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Issue Date	Title	Author(s)
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	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
2019-11	Resource ratio based virtual machine placement in heterogeneous cloud data centres	Jangiti, Saikishor
2019	Bulk-bin-packing based migration management of reserved virtual machine requests for green cloud computing	Jangiti, Saikishor
2017	The role of cloud computing infrastructure elasticity in energy efficient management of datacenters	Jangiti, Saikishor
2015	BEST ELECTRONIC SHOPPING TECHNIQUE (BEST)-AN ADHOC COMPONENT USING BAT MODEL	Jangiti, Saikishor
2019	Automated question extraction and tagging for cloud-based online communities	Jangiti, Saikishor
2014	DMK-Medoid Heuristic Product Ranking in Online Market	Jangiti, Saikishor
2018	Workflow Scheduling In Clouds Using Randomized Scheduling Algorithm	Jangiti, Saikishor