Energy management is considered the major concern in cloud computing, which supports the rapid growth of data centers and computing centers; therefore, energy and load balancing have become crucial issues in cloud data centers. To address this issue, the present paper proposed a two-phase energyaware load balancing (EALB) scheduling algorithm using the virtual machine migration through the Particle Swarm Optimization (PSO) algorithm to be applicable to dynamic voltage frequency scaling-enabled cloud data centers, which is called EALBPSO. In the first phase, an objective function was employed to deactivate a large number of physical machines in order to reduce energy consumption. The main idea of the algorithm was to maximize load balancing in the second phase, in which the remaining virtual and physical machines were used as the PSO inputs, and an objective function was also defined to distribute the load appropriately among the physical machines. In addition, a dataset was developed to test different parameters and scenarios with the aim of assessing the effectiveness of the proposed EALBPSO algorithm in comparison with other algorithms already proposed in the literature for similar purposes. The experimental results demonstrated that the proposed algorithm was capable of saving up to 0.896%, 9.716%, and 10.8% energy compared with the MDPSO algorithm, Kumar et al.'s algorithm, and Dahsti and Rahmani algorithm, respectively, and also it showed 5.91%, 16%, and 16.267% improvements for the number of virtual machines migrations, and 3.867%, 8.623%, and 6.953% improvements for the deviation of processors, all compared with their competitors stated above, respectively.
The navigation of a mobile robot is a very important issue, especially for an autonomous mobile robot. A robot autonomously can track the area by interpreting the arena, building an adequate map, and localizing itself to this map. This paper proposes a Hybrid filter for Concurrent Localization and Mapping (CLAM) in the navigation to rectify the faults, basically Unscented Fast Simultaneous Localization and Mapping (SLAM) (UFS). We also interrogate the effectiveness of the IF system to investigate nonlinear attributes. A probabilistic method has planned the solution to the CLAM issue, which is an essential requirement for the navigation of robots. The Hybrid filter CLAM contains an Intuitionistic Fuzzy Logic (IFL) and Unscented Kalman Filter (UKF). The IFL is first ordered by using a correctness function explained on score functions for the non-membership function (NMF) and membership function (MF) of the IFL. Then this ordering is utilized to develop a method for a sufficient decision on the CLAM issue. The proposed method has a few privileges in management robot navigation with nonlinear movements owing to the inference feature of the IFL, which also needs a fewer quantity of comparisons than the UFS and shows much better efficiency from the robustness, perspective assessment exactitude, and reliability than the UFS, also, for learning the measurement and control noise covariance matrices for increasing correctness and consistency are utilized IFL. The Hybrid filter CLAM proficiency compared with the UFS has a smaller quantity of computations and good efficiency for bigger areas as demonstrate in the results of simulation and experimental.
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