Integration of 6LOWPAN and Cloud Data Centers: The Role of Link Capacity between Sensor Networks and Cloud Data Centers
P Suganya1, Pradeep Reddy Ch2

1P.Suganya*, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu.
2Dr. Pradeep Reddy Ch School of Information Technology and Engineering. Vellore Institute of Technology ,Amaravathi, Andhra Pradesh

Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 2159-2164 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4715119119/2019©BEIESP | DOI: 10.35940/ijitee.A4715.119119
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Abstract: Internet of Things (IoT) and Internet of Mobile Things (IoMT) acquired widespread popularity by its ease of deployment and support for innovative applications. The sensed and aggregated data from IoT and IoMT are transferred to Cloud through Internet for analysis, interpretation and decision making. In order to generate timely response and sending back the decisions to the end users or Administrators, it is important to select appropriate cloud data centers which would process and produce responses in a shorter time. Beside several factors that determine the performance of the integrated 6LOWPAN and Cloud Data Centers, we analyze the available bandwidth between various user bases (IoT and IoMT networks) and the cloud data centers. Amidst of various services offered in cloud, problems such as congestion, delay and poor response time arises when the number of user request increases. Load balancing/sharing algorithms are the popularly used techniques to improve the performance of the cloud system. Load refers to the number of user requests (Data) from different types of networks such as IoT and IoMT which are IPv6 compliant. In this paper we investigate the impact of homogeneous and heterogeneous bandwidth between different regions in load balancing algorithms for mapping user requests (Data) to various virtual machines in Cloud. We investigate the influence of bandwidth across different regions in determining the response time for the corresponding data collected from data harvesting networks. We simulated the cloud environment with various bandwidth values between user base and data centers and presented the average response time for individual user bases. We used Cloud- Analyst an open source tool to simulate the proposed work. The obtained results can be used as a reference to map the mass data generated by various networks to appropriate data centers to produce the response in an optimal time.
Keywords: IoT, IoMT, Data Centers, Virtual Machines, Load Balancing, Cloud Computing
Scope of the Article: IoT