A New And Efficient Hybrid Technique For The Automatic Scaling Of Internet Things

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A new and efficient Hybrid Technique for the Automatic Scaling of Internet Things in Cloud Computing is proposed using Ant based techniques. The Proposed methodology applied here is used for the load balancing over cloud computing and hence scales over cloud for internet on Things. The methodology performs better in terms of Scalability and Decision Time and number of placements. The Various Experimental results Performed on Cloud Environment proofs to be more efficient in terms of Decision Time and Response Time in Comparison. . The Proposed Methodology implemented here is based on Ant based Clustering techniques, where Scaling of Internets is done by grouping the ants moving from one source Node to Another.
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We assume that the public cloud infrastructure provides proper security and data backup solutions with a Service Level Agreement (SLA) and mechanisms to fairly share its virtual resources among all its running VMs. The last few years have witnessed the emergence of cloud computing as a rapid, limitlessly scalable, and cost-efficient alternative in contrast to the in-house data centers. The IaaS model delegates more control to the customers over the provisioned resources. Hosting Internet applications in the IaaS environment is an efficient way to start a new and a sustainable business that expands the IT infrastructure gradually with the business growth.
A simple architecture of cloud computing consist the data centers servers for web application as well as a switch whose function is balancing the load and distribute the load to set of application server also hav-ing set of backend storage server. Fig. 1 shows the typical architecture of data center server for the In-ternet applications. It consists of a load balancing switch, a set of application servers, and a set of backend storage servers. The front end switch is typically a Layer 7 switch [5] which parses application level informa-tion in Web requests and forwards them to the servers with the corresponding applications running.
As each server machine can host

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