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Fault Tolerance in Cloud: A Brief Survey

Haribabu, K (Springer, 2022-03)
Replication Based Fault Tolerance Approach for Cloud

Haribabu, K (Springer, 2022-01)
SecureDL: A privacy preserving deep learning model for image recognition over cloud

Rajput, Amitesh Singh (Elsevier, 2022-07)
Privacy-preserving smart surveillance using local color correction and optimized ElGamal cryptosystem over cloud

Rajput, Amitesh Singh (IEEE, 2019)
CryptFine: Towards secure cloud based filtering in encrypted domain

Rajput, Amitesh Singh (IEEE, 2017)
Color me, store me, know me not: Supporting image color transfer and storage in encrypted domain over cloud

Rajput, Amitesh Singh (IEEE, 2017)
Example Based Privacy-Preserving Video Color Grading

Rajput, Amitesh Singh (Springer, 2019)
A Systematic Classification of Migration of Legacy Application to Cloud Services

Chandra Shekar, R.K. (IJCOT, 2017-10)
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 (Springer, 2018-08)
EMC2: Energy-efficient and multi-resource- fairness virtual machine consolidation in cloud data centres

Jangiti, Saikishor (Elsevier, 2020-09)

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