Enhanced Homomorphic Encryption Scheme with Particle Swarm Optimization for Encryption of Cloud Data

2020 ◽  
pp. 291-298
Author(s):  
Abhishek Mukherjee ◽  
Dhananjay Bisen ◽  
Praneet Saurabh ◽  
Lalit Kane
Keyword(s):  
Particle Swarm Optimization ◽  
Homomorphic Encryption ◽  
Particle Swarm ◽  
Encryption Scheme ◽  
Swarm Optimization ◽  
Cloud Data

scholarly journals Virtual Machine Placement via Bin Packing in Cloud Data Centers

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 389 ◽  
Author(s):  
Aisha Fatima ◽  
Nadeem Javaid ◽  
Tanzeela Sultana ◽  
Waqar Hussain ◽  
Muhammad Bilal ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Vm Placement ◽  
Swarm Optimization ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Vm Consolidation

With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.

scholarly journals Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing

2020 ◽  
Vol 13 (6) ◽  
pp. 42-51
Author(s):  
Archana Kollu ◽  
◽  
Sucharita Vadlamudi ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Energy Management ◽  
Optimization Technique ◽  
Service Level Agreement ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Cloud Data ◽  
Cauchy Mutation ◽  
Eagle Strategy

Energy management of the cloud datacentre is a challenging task, especially when the cloud server receives a number of the user’s request simultaneously. This requires an efficient method to optimally allocate the resources to the users. Resource allocation in cloud data centers need to be done in optimized manner for conserving energy keeping in view of Service Level Agreement (SLA). We propose, Eagle Strategy (ES) based Modified Particle Swarm Optimization (ES-MPSO) to minimize the energy consumption and SLA violation. The Eagle Strategy method is applied due to its efficient local optimization technique. The Cauchy Mutation method which schedules the task effectively and minimize energy consumption, is applied to the proposed ES-MPSO method for improving the convergence performance. The simulation result shows that the energy consumption of ES-MPSO is 42J and Particle Swarm Optimization (PSO) is 51J. The proposed method ES-MPSO achieves higher efficiency compared to the PSO method in terms of energy management and SLA.

scholarly journals Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area

2019 ◽  
Vol 11 (8) ◽  
pp. 980 ◽  
Author(s):  
Jiapeng Huang ◽  
Yanqiu Xing ◽  
Haotian You ◽  
Lei Qin ◽  
Jing Tian ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Background Noise ◽  
Spatial Clustering ◽  
Particle Swarm ◽  
Noise Filtering ◽  
Swarm Optimization ◽  
Cloud Data ◽  
Filtering Algorithm ◽  
Statistical Algorithm ◽  
The Mean

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), was launched successfully in 15 September 2018. The ATLAS represents a micro-pulse photon-counting laser system, which is expected to provide more comprehensive and scientific data for carbon storage. However, the ATLAS system is sensitive to the background noise, which poses a tremendous challenge to the photon cloud noise filtering. Moreover, the Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a commonly used algorithm for noise removal from the photon cloud but there has not been an in-depth study on its parameter selection yet. This paper presents an automatic photon cloud filtering algorithm based on the Particle Swarm Optimization (PSO) algorithm, which can be used to optimize the two key parameters of the DBSCAN algorithm instead of using the manual parameter adjustment. The Particle Swarm Optimization Density Based Spatial Clustering of Applications with Noise (PSODBSCAN) algorithm was tested at different laser intensities and laser pointing types using the MATLAS dataset of the forests located in Virginia, East Coast, and the West Coast, USA. The results showed that the PSODBSCAN algorithm and the localized statistical algorithm were effective in identifying the background noise and preserving the signal photons in the raw MATLAS data. Namely, the PSODBSCAN achieved the mean F value of 0.9759, and the localized statistical algorithm achieved the mean F value of 0.6978. For both laser pointing types and laser intensities, the proposed algorithm achieved better results than the localized statistical algorithm. Therefore, the PSODBSCAN algorithm could support the MATLAS photon cloud data noise filtering applicably without manually selecting parameters.

DPRA: Dynamic Power-Saving Resource Allocation for Cloud Data Center Using Particle Swarm Optimization

2018 ◽  
Vol 12 (2) ◽  
pp. 1554-1565 ◽  
Author(s):  
Li-Der Chou ◽  
Hui-Fan Chen ◽  
Fan-Hsun Tseng ◽  
Han-Chieh Chao ◽  
Yao-Jen Chang
Keyword(s):  
Resource Allocation ◽  
Particle Swarm Optimization ◽  
Data Center ◽  
Particle Swarm ◽  
Power Saving ◽  
Cloud Data Center ◽  
Swarm Optimization ◽  
Dynamic Power ◽  
Cloud Data

Region segmentation of point cloud data based on improved particle swarm optimization fuzzy clustering

2017 ◽  
Vol 25 (4) ◽  
pp. 1095-1105 ◽  
Author(s):  
王晓辉 WANG Xiao-hui ◽  
吴禄慎 WU Lu-shen ◽  
陈华伟 CHEN Hua-wei ◽  
史皓良 SHI Hao-liang
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Point Cloud ◽  
Particle Swarm ◽  
Point Cloud Data ◽  
Swarm Optimization ◽  
Region Segmentation ◽  
Improved Particle Swarm Optimization ◽  
Cloud Data
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