LOAD BALANCING USING ANT COLONY IN CLOUD COMPUTING

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1 LOAD BALANCING USING ANT COLONY IN CLOUD COMPUTING Ranjan Kumar 1 and G Sahoo 2 1 Deartment of Comuter Science & Engineering, C.I.T Tatisilwai, Ranchi, India 2 Deartment of Information Technology, B.I.T Mesra, Ranchi, India ABSTRACT Ants are very small insects.they are caable to find food even they are comlete blind. The ants lives in their nest and their job is to search food while they get hungry. We are not interested in their living style, such as how they live, how they slee. But we are interested in how they search for food, and how they find the shortest ath. The technique for finding the shortest ath are now alying in cloud comuting. The Ant Colony aroach towards Cloud Comuting gives better erformance. KEYWORDS Ant Colony, Cloud Comuting, Pheromone, Web Servers, Job Schedulers. 1. INTRODUCTION Cloud Comuting is very hot toic in IT field. Many researches are going on Cloud Comuting. This is basically on-demand service. It means whenever we need for some alications or some software, we demand for it and we immediately get it. We have to ay only that we use. This is the main motto of cloud comuting. Our desired alication will resent in our comuter in few moment. Cloud Comuting has basically two arts, the First art is of Client Side and the second art is of Server Side. The Client Side requests to the Servers and the Server resonds to the Clients. The request from the client firstly goes to the Master Processor of the Server Side. The Master Processor are attached to many Slave Processors, the master rocessor sends that request to any one of the Slave Processor which have free sace. All Processors are busy in their assigned job and non of the Processor get Idle. The rocess of assigning job from Master rocessor to the Slave rocessor and after comletion the job, then returning from the Slave rocessor to the Master rocessor is just lie Ant taes their food and return to their nest. The real ants left out heromone while travelling. A heromone is a chemical used for communication. Now we are moving from real ants to artificial ants. The artificial ants have some secial characteristics which is not found in real ants, such as they are not comletely blind, they have some memory called tabu. Now the artificial ants are used in cloud comuting. The cloud comuting is comosed of three service models, five essential characteristics, and four deloyment models. The three service models are as follows. Software as a Service (SaaA). Platform as a Service (PaaS). Infrastructure as a Service (IaaS). The five essential charactersistics are as follows. DOI: /itcs

2 On-demand self service Ubiquitous networ access Resource ooling Raid elasticity Location indeendence The four deloyment models are as follows. Private Cloud Public Cloud Community Cloud Hybrid Cloud Organization of this aer is as follows: Related wor is discussed in section II. Proosed Ant Colony is discussed in section III. Exerimental setu is discussed in section IV. Result is discussed in section V. And section VI gives conclusion. 2. RELATED WORK Marco Dorigo and Luca Maria Gambardella [1] described about real and artificial ant. An artificial ant colony, that was caable of solving Travelling Salesman Problem. Real ants are caable of finding the shortest ath from food source to the nest without using visual cues. Also, they are caable of adating to changes in the environment, for examle finding a new shortest ath once the old one is no longer feasible due to a new obstacle. Zehua Zhang and Xuejie Zhang [2] described about Load balancing mechanism based on Ant Colony. They described about the function of Load balancing and how to distribute the worload in a cloud and to realize a high ratio of user satisfication. They described the two characteristic of Comlex Networ and these two characteristics are considered for the move of the ants in the wor, since the ants move more quicly towards that region where more resources found. They also described about Underload and Overload of load balancing methods. Sarayut Nonsiri and Siriorn Suratid [3] discussed about the ACO that allows fast near otimal solutions to be found. It is useful in industrial environments where comutational resources and time are limited. Patomorn Premrayoon and Paramote Wardein [4] discussed about the toological communication networ design. They discussed about the bacbone networ and the Local Area Networ (LAN), they give the formula of Total number of ossible lins in a single design. They discussed about the Reliability calculation using bactracing algorithm for correctly calculate the system reliability. They also discussed about the basic rincile of ant colony and State Transition Rule in Ant Colony Otimization technique and Global udating rule. Zenon Chaczo, Venatesh Mahadevan, Shahrzad Aslanzadeh and Christoher Mcdermid [7] discussed about the availability and load balancing in cloud comuting. They discussed about the static and dynamic algorithms and the load balancing techniques to obtain measurable imrovements in resource utilization and availability of cloud comuting environment. 3. PROPOSED ANT COLONY Marco Dorigo, first introduced the Ant System (AS) in his Ph.D thesis in Now it is one of the best otimization technique, which finds the shortest ath. The deosition of heromone and the ant move is aroximately at the same seed and at the same rate. And that heromone attracts another ants to move on same ath. So, more ants move on same ath have higher concentration of heromone and the evaoration rate is very low on shorter ath, that s why ants chooses the shorter ath. 2

3 The robability with which ant currently at stage i choosing to go to stage j. ( t ) [ ( t )] [ ( t )] [A ] l J i [ ( t )] [ ( t )] Where, = Pheromone trail = Heuristic value = Parameter which determines the relative influence of the heromone trail. = Parameter which determines the relative influence of the heromone trail. A = Amount of heromone The roosed Algorithm is defined as follow. Ste 1 : Randomly select a Job Schedular. Ste 2 : Job Schedular Schedules job to different web services. While Job is not schedule to web services Reeat stes 3 & 4. Ste 3: Job checs its surrounding area for availability of web services with Probability, ( t ) [ ( t )] [ ( t )] [A ] l J i [ ( t )] [ ( t )] Ste 4 : if Web server is available Then Acquire web server Else Go to ste 3 Ste 5 : Return to the Job Schedular. Ste 6 : After comletition ill the job. Ste 7 : End 3

4 The Web Services have some amount of load at any time, since non of the rocessor get idle. The decision oint maes ants to realize the Load of different Web Services. 4. EXPERIMENTAL SETUP To evaluate the erformance of Ant Colony, the results were simulated in Window 7 basic (64- bit), i3 rocessor, 370 M rocessor, 2.40 GHz of seed with memory of 3 GB and language used C++. There are 10 job sechedulers and 44 different web services. The job secheduler sechedules the different jobs to the different web services. The number of ants in this simulation varies from 1 to These ants deosit some amount of heromone in there move. 5. RESULT We have exerimented by taing different amount of number of ants. The amount of heromone varies between 0 to 1. The table I shows the number of ants and the amount of heromone deosited. Table I No. of Ants Amount of Pheromone Uto Uto Uto Uto Uto Uto Uto Uto Uto Uto From the table I, we see that as the number of ants increases, the amount of heromone also increases, Since most of the ants uses the same ath. The figure I shows the grah of Table I No. of Ants------>1500 Ant Colony Ant Colony Pheromone Trail > Figure I. Ant Colony in resect of Ants & Pheromone Trail 4

5 3. CONCLUSIONS In this aer, we have roosed a method for load balancing. In which we emhasis on deosition of heromone. Here we see that when a node with minimum load is attracted by most of the ants gives result to the maximum deosition of heromone. REFERENCES [1] Marco Dorigo, Luca Maria Gambardella Ant Colonies for the travelling Salesman Problem, TR/IRIDIA, Vol.3, University Libre de Bruxelles, Belgium, [2] Zehua Zhang and Xuejie Zhang A Load Balancing Mechanism Based on Ant Colony and Comlex Networ Theory in Oen Cloud Comuting Federation, International Conference on Industrial Mechatronics and Automation, ,2010. [3] Sarayut Nonsiri and Siriorn Suratid Modifying Ant Colony Otimization, IEEE Conference on Soft Comuting in Industrial Alication, Muroran, Jaan. P [4] Patomorn Premrayoon and Paramote Wardein Toological Communication Networ Design Using Ant Colony Otimization, Deartment of telecommunication Engineering, King Mongut s Institute of Technology Landrabang Bano, Thailand. P [5] Kun Li, Gaochao Xu, Guangyu Zhao, Yushuang Dong and Dan Wang Cloud Tas scheduling based on Load Balancing Ant Colony Otimization, Jilin University, ChangChun, China, Sixth Annual ChinaGrid Conference [6] Shu-Ching Wang, Kuo-Qin Yan, Wen-Pin Liao and Shun-Sheng Wang Towards a Load Balancing in a Three-Level Cloud Comuting Networ, Chaoyang University of Technology, Taiwan, R.O.C [7] Zenon Chaczo, Venatesh Mahadevan, Shahrzad Aslanzadeh and Christoher Mcdermid Availability and Load Balancing in Cloud Comuting, International Conference on Comuter and Software Modelling, IPCSIT, vol. 14, Singaore [8] Ratan Mishra and Anant Jaiswal Ant Colony Otimization: A Solution of Load balancing in Cloud International Journal of Web and Semantic Technology. Vol. 3, No Aril [9] Kumar Nishant, Prati Sharma, Vishal Krishna, Chhavi Guta, Kuwar Prata Singh, Nitin and Ravi Rastogi Load Balancing of Nodes in Cloud Using Ant Colony Otimization, Deartment of CSE and ICT, Jayee University of Information Technology, 14th International Conference on Modelling and Simulation [10] Swarm Intelligence from Natural to Artificial System by Marco Dorigo and Eric Bonabeau, [11] Cloud Comuting and SOA Convergence in Your Enterrise by David s. Linthicum, [12] Cloud Comuting Web Based Alication by Michael Miller, Authors Ranjan Kumar received M.Tech degree in Comuter Science from B.I.T Mesra, Ranchi. He has one year teaching exerience. His research interests include cloud comuting, Algorithm and comiler. 5