Location-Aware Mobile Cloud using Artificial Intelligence
Vishal1, Bikrampal Kaur2, Surender Jangra3
1Vishal*, Research scholar at I.K.G. Punjab Technical University, Kapurthala, Punjab, India.
2Prof. Bikrampal Kaur, Professor in the Department. of Computer Science & Engineering in Chandigarh Engineering College, Landran, Mohali, India.
3Dr.Surender Jangra, Assistant Professor, Department of Computer Science, GTB College, Bhawanigarh, (Sangrur), India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 25 September, 2019. | Manuscript published on October 10, 2019. | PP: 436-444 | Volume-8 Issue-12, October 2019. | Retrieval Number: L33201081219/2019©BEIESP | DOI: 10.35940/ijitee.L3320.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Mobile cloud computing is a rapidly evolving technology these days and it faces major problems of load imbalance due to the high demand for mobile applications. There are many techniques to solve the problem, but the user can improve load by using a more optimized solution. This research deals with the VM allocation and migration by means of location awareness. The research paper also presents a user authentication and server load management system to reduce the overload of the server. A captcha based authentication mechanism is also presented for user verification. The concept of Feedback is also introduced for the mobile servers. This concept makes the selection of mobile server for the job list. ANN (Artificial neural network) is used for location awareness judgment. ANN is a machine learning approach, which is used to minimize the human effort and also minimize the processing time to allocate job to an accurate server with minimum SLA violation. MBFD (Mobile best fit decreasing) algorithm is used for the VM allocation and selection policy. This research has considered SLA (Service level agreement) violation and energy consumption to compute the performance of the work with an aim of reducing energy consumption with maximized resource efficiency. The proposed work model is also compared with the work presented by Xiong in the same area.
Keywords: Mobile Cloud, VM Migration, VM Allocation, Location Awareness, Neural Network, MBFD, Energy
Scope of the Article: Artificial Intelligence