A predictive acknowledgement model for enhancing traffic redundancy elimination in cloud

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Due to the huge increase in the utilization of cloud storage in recent days, it leads to a massive growth in data traffic from application based servers to applications like smart phones, which not only influences batteries and computational capacities but as well swamp down multi-hopping strategies during data transmission. To resolve this crisis, traffic redundancy elimination (TRE) is an effectual solution, where the chunks to be transmitted will be directly fetched out from the receivers’ cache. Moreover, prevailing solutions cannot be directly applied or it is not appropriate for smart phones owing to its energy overhead ad high computation that is imposed on the applications. In order to overcome this problem, in this investigation, a novel a Predictive Acknowledgement for Eliminating Traffic (PACKET) is proposed which comprises of three significant elements. Initially, every application possess a clone in cloud that are responsible for calculating intensive tasks like detecting redundancy and parsing traffic. Secondly, consider that every cloud user has some specific applications like Facebook to be used in regular day to day life, every clones of cloud has to selectively determine the applications that are most frequently utilized and also reduce the high redundancy ratio. Thirdly, some cloud users always possess certain common applications; the proposed PACKET clusters those clones to co-operatively perform redundancy detection so as to diminish cache resource consumption in cloud. The simulation is carried out in MATLAB environment; the traces of applications are collected from online available data and are utilized for simulation purpose. Experimental outcomes demonstrates that PACKET can attain much higher hit ratio, reduced E2E delay, increased E2E throughput, energy efficiency and effectual bandwidth utilization in contrast to existing approaches. The proposed PACKET shows better and efficient trade-off than prevailing techniques.




Rathi, R., & Ramaparvathy, L. (2019). A predictive acknowledgement model for enhancing traffic redundancy elimination in cloud. International Journal of Innovative Technology and Exploring Engineering, 8(9), 2571–2580. https://doi.org/10.35940/ijitee.i8478.078919

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