IMPROVEMENT OF ENERGY EFFICIENCY IN CLOUD COMPUTING BY LOAD BALANCING ALGORITHM

IMPROVEMENT OF ENERGY EFFICIENCY IN CLOUD COMPUTING BY LOAD BALANCING ALGORITHM

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  IMPROVEMENT OF ENERGYEFFICIENCY IN CLOUD COMPUTING BY LOAD BALANCING ALGORITHM ANIL PRASAD BARNWAL, RESEARCH SCHOLAR, SRI SATYA SAI UNIVERSITY OF TECHNOLOGY & MEDICAL SCIENCES, SEHORE, M.P. Dr V S DIXIT, Associate Professor, ARSD College, Delhi University. Abstract: - The concept of Genetic algorithm is specifically useful in load balancing for best virtual machines distribution across servers. In this paper, we focus on load balancing and also on efficient use of resources to reduce the energy consumption without degrading cloud performance. Cloud computing is an on demand service in which shared resources, information, software and other devices are provided according to the clients requirement at specific time. It‟s a term which is generally used in case of Internet. The whole Internet can be viewed as a cloud. Capital and operational costs can be cut using cloud computing. Cloud computing is defined as a large scale distributed computing paradigm that is driven by economics of scale in which a pool of abstracted virtualized dynamically scalable , managed computing power ,storage , platforms and services are delivered on demand to external customer over the internet. cloud computing is a recent field in the computational intelligence techniques which aims at surmounting the computational complexity and provides dynamically services using very large scalable and virtualized resources over the Internet. It is defined as a distributed system containing a collection of computing and communication resources located in distributed data enters which are shared by several end users. It has widely been adopted by the industry, though there are many existing issues like Load Balancing, Virtual Machine Migration, Server Consolidation, Energy Management, etc. Keywords: Cloud computing, load balancing, genetic algorithm, energy efficient management Cloud computing, Capital and operational costs, Cloud computing, Capital and operational costs INTRODUCTION:- Cloud computing is a promising technology that has a diverse association with distributed computing and grid computing. Cloud computing is the phrase used for the cloud users to acquire services through Internet connectivity as proposed by Mell and Grance (2011). Computer ISSN NO: 2279-543X Page No: 51 International Journal of Scientific Research and Review Volume 5 Issue 1 2016
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  researchers have inventedmany computing technologies only for on-demand resource requests. Grid computing has been designed to maximize the computing power through multiple virtualization techniques. Later, grid computing has been transformed to cloud computing with some service oriented enhancements for the resource virtualization through optimal computing methodologies. Many cloud service providers such as Google cloud bus, Drop box-cloud, Amazon EC2, IBM-cloud, Microsoft-Azure cloud are available for the normal cloud users all around the world for providing cloud based services as said by Cao et al (2014). Through a proper Internet connectivity, cloud users can easily access the above-mentioned cloud services and get the resources or services anytime and anywhere from the world. Based on the user's requirement, cloud computing technology delivers some services to access and store data with cloud-based applications as suggested by Buyya et al (2010). Further, it provides application design through platform oriented services, infrastructure to build and balancing the loads among multiple servers and also provides set of software for the end-users to work from their machines like Google docs. In cloud computing, various organizations are given by the cloud authority associations. SaaS – Software as a Service model sponsorships various applications rely upon the cloud and the business regards Anil et al., (2014 & 2015). Correspondingly, IaaS - Infrastructure as a Service given by the authority association serves to the cloud customer to get a bit of the organizations like securing records on 2 the cloud server, cloud server ranch and dealing with the load balancing issues by the cloud server. The PaaS – Platform as a Service supports different stage organized organizations, for instance, access to the databases from various working systems and application improvement through on the web. Cloud computing is an ongoing field in the computational insight methods which goes for surmounting the computational multifaceted nature and gives progressively administrations utilizing enormous adaptable and virtualized assets over the Internet. It is characterized as a dispersed framework containing an accumulation of computing and correspondence assets situated in appropriated information enters which are shared by a few end clients. It has generally been embraced by the business, however there are many existing issues like Load Balancing, Virtual Machine Migration, Server Consolidation, Energy Management, and so forth. Fundamental to these issues is the issue of load balancing that is a system to appropriate the ISSN NO: 2279-543X Page No: 52 International Journal of Scientific Research and Review Volume 5 Issue 1 2016
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  dynamic workload equitablyto every one of the hubs in the entire cloud to accomplish a high client fulfillment and asset usage proportion. In this examination the different and just the most efficient existing algorithms to beat the issues of load balancing has been talked about. With the touchy development of the utilization of cloud computing, the workload on servers is expanding quickly and servers may effortlessly be overloaded. REVIEW OF LITERATURE:- A short discussion on these strategies is given underneath: Krauter et al (2002) recommended about lattice resource management frameworks for dispersed computing. That was the key concept behind the cloud computing innovations. In the year 2011, cloud computing framework has been clearly characterized by Mell and Grance (2011) from the National Institute of Standards and Technology (NIST). Later, many researchers have displayed their work in the area of cloud computing. Ian Foster et al (2008) remarked the cloud computing with various points of view by establishing lattice computing paradigm with relevant advances. Birman et al (2009) recommended about the cloud computing models and cloud services through various research ideas based on the disseminated frameworks concept Rajkumar Buyya et al (2009) informed a detailed report on the cloud computing as the fifth utility in human life. In that the cloud computing or at the end of the day the Internet , is said to change the human life into a reality. Rajkumar Buyya et al (2010) exhibited ideas on Inter cloud for cloud computing environments that gives scalable applications across various geographical data focuses. The simulation environment utilized is Cloud Sim apparatus and the outcome demonstrates the great performance in the cloud computing environment. Also, Dillon et al (2010) proposed probably the latest issues and challenges in the cloud computing. That has given a colossal opening to the up and coming researchers. Manvi and Shyam (2014) prescribed an important issue for managing of the resource and studied on some cloud methods which had great impact on cloud resource mapping, adaption and provisioning. Addis et al (2013) explained about large computing platforms are critical for resource management (RM). Subsequently an optimal RM framework has been necessitated that acts at multiple instances. At the point when compared with all other resource managing strategies, the outcomes analyzed in this model have a decent resource management for large computing platforms, for example, enormous data analytics. ISSN NO: 2279-543X Page No: 53 International Journal of Scientific Research and Review Volume 5 Issue 1 2016
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  Chen et al(2015) proposed a model in number hypothesis towards vitality productive planning which has much dynamicity that endeavors proactive booking techniques. The three strategies have been proposed and four typical baseline algorithms for planning of tasks. Rao KS, Thilagam (2015) Weighted round robin booking algorithm The weight assignment is based on an intricate rationale. The rationale applied to process the heaviness of VM isn't in all respects clearly portrayed in the manuscript. Displayed residual resource fragmentation (RFAware) in cloud data focuses and with server consolidation examined the feasibility of residual resource defragmentation. Based on this authors proposed defragmentation with low vitality cost, SLA violations and decreased VM migration by controlled defragmentation with consolidation. STRATEGIES FOR ENERGY EFFICIENT LOAD BALANCING OF TASKS IN CLOUD COMPUTING ENVIRONMENT:- „Research' alludes to the systematic technique consisting of enunciating the issue, formulating a speculation, gathering the facts or data, analyzing the facts and reaching certain conclusions either as solutions(s) towards the concerned issue or in certain generalizations for some theoretical formulation. A researcher ought to have the option to distinguish and choose appropriate and relevant research strategies/systems to achieve required result from an examination. In research procedure, the researcher chooses what instruments ought to be utilized for the analysis and why, taking into account all the hidden assumptions and all the criteria under consideration. This suggests that the structure of research philosophy may vary from issue to issue. To meet out learning and research goals we need relevant strategies. Energy efficient power model in firefly search algorithm:- The vitality proficiency in each cloud server is increased by applying this firefly search algorithm implementation. A power saver model is utilized for relating this strategy. A fact is, if appropriate power saving is there in all the cloud data focuses, and then the overall vitality consumption will be less. There are various units that consume vitality in the cloud data focus ISSN NO: 2279-543X Page No: 54 International Journal of Scientific Research and Review Volume 5 Issue 1 2016
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  Energy Consumption unitsin cloud data centers RESULTS:- Load balancing of tasks in the cloud computing environment is a challenging task for all the researchers as well as the industry individuals. Beforehand many researchers started to chip away at the load balancing of tasks in conveyed environment. A portion of the investigations in load balancing of tasks in the dispersed computing paves way for the cloud load balancing scenario. In that, the major issues faced by many cloud service suppliers were about vast contrast in response time of the cloud servers while delivering solicitations to the cloud clients. Also, the vitality proficiency constraint has to be analyzed appropriately to save the resource usage. In the initial segment of this research work, firefly search behavior inspired load balancing of tasks concept is proposed. In that scenario almost in each individual cloud server, the vitality productivity has been improved by less task migration time between the quantities of tasks in each task allocated virtual machines.. Content based load balancing of tasks has been performed to channel the record contents inserted as tasks by using the Distributed Hash Table (DHT) index for each task. ISSN NO: 2279-543X Page No: 55 International Journal of Scientific Research and Review Volume 5 Issue 1 2016
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  CONCLUSIONS:- The content basedload balancing of tasks technique benefits the digital society in multiple ways. Right off the bat, this technique lessens the video latency time in the virtual machine instances across various cloud servers that aides in better video streaming in popular Socio Internet sites like Youtube.com. And the video upload bandwidth utilization (mbps) is better when compared to many of existing approachs. In the region rerouting load balancing of tasks over the cloud computing scenario, the server should concentrate on the maximum number of tasks it handles. On the off chance that the server is having overflow with gigantic number of tasks, then all the other incoming tasks ought to be rerouted to the nearest geographic region. The outcome graphs and tables demonstrates that, this approach region rerouting load balancing algorithm (RRRL) for effective routing of tasks is better when compared with other load balancing algorithms. To conclude, always the tasks are steered to the cloud server according to the nearest cloud server mapping algorithms. REFRENCES :- 1. Krauter, K., R. Buyya and M. Maheswaran (2002). A taxonomy and survey of grid resource management systems for distributed computing, Software-Practice and Experience, Vol. 32, No. 2, pp. 135-64. 2. Mell, P. and T. Grance (2011). The NIST definition of cloud computing. 3. Foster, I., Y. Zhao, I. Raicu and S. Lu (2008). Cloud computing and grid computing 360-degree compared, IEEE in Grid Computing Environments Workshop, GCE'08, pp. 1-10. 4. Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, James Broberg and Ivona Brandic.(2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, Future Generation Computer Systems, Vol. 25, No. 6, pp. 599-616. 5. Anil Lamba, "Uses Of Cluster Computing Techniques To Perform Big Data Analytics For Smart Grid Automation System", International Journal for Technological Research in Engineering, Volume 1 Issue 7, pp.5804-5808, 2014. 6. Anil Lamba, “Uses Of Different Cyber Security Service To Prevent Attack On Smart Home Infrastructure", International Journal for Technological Research in Engineering, Volume 1, Issue 11, pp.5809-5813, 2014. 7. Anil Lamba, "A Role Of Data Mining Analysis To Identify Suspicious Activity Alert System”, International Journal for Technological Research in Engineering, Volume 2 Issue 3, pp.5814- 5825, 2014. 8. Anil Lamba, "To Classify Cyber-Security Threats In Automotive Doming Using Different Assessment Methodologies”, International Journal for Technological Research in Engineering, Volume 3, Issue 3, pp.5831-5836, 2015. 9. Buyya, R., R. Ranjan and R. N. Calheiros (2010). Intercloud: Utility-oriented federation of cloud computing environments for scaling of application services, Springer Berlin Heidelberg in Algorithms and architectures for parallel processing, pp. 13-31. Supreeth S, Biradar S. ISSN NO: 2279-543X Page No: 56 International Journal of Scientific Research and Review Volume 5 Issue 1 2016
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  Scheduling virtual machinesfor load balancing in cloud computing platform. International Journal of Science and Research (IJSR), India Online ISSN. 2013 Jun:2319-7064. 10. Devi DC, Uthariaraj VR. Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks. The Scientific World Journal. 2016 Feb 3;2016. 11. Voorsluys W, Broberg J, Venugopal S, Buyya R. Cost of virtual machine live migration in clouds: A performance evaluation. InIEEE International Conference on Cloud Computing 2009 Dec 1 (pp. 254-265). Springer Berlin Heidelberg. 12. Kliazovich D, Pecero J, Tchernykh A, Bouvry P, Khan SU, Zomaya AY. CA-DAG: communication-aware directed acyclic graphs for modeling cloud computing applications. InProceedings of the 2013 IEEE Sixth International Conference on Cloud Computing 2013 (pp. 277-284). IEEE Computer Society. ISSN NO: 2279-543X Page No: 57 International Journal of Scientific Research and Review Volume 5 Issue 1 2016