Browsing by Author Jangiti, Saikishor
Showing results 1 to 15 of 15
| Issue Date | Title | Author(s) |
| 2019 | Automated question extraction and tagging for cloud-based online communities | Jangiti, Saikishor |
| 2015 | BEST ELECTRONIC SHOPPING TECHNIQUE (BEST)-AN ADHOC COMPONENT USING BAT MODEL | Jangiti, Saikishor |
| 2019 | Bulk-bin-packing based migration management of reserved virtual machine requests for green cloud computing | Jangiti, Saikishor |
| 2014 | DMK-Medoid Heuristic Product Ranking in Online Market | Jangiti, Saikishor |
| 2020-09 | EMC2: Energy-efficient and multi-resource- fairness virtual machine consolidation in cloud data centres | Jangiti, Saikishor |
| 2020-12 | Ensemble Gaussian mixture model-based special voice command cognitive computing intelligent system | Jangiti, Saikishor |
| 2020-08 | Ensemble Gaussian mixture model-based special voice command cognitive computing intelligent system | Jangiti, Saikishor |
| 2020 | Hybrid Best-Fit Heuristic for Energy Efficient Virtual Machine Placement in Cloud Data Centers | Jangiti, Saikishor |
| 2018 | Incremental MapReduce for K-Medoids Clustering of Big Time-Series Data | Jangiti, Saikishor |
| 2019-11 | Resource ratio based virtual machine placement in heterogeneous cloud data centres | Jangiti, Saikishor |
| 2017 | The role of cloud computing infrastructure elasticity in energy efficient management of datacenters | Jangiti, Saikishor |
| 2018 | Scalable and direct vector bin-packing heuristic based on residual resource ratios for virtual machine placement in cloud data centers | Jangiti, Saikishor |
| 2019-05 | Scalable hybrid and ensemble heuristics for economic virtual resource allocation in cloud and fog cyber-physical systems | Jangiti, Saikishor |
| 2018-08 | Virtual Machine (VM) placement consolidates VMs into a minimum number of Physical Machines (PMs), which can be viewed as a Vector Bin-Packing (VBP) problem. Recent literature reveals the significance of first-fit-decreasing variants in solving VBP problems, however they suffer from reduced packing efficiency and delayed packing speed. This paper presents VM NeAR (VM Nearest and Available to Residual resource ratios of PM), a novel heuristic method to address the above said challenges in VBP. Further, we have developed Bulk-Bin-Packing based VM Placement (BBPVP) and Multi-Capacity Bulk VM Placement (MCBVP) as a solution for VBP. The simulation results on real-time Amazon EC2 dataset and synthetic datasets obtained from CISH, SASTRA shows that VM NeAR based MCVBP achieves about 1.6% reduction in the number of PMs and possess a packing speed which was found to be 24 times faster than exisiting state-of-the-art VBP heuristics. | Jangiti, Saikishor |
| 2018 | Workflow Scheduling In Clouds Using Randomized Scheduling Algorithm | Jangiti, Saikishor |
