling. The algorithm is tested on various workloads for heterogeneous resources.
- 25 November 2022
- Published by Wiley in Concurrency and Computation: Practice and Experience
- Vol. 35 (4)
New efficient Hadoop scheduler: Generalized particle swarm optimization and simulated annealing‐dominant resource fairness
Summary: Digital growth during the Corona pandemic has generated massive data. The Hadoop in big data has to be more efficient in resource handling and job scheduling. This article proposes the improved job scheduler which is more efficient in fair job scheduling even with the heterogeneous resources. The faster job execution depends upon the localization too. The nearer the slots are, the faster is the execution. So, this article proposes a hybrid metaheuristic algorithm with fair scheduling and data locality as the two objectives in job scheduling. The dominant resource fairness policy in Hadoop YARN is updated by hybrid generalized particle swarm optimization and simulated annealing for minimum locality and maximum fairness in scheduling. The algorithm is tested on various workloads for heterogeneous resources.