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Figure 1 IBM’s characterization of big data. One of the biggest challenges of IoT is that the information generated by devices is transmitted, shared and received at a rate that is equal to the speed of light through optical fiber and wireless networks. Information generation and collection rates are rapidly exceeding the boundary range. The massive amount of data generated by devices, known as Big Data, has complexity and exponential arrival and growth rates, which makes it difficult to capture, manage, process, plan, visualize and analyze them by conventional techniques and within a certain time frame. Doug Laney, an expert from META (currently Gartner), an American research and advisory firm, has defined the 3Vs model to describe Big Data and the trials and prospects associated with it. The 3Vs model represents volume, velocity, and variety. Although this model was not originally intended to define Big Data, Gartner and many other enterprises, including IBM and some research departments of Microsoft, still use the 3Vs model to describe Big Data.

Figure 1 IBM’s characterization of big data. One of the biggest challenges of IoT is that the information generated by devices is transmitted, shared and received at a rate that is equal to the speed of light through optical fiber and wireless networks. Information generation and collection rates are rapidly exceeding the boundary range. The massive amount of data generated by devices, known as Big Data, has complexity and exponential arrival and growth rates, which makes it difficult to capture, manage, process, plan, visualize and analyze them by conventional techniques and within a certain time frame. Doug Laney, an expert from META (currently Gartner), an American research and advisory firm, has defined the 3Vs model to describe Big Data and the trials and prospects associated with it. The 3Vs model represents volume, velocity, and variety. Although this model was not originally intended to define Big Data, Gartner and many other enterprises, including IBM and some research departments of Microsoft, still use the 3Vs model to describe Big Data.

Related Figures (6)

Figure 2 Framework for self-optimized 5G.
Figure 2 Framework for self-optimized 5G.
Figure 3 Traffic values for different scenarios. We considered a 5MHz LTE system with 25 resource blocks, as shown in Figure. 3.
Figure 3 Traffic values for different scenarios. We considered a 5MHz LTE system with 25 resource blocks, as shown in Figure. 3.
Figure 4 Blocking probabilities for different scenarios. The blocking probabilities for each scenario are shown in Figure 4.
Figure 4 Blocking probabilities for different scenarios. The blocking probabilities for each scenario are shown in Figure 4.
Figure 5 Load balancing scenarios.
Figure 5 Load balancing scenarios.
Table 1 Comparison of different load balancing techniques. As can be seen from Table 1, various load-balancing methods have been proposed in the literature. However, some of these methods require installation of additional dedicated load balancers, or they look for manual reconfiguration of the devices to handle newer services. The above mentioned techniques are either expensive, inefficient, time-consuming or impractical. Our proposed method overcomes all the limitations mentioned in Table 1. It does not require installation of WiFi access or remote electric tilt controllers in every cell. Each
Table 1 Comparison of different load balancing techniques. As can be seen from Table 1, various load-balancing methods have been proposed in the literature. However, some of these methods require installation of additional dedicated load balancers, or they look for manual reconfiguration of the devices to handle newer services. The above mentioned techniques are either expensive, inefficient, time-consuming or impractical. Our proposed method overcomes all the limitations mentioned in Table 1. It does not require installation of WiFi access or remote electric tilt controllers in every cell. Each
Figure 6 Blocking probabilities after balancing.
Figure 6 Blocking probabilities after balancing.
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Computer ScienceGICT ApplicationsSelf Optimization
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