A Review Paper on Face Recognition System

Author: Ritu khatri

Abstract—Massive-scale cloud data centers host many applications and comprise of millions of servers thus consuming more power than ever. In order to efficiently manage the power usage of these data centers, Green computing offers schemes like load balancing across physical machines, live migration of virtual machines and Sever Consolidation which aims at minimizing the number of Active Physical Machines (APM). Server consolidation is a result of Virtual Machine (VM) scheduling which involves— VM selection, VM placement and VM placement re-optimization. In this paper, we present a VM placement optimization technique used in green cloud, particularly based on the classical problem of Bin Packing. Bin packing is inspired by the NP-Hard knapsack problem and reduces the total number of Active Physical Machines (APM). Further these placements are optimized using Rank based VM scheduling algorithm. The proposed approach subsequently reduces the energy consumption and provides improved server consolidation.

Keywords— Cloud Computing, Data Center, Load Balancing, Server Consolidation, Virtual Machine Scheduling.

[1] SONG, WEIJIA, ET AL. "ADAPTIVE RESOURCE PROVISIONING FOR THE CLOUD USING ONLINE BIN PACKING." COMPUTERS, IEEE TRANSACTIONS ON 63.11 (2014): 2647-2660.

[2] Ghribi, Chaima, Makhlouf Hadji, and Djamal Zeghlache. "Energy efficient vm scheduling for cloud data centers: Exact allocation and migration algorithms."Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on. IEEE, 2013.

[3] Alahmadi, Ahmed, et al. "Enhanced First-Fit Decreasing Algorithm for Energy-Aware Job Scheduling in Cloud." Computational Science and Computational Intelligence (CSCI), 2014 International Conference on. Vol. 2. IEEE, 2014.

[4] Zhang, Yan, and Nayeem Ansari. "Heterogeneity aware dominant resource assistant heuristics for virtual machine consolidation." Global Communications Conference (GLOBECOM), 2013 IEEE. IEEE, 2013.

[5] Dong, Jiankang, Hongbo Wang, and Shiduan Cheng. "Energyperformance tradeoffs in IaaS cloud with virtual machine scheduling." Communications, China 12.2 (2015): 155-166.

[6] Li, Mingfu, et al. "Unveiling the resource consumption overhead of virtual machine consolidation in data centers." Global Communications Conference (GLOBECOM), 2012 IEEE. IEEE, 2012.

[7] Bobroff, Norman, Andrzej Kochut, and Kirk Beaty. "Dynamic placement of virtual machines for managing sla violations." Integrated Network Management, 2007. IM'07. 10th IFIP/IEEE International Symposium on. IEEE, 2007.

[8] Wang, Meng, Xiaoqiao Meng, and Li Zhang. "Consolidating virtual machines with dynamic bandwidth demand in data centers." INFOCOM, 2011 Proceedings IEEE. IEEE, 2011.

[9] Chen,Ming,et al. "Effective VM sizing in virtualized data centers." Integrated Network Management (IM), 2011 IFIP/IEEE International Symposium on. IEEE, 2011.

[10] Srikantaiah, Shekhar, Aman Kansal, and Feng Zhao. "Energy aware consolidation for cloud computing." Proceedings of the 2008 conference on Power aware computing and systems. Vol. 10. 2008.

[11] Wood, Timothy, et al. "Sandpiper: Black-box and gray-box resource management for virtual machines." Computer Networks 53.17 (2009): 2923-2938.

[12] Beloglazov, Anton, and Rajkumar Buyya. "Adaptive thresholdbased approach for energy-efficient consolidation of virtual machines in cloud data centers."Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and eScience. Vol. 4. ACM, 2010.

[13] Li, Kangkang, Huanyang Zheng, and Jie Wu. "Migration-based virtual machine placement in cloud systems." Cloud Networking (CloudNet), 2013 IEEE 2nd International Conference on. IEEE, 2013.

[14] Bein, Doina, Wolfgang Bein, and Swathi Venigella. "Cloud storage and online bin packing." Intelligent Distributed Computing V. Springer Berlin Heidelberg, 2012. 63-68.

[15] Singh, Aameek, Madhukar Korupolu, and Dushmanta Mohapatra. "Server-storage virtualization: integration and load balancing in data centers."Proceedings of the 2008 ACM/IEEE conference on Supercomputing. IEEE Press, 2008.

[16] Ho, Yufan, Pangfeng Liu, and Jan-Jan Wu. "Server consolidation algorithms with bounded migration cost and performance guarantees in cloud computing."Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on. IEEE, 2011.

[17] Lin, Ching-Chi, Pangfeng Liu, and Jan-Jan Wu. "Energy-efficient virtual machine provision algorithms for cloud systems." Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on. IEEE, 2011.

[18] Beloglazov, Anton, and Rajkumar Buyya. "Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers." Concurrency and Computation: Practice and Experience 24.13 (2012): 1397-1420.

[19] A. Basu, H. B. Manasa. ―Energy Aware Resource Allocation in Cloud Data Centers .― International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-5, June 2013

[20] Buyya, Rajkumar, Anton Beloglazov, and Jemal Abawajy. "Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges." arXiv preprint arXiv:1006.0308(2010).

[21] Mishra, Mayank, and Anirudha Sahoo. "On theory of vm placement: Anomalies in existing methodologies and their mitigation using a novel vector based approach." Cloud Computing (CLOUD), 2011 IEEE International Conference on. IEEE, 2011.

[22] Gambosi, Giorgio, Alberto Postiglione, and Maurizio Talamo. "Algorithms for the relaxed online bin-packing model." SIAM journal on computing 30.5 (2000): 1532-1551.

[23] Zoha Usmani and Shailendra Singh / Procedia Computer Science 78 ( 2016 ) 491 – 498, Paper Title, A Survey of Virtual Machine Placement Techniques in a Cloud Data Center in Conference ―International Conference on Information Security & Privacy (ICISP2015), 11-12 December 2015, Nagpur, INDIA‖.

An Experimental Investigation of Outlet Performance on CI Engine Filled with Soyabean Blended with Methanol

Authors: Dr. Hiregoudar Yerrennagoudaru, Manjunatha.k , Shiva kumar.S, S.Veeresh kumar

Abstract—All over the world the use of petroleum products has increased day by day. The Vehicle population is also increasing day by day. The vehicle population has tremendously increased in the recent year with the explosion of vehicle population in the Worldwide. As the vehicle population increases the use of fossil fuels like petrol and Diesel has also increased tremendously. The present automobile industry like petrol engine, diesel engine uses the fossil fuel like petrol and diesel as fuel. The pollution caused by this fossil fuel has much more effect on environmental air pollution. All over the world many researchers are trying to reduce the emission from these engines to protect the environment from air pollution. For the above mentioned problem many researchers have develop the catalyst converter to reduce the exhaust emission like CO, HC and NOX. Generally CO, HC, NOx, SO2, particulate matters and smoke are treated as emissions from the exhaust gas of engines. Among these emissions, HC and CO are more toxicin nature which leads to air pollution. The diesel engine being predominant than petrol engine in developed countries like India, these engines are widely used in the sectors like agriculture,power and automobile.Finally we conclude that use of ethanol blended fuels resulted in reduced usage of petroleum products .Main aim of the project is to reduce emissions like HC, CO, CO2,NOx, SOX from the exhaust gases of diesel engine.Vegetable oils are considered as good alternatives to diesel as their properties are close to diesel. Thus they offer the advantage of being used readily in existing diesel engines without modifications. Hence to meet these requirements humans have to look towards alternatives to the petroleum based fuels like petrol and diesel. This project tries to find an alternate to diesel fuel. Initially base line data was generated with diesel and neat Soyabean oil. Subsequently, Soyabean oil was converted into its methyl ester by transesterification to minimize the problems of volatility, viscosity and polyunsaturated characters faced while using crude oil.

Keywords— CO, HC, NOx, SO2, NOx, SO X

References-

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Energy Efficient Dynamic Resource Scheduling for Cloud Data Center

Authors: Rakesh Kumar Vishwarkarma, Dr. Syed Imran Ali, Anidra Katiyar

Abstract—Massive-scale cloud data centers host many applications and comprise of millions of servers thus consuming more power than ever. In order to efficiently manage the power usage of these data centers, Green computing offers schemes like load balancing across physical machines, live migration of virtual machines and Sever Consolidation which aims at minimizing the number of Active Physical Machines (APM). Server consolidation is a result of Virtual Machine (VM) scheduling which involves— VM selection, VM placement and VM placement re-optimization. In this paper, we present a VM placement optimization technique used in green cloud, particularly based on the classical problem of Bin Packing. Bin packing is inspired by the NP-Hard knapsack problem and reduces the total number of Active Physical Machines (APM). Further these placements are optimized using Rank based VM scheduling algorithm. The proposed approach subsequently reduces the energy consumption and provides improved server consolidation.

Keywords— Cloud Computing, Data Center, Load Balancing, Server Consolidation, Virtual Machine Scheduling.

[1] SONG, WEIJIA, ET AL. "ADAPTIVE RESOURCE PROVISIONING FOR THE CLOUD USING ONLINE BIN PACKING." COMPUTERS, IEEE TRANSACTIONS ON 63.11 (2014): 2647-2660.

[2] Ghribi, Chaima, Makhlouf Hadji, and Djamal Zeghlache. "Energy efficient vm scheduling for cloud data centers: Exact allocation and migration algorithms."Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on. IEEE, 2013.

[3] Alahmadi, Ahmed, et al. "Enhanced First-Fit Decreasing Algorithm for Energy-Aware Job Scheduling in Cloud." Computational Science and Computational Intelligence (CSCI), 2014 International Conference on. Vol. 2. IEEE, 2014.

[4] Zhang, Yan, and Nayeem Ansari. "Heterogeneity aware dominant resource assistant heuristics for virtual machine consolidation." Global Communications Conference (GLOBECOM), 2013 IEEE. IEEE, 2013.

[5] Dong, Jiankang, Hongbo Wang, and Shiduan Cheng. "Energyperformance tradeoffs in IaaS cloud with virtual machine scheduling." Communications, China 12.2 (2015): 155-166.

[6] Li, Mingfu, et al. "Unveiling the resource consumption overhead of virtual machine consolidation in data centers." Global Communications Conference (GLOBECOM), 2012 IEEE. IEEE, 2012.

[7] Bobroff, Norman, Andrzej Kochut, and Kirk Beaty. "Dynamic placement of virtual machines for managing sla violations." Integrated Network Management, 2007. IM'07. 10th IFIP/IEEE International Symposium on. IEEE, 2007.

[8] Wang, Meng, Xiaoqiao Meng, and Li Zhang. "Consolidating virtual machines with dynamic bandwidth demand in data centers." INFOCOM, 2011 Proceedings IEEE. IEEE, 2011.

[9] Chen,Ming,et al. "Effective VM sizing in virtualized data centers." Integrated Network Management (IM), 2011 IFIP/IEEE International Symposium on. IEEE, 2011.

[10] Srikantaiah, Shekhar, Aman Kansal, and Feng Zhao. "Energy aware consolidation for cloud computing." Proceedings of the 2008 conference on Power aware computing and systems. Vol. 10. 2008.

[11] Wood, Timothy, et al. "Sandpiper: Black-box and gray-box resource management for virtual machines." Computer Networks 53.17 (2009): 2923-2938.

[12] Beloglazov, Anton, and Rajkumar Buyya. "Adaptive thresholdbased approach for energy-efficient consolidation of virtual machines in cloud data centers."Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and eScience. Vol. 4. ACM, 2010.

[13] Li, Kangkang, Huanyang Zheng, and Jie Wu. "Migration-based virtual machine placement in cloud systems." Cloud Networking (CloudNet), 2013 IEEE 2nd International Conference on. IEEE, 2013.

[14] Bein, Doina, Wolfgang Bein, and Swathi Venigella. "Cloud storage and online bin packing." Intelligent Distributed Computing V. Springer Berlin Heidelberg, 2012. 63-68.

[15] Singh, Aameek, Madhukar Korupolu, and Dushmanta Mohapatra. "Server-storage virtualization: integration and load balancing in data centers."Proceedings of the 2008 ACM/IEEE conference on Supercomputing. IEEE Press, 2008.

[16] Ho, Yufan, Pangfeng Liu, and Jan-Jan Wu. "Server consolidation algorithms with bounded migration cost and performance guarantees in cloud computing."Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on. IEEE, 2011.

[17] Lin, Ching-Chi, Pangfeng Liu, and Jan-Jan Wu. "Energy-efficient virtual machine provision algorithms for cloud systems." Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on. IEEE, 2011.

[18] Beloglazov, Anton, and Rajkumar Buyya. "Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers." Concurrency and Computation: Practice and Experience 24.13 (2012): 1397-1420.

[19] A. Basu, H. B. Manasa. ―Energy Aware Resource Allocation in Cloud Data Centers .― International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-5, June 2013

[20] Buyya, Rajkumar, Anton Beloglazov, and Jemal Abawajy. "Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges." arXiv preprint arXiv:1006.0308(2010).

[21] Mishra, Mayank, and Anirudha Sahoo. "On theory of vm placement: Anomalies in existing methodologies and their mitigation using a novel vector based approach." Cloud Computing (CLOUD), 2011 IEEE International Conference on. IEEE, 2011.

[22] Gambosi, Giorgio, Alberto Postiglione, and Maurizio Talamo. "Algorithms for the relaxed online bin-packing model." SIAM journal on computing 30.5 (2000): 1532-1551.

[23] Zoha Usmani and Shailendra Singh / Procedia Computer Science 78 ( 2016 ) 491 – 498, Paper Title, A Survey of Virtual Machine Placement Techniques in a Cloud Data Center in Conference ―International Conference on Information Security & Privacy (ICISP2015), 11-12 December 2015, Nagpur, INDIA‖.

Evaluation of Different Image Enhancement Methods - A Study

Authors: Pragati Shinde, Arun Kumar

Abstract- There are many definitions available for the term image enhancement, one of them is “Image enhancement is basically improving the interpretability or perception of information in images for human viewers and providing `better' input for other automated image processing techniques”. On other words the objective of image enhancement is to modify its features according to the requirements of processing space. While considering the above mentioned things it is clear that enhancement techniques are very relevant to the field where the processed image to be used, because of this several techniques are available for enhancement of image depending upon the use (like human perceptions, medical imagery or very complex radar systems). Another problem with enhancement techniques is that most of the method requires a properly denoised image otherwise the noise generated artifacts could also get enhanced. Hence, de-noising is often a necessary and the first step to be taken before the image data is analyzed. It is necessary to apply an efficient de-noising technique to compensate for such data corruption.

Keywords – NPR, Wavelet, ANN.

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