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Cloud computing is more popular these days, and the number of its users is increasing hence the security issues are also increasing. Cloud Computing is a relatively new concept that presents a good number of benefits for its users; however, it also raises some security problems which may slow down its use .The biggest issue in the cloud computing is security. This paper discuss about cloud service model, security issues in cloud computing. There are some security challenges like data security, network security and data transmission. Paper discuss about them and types of firewalls and how to implement firewall in the system that will help in cloud security.

Cloud computing, firewall, cloud security

[1] Krutika K. Shah Vahida U. Vadiya Rutvij H. Jhaveri journal on A Survey Paper on Security in Cloud Computing: A Bibliographic Analysis
[2] Krishan Kant Lavania,” International Journal on Recent and Innovation Trends in Computing and Communication” Volume: 1 Issue: 3 161 –163
[3] R. H. Sakr, F. Omara, O. Nomir“An Optimized Technique for Secure Data Over Cloud OS” International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Volume 3, Issue 3, May-June 2014 ISSN 2278-6856
[4] Rajnish Choubey, Rajshree Dubey , Joy Bhattacharjee “A Survey on Cloud Computing Security, Challenges and Threats International Journal on Computer Science and Engineering” (IJCSE) Vol. 3 No. 3 Mar 2011

Abhishek Langote, Omkar Dalal, Jyoti Kharade, "Security in cloud computing using firewall," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1092-1094, 2018.

Cloud computing is an elastic model which is used to satisfied changing needs of users. It provides pay as you go services (PaaS, SaaS, and IaaS) to the users. The growing trend of cloud computing has raises the concern of energy efficiency in cloud computing because a data center consumes lots of energy and emits carbon-dioxide in the environment. Today, the main focus of researcher has been diverted from cloud resource management to energy management. Various algorithms on VM allocation, migration, task scheduling and load balancing have been developed to ensure minimum energy dissipation in cloud data center. The main focus of this paper is study the existing algorithms and to analysis the best algorithms.

energy efficiency, vm-migration, load-balancing, vm-allocation

[1] Beloglazov, A., & Buyya, R, “Energy efficient resource management in virtualized cloud data centers,” Proceedings of the IEEE/ACM international conference on cluster, cloud and grid computing, pp. 826-831,2010.
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[3] ] Chien, N.k., et.al., “An Efficient Virtual Machine Migration Algorithm Based on Minimization of Migration in Cloud Computing”, International Conference on Nature of Computation and Communication ICTCC, Springer, pp. 62-71, 2016.
[4] Tziritas, N et.al, “ Application Aware Workload Consolidations to Minimize both Energy Consumption and Network Load in Cloud Environment”, 42th International conference on Parallel Processing, IEEE, 2013.
[5] Khan, M.A., et.al., “Dynamic Virtual Machine Consolidation Algorithms for Energy-Efficient Cloud Resource Management: A Review”, Springer International Publishing, pp.135-165,2018.
[6] Beloglazov A, Abawajy J, Buyya R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst. 2012;28(5):755–68.
[7] Selim GEI, El-Rashidy MA, El-Fishawy NA, editors. An efficient resource utilization technique for consolidation of virtual machines in cloud computing environments. In: 2016 33rd national radio science conference (NRSC). 22–25 Feb 2016.
[8] Beloglazov A, Abawajy J, Buyya R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst. 2012;28(5):755–68.
[9] Beloglazov A, Buyya R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr Comput. 2012;24(13):1397–420.
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consolidation. Proceedings of the 2009 USENIX Annual Technical Conference, San Diego, CA, USA, 2009; 28–28.
[12] Gao,Y et.al., “A multi-objective ant colony system algorithm for virtual machine placement in cloud
computing”, Journal of Computer and System Sciences, Elsevier, 2013
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[14] Varasteh A, Goudarzi M. Server consolidation techniques in virtualized data centers: a survey. IEEE Syst J. 2015;11(2):772–83.
[15] S. Martello, P. Toth, "Knapsack Problems–Algorithms and Computer Implementations", John Wiley & Sons, 1990
[16] N. Tziritas, C.-Z. Xu, T. Loukopoulos, S. U. Khan, Z. Yu, "Application-aware Workload Consolidation to Minimize both Energy Consumption and Network Load in Cloud Environments", 42nd IEEE International Conference on Parallel Processing (ICPP), 2013
[17] N. Quang-Hung, N. Thoai,, N. Son, "Epobf: Energy efficient allocation of virtual machines in high performance computing", Journal of Science and Technology, Vietnamese Academy of Science and Technology, Special on International Conference on Advanced Computing and Applications (ACOMP2013), Volume 51, pp 173-182, 2013

R. Garg, "Survey on Energy-Aware Cloud Computing Algorithms: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1095-1099, 2018.

Vehicular Ad hoc Networks (VANETs) are the promising approach to provide traffic, safety and other applications to the drivers as well as passengers. It becomes a key component of the intelligent transport system. Communication is not for only reliable data delivery but also the achieving the reliability. In this paper, we present how to obtain best communication in VANET system by using Transitive Trust Relationships concept and thus improving the performance of the authentication procedure of the whole network in a cluster. Proposed NTBS protocol not only adapts the concept of Transitive Trust Relationships but also improves the performance of the authentication procedure and it provides best communication with good security. We implement NTBS protocol in NS2 Simulator. NS2 is open source and discrete event-driven, object-oriented and freely available simulation tool to simulate and analyze dynamic nature of communication networks. it is also a powerful tool to develop new protocols and functions. It provides support for OSI and TCP/IP protocols stack and many standard routing and application protocols for wire and wireless networks. NAM is used to display the process of simulation.

VANET, TTR, NS2, NAM, TCL, NTBS, OBU, RSU, AS, Clustering Protocol

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[27] W. Fan, Y. Shi, S.chen, and L. Zou, “A mobility metric based dynamic clustering algorithm (DCA) for VAETS”, in International Conference on Communication Technology and Application, Beijing, pp.752-756, 2011.
[28] Zhenxia Zhang, Azzedine Boukerche, and Richards Pazzi, “A novel multi-hop clustering scheme for vehicular ad hoc networks”, In the Ptoceedings of the 9th ACM international symposium on Mobility management and wireless access, ACM, pp.19-26, 2011.
[29] Mohamed Nidhal Merji, Jalel Ben-Othman, Mohamed Hamdi, “A Survey on VANET security challenges and possible cryptographic solutions”, Vehicular Communications, Vol.1, Issue.2, pp.53-66, 2014.
[30] Jinyuan Sun, Chi Zhang, Yanchao Zhang, and Yaguang Fang, “An Identity-Based Security System for User Privacy in Vehicular Ad Hoc Networks”, Parallel and Distributed Systems, IEEE Transactions on, Vol.21, Issue.9, pp.1227-1239, 2010.
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Venkatamangarao Nampally, M. Raghavender Sharma, "Invention and Implementation of NTBS Clustering Protocol for VANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1100-1110, 2018.

Identification, and classification of flower images is a crucial issue faced by academicians and researchers. The manual process to distinguish different flower images is a complex task and found difficult for novice persons. A process of extraction, analysis, and understanding of useful information from images is accomplished by an automated process using Computer vision. It basically aims to model, replicate and exceed human vision using computer hardware and software. Image processing techniques may help to recognize a flower image for further identification and classification of them in different species. The fundamental step in image processing is image preprocessing that is applied to improve the quality of images and removing the irrelevant noises existed in images. This paper represents a comparative analysis of different image preprocessing techniques implemented on flower images. The performance evaluation of these techniques is based on their potential to remove noise in flower images. For performance evaluation, Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) methods are used.

Image processing, Image preprocessing techniques, PSNR, RMSE

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[16] Meenal Jain, Sumit Sharma, Ravi Mohan Sairam, “Result Analysis of Blur and Noise on Image Denoising based on PDE”, International Journal of Advanced Computer Research (IJACR) Volume-2, Issue-7, pp. 70-77, December-2012.
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[25] Mohd. Junedul Haque, “A Brief Review of Image Restoration Techniques”, International Journal of Advanced Computing Research, Volume 01, pp. 42-45, 2014.
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Isha Patel, Sanskruti Patel, Atul Patel, "Analysis of Various Image Preprocessing Techniques for Denoising of Flower Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1111-1117, 2018.

A load balancing method is proposed for the long term evolution networks. Today the traffic on networks is growing because of huge requirement of higher data rates for e.g. for Video streaming, Video conferencing, Online gaming etc. This demand results in excess load on the networks due to which the networks suffer from the heavy load and the quality of networks get degraded. The Load balancing is achieved between the loaded cell and its neighbor cells which are directly connected to each other and one of them have enough available resources so that the load can be shared with the neighbor node. In this work, we have proposed a method for load balancing in LTE networks which shows the simulation of two scenarios in which the handover is done between the LTE to LTE and LTE to Non LTE network nodes done.

LTE Networks, Mobility, Handover, Load Balancing

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B. Singh, M. Yadav, "A study of X2 and S1 Handover in LTE Networks using MATLAB," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1118-1122, 2018.

Cloud computing gives shared pool of assets (computers resources like networks, server and storage) on the demand of the user in ubiquitous and simple way that can be provisioned to the user with a very little management effort. The basic concept of cloud computing can be understood by the following definition according to NIST. It can be concluded from the above discussions that the demands required by the user are fulfilled by the cloud computing. These demands include both hardware and software resources which are present on the internet. A shared pool of resources is provided by the cloud computing provider which can be accessed by the users as per their demands. The users subscribe as per their requirements and access the resources until it wants to. Virtualization is the technique which helps in providing such services and also in reducing the cost of implementation and also adding hardware parts which will help in meeting the requirements of the user. The PDPT and NTBPRET are the techniques which ensure the data integrity.

Mobile cloud computing, secure data distribution, data integrity, access control, proxy re-encryption.

[1] Shui Han, Jianchuan Xing, “Ensuring Data Storage Through A Novel Third Party Auditor Scheme in Cloud Computing” , 2011, IEEE computer science & Technology, pp 264-268.
[2] Bhavna Makhija, VinitKumar Gupta, “Enhanced Data Security in Cloud Computing with Third Party Auditor”, 2013, International Journal of Advanced Research in Computer Science and Software Engineering, pp 341-345
[3] Sultan Aldossary, William Allen, “Data Security, Privacy, Availability and Integrity in Cloud Computing: Issues and Current Solutions”, (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 7, pp. 485-492, 2016.
[4] Saranya Eswaran, Dr. Sunitha Abburu, “Identifying Data Integrity in the Cloud Storage”, IJCSI International Journal of Computer Science Issues, vol. 9, pp. 403-408, 2012.
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[9] Xuefeng Liu, Wenhai Sun, Wenjing Lou, Qingqi Pei, Yuqing Zhang, “One-tag Checker: Message-locked Integrity Auditing on Encrypted Cloud Deduplication Storage”, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications
[10] Andrey N. Rukavitsyn, Konstantin A. Borisenko, Ivan I. Holod, Andrey V. Shorov, “The Method of Ensuring Confidentiality and Integrity Data in Cloud Computing”, 2017, IEEE
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[12] Benoˆıt Libert and Damien Vergnaud. Unidirectional chosenciphertext secure proxy re-encryption. Information Theory, IEEE Transactions on, 57(3):1786 –1802, march 2011.
[13] Qiang Tang. Type-based proxy re-encryption and its construction. In DipanwitaRoy Chowdhury, Vincent Rijmen, and Abhijit Das,editors, Progress in Cryptology - INDOCRYPT 2008, volume 5365 of Lecture Notes in Computer Science, pages 130–144. Springer Berlin Heidelberg, 2008.
[14] Parsi, K., & M.Laharika, “A Comparative Study of Different Deployment Models in a Cloud”, 2013, International Journal of Advanced Research in Computer Science and Software Engineering , 3 (5), 512-515.
[15] Srinivas.J, K. Venkata Subba Reddy, Dr. A. Moiz Qyser, “Cloud Computing Basics”, 2012, International journal of advanced research in computer and communication engineering , pp. 343-347
[16] Seyyed Mansur Hosseini and Mostafa Ghobaei Arani,” Fault-Tolerance Techniques in Cloud Storage: A Survey”, 2015, International Journal of Database Theory and Application, Vol.8, No.4 (2015), pp.183-190
[17] SookKyong Choi, KwangSik Chung, Heonchang Yu,” Fault tolerance and QoS scheduling using CAN in mobile social cloud computing”, 2013, Springer Science+Business Media New York
[18] Dr. Lakshmi Prasad Saikia, Yumnam Langlen Devi,” FAULT TOLEREANE TECHNIQUES AND ALGORITHMS IN CLOUD COMPUTING”, International Journal of Computer Science & Communication Networks, Vol 4(1),01-08
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[22] Sanjoli Singla, Jasmeet Singh, 2013 “Cloud Data Security using Authentication and Encryption Technique” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 7, July 2013, pp 2232-2235
[23] Tushar Desai, Jignesh Prajapati, “A Survey of Various Load Balancing Techniques and Challenges in Cloud Computing”, INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 2, ISSUE 11, NOVEMBER 2013
[24] Vimmi Pandey, “Securing the Cloud Environment Using OTP” International Journal of Scientific Research in Computer Science and Engineering vol-1, Issue-4, 2013
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[26] Jiang Zhang, Zhenfeng Zhang, Hui Guo. "Towards Secure Data Distribution Systems in Mobile Cloud Computing", IEEE Transactions on Mobile Computing, 2017
[27] RezaOsamaRandal CurtmolaKhanBurns. "Robust remote data checking", Proceedings of the 4th ACM international workshop on Storage security and survivability - StorageSS 08 StorageSS 08, 2008
[28] ZHU, YAN, SHANBIAO WANG, HONGXIN HU, GAIL-JOON AHN, and DI MA. "SECURE COLLABORATIVE INTEGRITY VERIFICATION FOR HYBRID CLOUD ENVIRONMENTS", International Journal of Cooperative Information Systems, 2012.

Ankit Chamoli, Anshika Goyal , "Data Integrity and Performance Comparison of New Type- Based Proxy Re-encryption (TB-PRE) and Provable Data Possession (PDP) in Mobile Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1123-1128, 2018.

India being a developing country produces a large amount of CO2, Major portion of which includes Electricity production. This paper aims to bring light to the technologies related to Smart Grid Systems and Smart homes which can be incorporated to reduce energy usage. Smart Grid is a technology to decentralize energy supply and communicate with users to dynamically set prices to reduce load on the power grid. Smart Home Technology automates and controls all electrical devices connected in the network. The combination of these two technologies can help reduce load on Power Grid and thus save energy consumption.

Smart homes, smart grid, smart home energy managment system, real-time pricing, Internet of Things

[1] Allcot, H.t: “Real time pricing and electricity markets”, Harvard University, 2009.
[2] CEN-CENELEC-ETSI Smart Grid Coordination Group: “Reference Architecture for the Smart Grid” (SG CG/RA Smart Grid Reference Architecture), Brussels, 2012.
[3] Federal Energy Regulatory Commission: “Assessment of demand re- sponse and advanced metering”
http://www.ferc. gov/legal/staff-reports/2010-dr-report.pdf, 2011
[4] Q. Hu, L. Fi., “Hardware Design of Smart Home Energy Management System With Dynamic Price Response”, IEEE Transactions on Smart Grid Vol. 4, pp.1878-1887, 2013.
[5] K.M. Tsui, K., S.C. Chan, “Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing”, IEEE Transactions on Smart Grid Vol. 3 No. 4, pp.1812-1821, 2012.
[6] M. Cabras, V. Pilloni, A. Luigi, “A Novel Smart Home Energy Management System: Cooperative Neighbourhood and Adaptive Renewable Energy Usage” IEEE International Conference on Communications,India, pp.716 – 721, 2015.
[7] Gov. Of India “CO2 Baseline Database for the Indian Power Sector”, User Guide Version 12.0, May 2017.
[8] Gov. Of India, Ministery of Power Central Authority “Summary for month of Jan 2017”, Jan 2017
[9] G. Song, F. Ding, W. Zhang, A. Song, “A wireless power outlet system for smart homes”. IEEE Trans. Consum. Electron., vol. 54, no.4, pp.1688–1691, 2008.
[10] P. Wang, J. Huang , Y. Ding, , P. Loh , L. Goel, “Demand side load management of smart grids using intelligent trading/metering/billing system”, IEEE Power Energy Soc. Gen. Meet 2010, pp.1–6, 2010.
[11] D. Cook , M. Youngblood , E. Heierman , K. Gopalratnam, S. Rao, A. Litvin, F. Khawaja, “Mavhom: An agent-based smart home”, in Proc. IEEE PerCom 2003, pp.521–524, 2003.

A. Gupta, T. Garg, S.D. Chaudhary, P. Paygude, "Smart Home Energy Management Systems: A Literature survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1129-1132, 2018.

Patters in mobile agents help to design different applications which have non-permanent connections by adding the mobility code, improved networks, goal sustainability and machine based intelligence. Also the agent patterns mainly provide easy methods of developing the agent-based applications which are used or operated in open and large-scale distributed environments. It can provide various facilities like mobility, working in intermittent connections, autonomous execution, and many more that can allow sustainability of mobile agent based systems amidst of existing technologies like client-server. This paper gives an overview of the patterns of the mobile agents in mobile computing. Also the discussion covers the patterns and their usage, architectures, languages, applications, and the implementation challenges that are likely to be faced. This paper mainly discusses mobile agent which is a new technology that can be used in designing, implementing, and maintaining distributed systems. The discussion covers the patterns for mobile agents in the mobile computing applications.

distributed computing, agents patterns, agent oriented programming

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[3] Chowhan R. S., “Mobile Agent based Workflow Management System (MAWFMS) for Information Flow and Business Processes.” Orient.J. Comp. Sci. and Technol;Vol.11(1), 2018
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[9] Wang, Xiaofei, Min Chen, Taekyoung Kwon, and Han-Chieh Chao. “Multiple mobile agents` itinerary planning in wireless sensor networks: survey and evaluation.”, IET communications Vol.5, no.12 ,pp.1769-1776, 2011.
[10] Chen, Min, Wei Cai, Sergio Gonzalez, and Victor CM Leung. “Balanced itinerary planning for multiple mobile agents in wireless sensor networks.” In International Conference on Ad Hoc Networks, pp. 416-428. Springer, Berlin, Heidelberg, 2010.
[11] Cai, Wei, Min Chen, Takahiro Hara, and Lei Shu. “GA-MIP: genetic algorithm based multiple mobile agents itinerary planning in wireless sensor networks.” In Wireless Internet Conference (WICON), The 5th Annual ICST, pp.1-8. IEEE, 2010.
[12] Chen, Min, Laurence T. Yang, Taekyoung Kwon, Liang Zhou, and Minho Jo. "Itinerary planning for energy-efficient agent communications in wireless sensor networks." IEEE Transactions on Vehicular Technology Vol.60, no.7, pp.3290-3299, 2011.
[13] Aloui, Imene, Okba Kazar, Laid Kahloul, and Sylvie Servigne. “A new Itinerary planning approach among multiple mobile agents in wireless sensor networks (WSN) to reduce energy consumption.” International Journal of Communication Networks and Information Security Vol.7, no.2, 2015.
[14] Cai, Wei, Min Chen, Takahiro Hara, Lei Shu, and Taekyoung Kwon. “A genetic algorithm approach to multi-agent itinerary planning in wireless sensor networks.” Mobile Networks and Applications Vol.16, no.6, pp.782-793, 2011.
[15] Nestinger, Stephen S., Bo Chen, and Harry H. Cheng. “A mobile agent-based framework for flexible automation systems.” IEEE/Asme Transactions on Mechatronics Vol.15, no.6 pp.942-951, 2010.
[16] Miller, Naomi Liora, Harold Roy Miller, and Warren Stableford. “Translation of user requests into itinerary solutions.” U.S. Patent 9,659,099, issued May 23, 2017.
[17] Rahul Singh Chowhan and Rajesh Purohit, “Study of mobile agent server architectures for homogeneous and heterogeneous distributed systems.” International Journal of Computer Applications Vol.156, no. 4,pp.32-37, 2016.
[18] Rahul Singh Chowhan, Amit Mishra, and Ajay Mathur, “Aglet and kerrighed as a tool for load balancing and scheduling in distributed environment.” In Recent Advances and Innovations in Engineering (ICRAIE), International Conference on, pp.1-6. IEEE, 2016.
[19] Bagga, Pallavi, and Rahul Hans. “Applications of mobile agents in healthcare domain: a literature survey.” International Journal of Grid Distribution Computing Vol.8, no. 5, pp.55-72, 2015.
[20] Dinh, Hoang T., Chonho Lee, Dusit Niyato, and Ping Wang. “A survey of mobile cloud computing: architecture, applications, and approaches.” Wireless communications and mobile computing Vol.13, no.18 pp.1587-1611, 2013.
[21] Jara, Antonio J., Miguel A. Zamora-Izquierdo, and Antonio F. Skarmeta. "Interconnection framework for mHealth and remote monitoring based on the internet of things." IEEE Journal on Selected Areas in Communications Vol.31, no. 9 pp.47-65, 2013.
[22] Shiraz, Muhammad, Abdullah Gani, Rashid Hafeez Khokhar, and Rajkumar Buyya. “A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing.” IEEE Communications Surveys & Tutorials Vol.15, no. 3, pp.1294-1313, 2013.
[23] Baig, Mirza Mansoor, Hamid Gholamhosseini, and Martin J. Connolly. "A comprehensive survey of wearable and wireless ECG monitoring systems for older adults." Medical & biological engineering & computing Vol.51, no. 5, pp. 485-495, 2013.
[24] Iwaya, Leonardo H., Marco AL Gomes, M. A. Simplício, T. C. M. B. Carvalho, Cristina K. Dominicini, Rony RM Sakuragui, Marina S. Rebelo, Marco Antonio Gutierrez, Mats Näslund, and P. Håkansson. “Mobile health in emerging countries: a survey of research initiatives in Brazil.” International journal of medical informatics Vol.82, no 5, pp.283-298, 2013.
[25] Bhaskar, B., Kumar T. Jagadish., Kamal, M.V., “A Security Determination-Reaction Architecture for Heterogeneous Distributed Network.”, IJSRCSE, Vol.5, Issue.5, pp.22-34, 2017.
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R.S. Chowhan, P. Dayya, "Itinerary and Mobile Code Patterns for Emerging Mobile Agent Systems in Large Scale Distributed Environments," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1133-1141, 2018.

According to literature meaning of Cloud Computing is distributed computing, storing, sharing, and accessing data over the internet. Cloud is the platform which provides numerous type of resources where the end user may use the resource for developing own software and even their own cloud and even include a new resource to the existing once. The biggest issue for a cloud datacentre is to tackle with billions of request coming dynamically from the end users to handle their database in efficient and effective manner. To achieve this goal, various load balancing approaches have been proposed in past years. Database load balancing strategies aim at achieving high software developer satisfaction by producing service like auto scale of their data in database, zero-downtime, multiple database choices, multi tenancy support. Load balancing in this environment means equal distribution of workload across instances. End users needs ample space to store their database data to decrease the maintenance cost and buying cost of servers and area required to assemble them this paper focus on the balancing the data in database. This paper, focus on database based load balancing which works well in cloud environment, considers resources specific demands of the tasks and reduces overflow of data overhead by dividing the data on running instances.

Auto scalability, Cloud computing, load balancer, Multi tenancy, Snapshot, Zero downtime

[1]. Z. Gong, X. Gu, and X. Ma. Siglm: Signature-driven load management for cloud computing infrastructures. In Proc. IEEE International Conference on Quality of Service (IWQoS), Charleston, South Carolina, 2009
[2]. Ha`c and X. Jin. Dynamic load balancing in distributed system using a decentralized algorithm. In Intl. Conf. on Distributed Computing Systems, 1987.
[3]. Mahajan, K., & Dahiya, D., “A Cloud Based Deployment Framework For Load Balancing Policies” IEEE seventh International Conference on Contemporary Computing, pp. 565-570, August 2014.
[4]. Sharma, S., Singh, S., & Sharma, M. “Performance Analysis Of Load Balancing Algorithms” World Academy of Science, Engineering and Technology, 38, pp. 269-272, 2008.
[5]. Rahman, M., Iqbal, S., & Gao, J., “Load Balancer as a Service in Cloud Computing”, IEEE 8th
[6]. Nuaimi, K. A., Mohamed, N., Nuaimi, M. A., & Al-Jaroodi, J., “A Survey of Load Balancing in Cloud Computing: Challenges and Algorithms” IEEE second symposium on Network Cloud Computing, pp.137-142,December2012.

Shital Pawar, Sadiya Shaikh, Priyanka Wadagave, Megha Chavan, Deepika Tambat, "Online Database Load Balancer to Collaborating With Existing Database," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1142-146, 2018.

US 413 (Al-12Si) alloy volume deficit characteristic has been studied in this paper. Influence of pouring temperature on volume deficit is studied and latter is addition of Macro cavities, Internal porosity, Surface sink and Volumetric contraction. The decrease in definite volume of molten metal leads to volume deficit in castings, and it can be envisaged as a casting defect. Volume deficit of a casting depends on casting material and casting conditions. Influence of pouring temperature and casting shape on the volume deficit characteristics are studied in this paper. Increase in pouring temperature lowers the rate of heat extraction by the mould thereby reducing volume deficit.

Volume deficit, pouring temperature, Macro cavities, Internal porosity, Surface sinks, Volumetric contraction, Al-Si alloy

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Samavedam Santhi, "Affect of pouring temperature on volume deficit of US 413," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1147-1150, 2018.