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This paper aims to develop the Dragonfly-based exponential gravitational search algorithm to VMM strategy for effective load balancing in cloud computing. Due to widespread growth of cloud users, load balancing is the essential criterion to deal with the overload and underload problems of the physical servers. DEGSA-VMM is introduced, which calculates the optimized position to perform the virtual machine migration (VMM).

This paper presents an algorithm Dragonfly-based exponential gravitational search algorithm (DEGSA) that is based on the VMM strategy to migrate the virtual machines of the overloaded physical machine to the other physical machine keeping in mind the energy, migration cost, load and quality of service (QoS) constraints. For effective migration, a fitness function is provided, which selects the best fit that possess minimum energy, cost, load and maximum QoS contributing toward the maximum energy utilization.

For the performance analysis, the experimentation is performed with three setups, with Setup 1 composed of three physical machines with 12 virtual machines, Setup 2 composed of five physical machines and 19 virtual machines and Setup 3 composed of ten physical machines and 28 virtual machines. The performance parameters, namely, QoS, migration cost, load and energy, of the proposed work are compared over the other existing works. The proposed algorithm obtained maximum resource utilization with a good QoS at a rate of 0.19, and minimal migration cost at a rate of 0.015, and minimal energy at a rate of 0.26 with a minimal load at a rate of 0.1551, whereas with the existing methods like ant colony optimization (ACO), gravitational search algorithm (GSA) and exponential gravitational search algorithm, the values of QoS, load, migration cost and energy are 0.16, 0.1863, 0.023 and 0.29; 0.16, 0.1863, 0.023 and 0.28 and 0.18, 0.1657, 0.016 and 0.27, respectively.

This paper presents an algorithm named DEGSA based on VMM strategy to determine the optimum position to perform the VMM to achieve a better load balancing.

The design of jet engine parts makes particular claims on the balancing machines used: shortest spinning time in the balancing machine, e.g. no nulling and calibration runs—set up of the plane computer at stand still—exact plane setting even at close compensation plane distances and large bearing plane space—installation of rotors with their own operational ball or roller bearings without necessity of the parasitic mass of connection frames or squares.

THE balancing of rotating parts can be effected either on a balancing machine, or by means of vibration measurements carried out on the completed assembly.

To present results of research closely linked with real life applications. It resumes work of a period of about two years.

Applying the finite‐element method (FEM) the impact of balancing kerfs in the bars of squirrel‐cage rotors of a small scale, mass series induction machine (IM) is studied. For the analysis and design optimization of the IM both, 2D electromagnetic, multi‐slice and 3D structure‐dynamic models are considered. Introducing and applying a novel 2D‐3D force‐transformation scheme, all possible balancing variants of the IM are studied in terms of electromagnetic and mechanical behaviour.

The obtained results lead to a significant improvement of the studied IM. In fact, it is found, that the method of balancing the rotor by carving the rotor bars results in higher unbalanced pull rather than reducing it. This is due to electromagnetic unbalance caused by balancing. Hence, the IM is no longer balanced in series production. This again leads to a major economic benefit.

Using the FEM for simulation of structure dynamic problems is often limited to how the boundary layers are handled. In real life materials are not “connected” but glued or clamped. Therefore, the behaviour can only be adopted by manipulating the material parameters derived from iterative parameter adoption by measurement.

Owing to the findings the IM is no longer balanced in series production, leading to a significant reduction of costs. In general, the applied methods can be used for the analysis and optimization of any kind of manufacturing or tolerance problem of electrical machines such as various kinds of eccentricity, punching kerfs, broken bars, magnetization errors in permanent‐magnet machines, etc.

This contribution gives a close insight of how to study the impact of manufacturing and tolerance problems of electric machinery, applying the method to an IM with balancing kerfs.

To propose a methodology based on genetic algorithm (GA) to solve the parallel machine scheduling problems with precedence constraints.

Workflow balancing helps to remove bottlenecks present in a shop floor yielding faster movements of components or jobs. Multiple machines are used in parallel for processing the jobs to meet the demand. In parallel machine scheduling with precedence constraints, there are m machines to which n jobs are assigned using suitable scheduling algorithms. Workflow of a machine is the sum of processing time of all jobs assigned. All the preceding jobs are allocated first to satisfy the constraints. GA is developed to solve parallel machine scheduling problems with precedence constraints based on the objective of workflow balancing. The GA was coded on IBM/PC compatible system in the C++ language for simulation to a standard manufacturing environment.

The relative percentage of imbalance (RPI) in workloads among the parallel machines is used to evaluate the performance of the GA developed. The proposed GA produces lesser RPI values against the RANDOM heuristic algorithm for a wider range of jobs and machines.

The performance of GA can be compared with the performance of other meta‐heuristic algorithms to find out the robustness of the results obtained by this research.

The proposed GA also gives better solution for a case study of assembly scheduling.

The allocation of assembly operations to the operators is modeled into a parallel machine scheduling problem with precedence constraints using the objective of minimizing the workflow among the operators.

Virtual machines (VMs) are suggested by the providers of cloud services as the services for the users over the internet. The consolidation of VM is the tactic of the competent and smart utilization of resources from cloud data centers. Placement of a VM is one of the significant issues in cloud computing (CC). Physical machines in a cloud environment are aware of the way of the VM placement (VMP) as the mapping VMs. The basic target of placement of VM issue is to reduce the physical machines' items that are running or the hosts in cloud data centers. The VMP methods have an important role in the CC. However, there is no systematic and complete way to discuss and analyze the algorithms. The purpose of this paper is to present a systematic survey of VMP techniques. Also, the benefits and weaknesses connected with selected VMP techniques have been debated, and the significant issues of these techniques are addressed to develop the more efficient VMP technique for the future.

Because of the importance of VMP in the cloud environments, in this paper, the articles and important mechanisms in this domain have been investigated systematically. The VMP mechanisms have been categorized into two major groups, including static and dynamic mechanisms.

The results have indicated that an appropriate VMP has the capacity to decrease the resource consumption rate, energy consumption and carbon emission rate. VMP approaches in computing environment still need improvements in terms of reducing related overhead, consolidation of the cloud environment to become an extremely on-demand mechanism, balancing the load between physical machines, power consumption and refining performance.

This study aimed to be comprehensive, but there were some limitations. Some perfect work may be eliminated because of applying some filters to choose the original articles. Surveying all the papers on the topic of VMP is impossible, too. Nevertheless, the authors are trying to present a complete survey over the VMP.

The consequences of this research will be valuable for academicians, and it can provide good ideas for future research in this domain. By providing comparative information and analyzing the contemporary developments in this area, this research will directly support academics and working professionals for better knowing the growth in the VMP area.

The gathered information in this paper helps to inform the researchers with the state of the art in the VMP area. Totally, the VMP's principal intention, current challenges, open issues, strategies and mechanisms in cloud systems are summarized by explaining the answers.

The purpose of this paper is to propose the dynamically balanced mechanism for cleaning unit used in the agricultural thresher machine using the system of point masses.

The cleaning unit works on crank-rocker Grashof mechanism. To balance the mechanism, the shaking forces and shaking moments are minimized by optimizing the mass distribution of links using the dynamically equivalent system of point masses. The point mass parameters are taken as the design variables. Then, the optimization problem is solved using Jaya algorithm and genetic algorithm (GA) under suitable design constraints.

The mass, center of mass and inertias of each link are calculated using optimum design variables. These optimum parameters improve the dynamic performance of the cleaning unit.

The proposed methodology is tested through the standard four-bar mechanism taken from literature and also applied to the existing cleaning mechanism of the thresher machine. It is observed that the Jaya algorithm is computationally more efficient than the GA. The dynamic analysis of the proposed mechanism is simulated using ADAMS software.

Line balancing has been an important technique for manufacturing system design, because a completely balanced system can provide maximum resource utilization at the designed capacity. However, even if a system is completely balanced, it still has capacity waste when the entire product life cycle is considered, because real production is often significantly less than capacity. Avoiding this mismatch requires scalable systems such as reconfigurable manufacturing systems (RMSs) to meet changing product demand. Stage paralleling is suggested as an approach to scalability for RMSs. By comparing the economic feasibility of such manufacturing systems with completely balanced transfer line systems with respect to station cost, it is shown that line balancing is not necessarily desirable with this approach. The effect of station cost differences for unbalanced systems is also considered.

This paper aims to propose a dynamically balanced mechanism for cleaning unit used in agricultural thresher machine using a dynamically equivalent system of point masses.

The cleaning unit works on crank-rocker Grashof mechanism. This mechanism can be balanced by optimizing the inertial properties of each link. These properties are defined by the dynamic equivalent system of point masses. Parameters of these point masses define the shaking forces and moments. Hence, the multi-objective optimization problem with minimization of shaking forces and shaking moments is formulated by considering the point mass parameters as the design variables. The formulated optimization problem is solved using a posteriori approach-based algorithm i.e. the non-dominated sorting Jaya algorithm (NSJAYA) and a priori approach-based algorithms i.e. Jaya algorithm and genetic algorithm (GA) under suitable design constraints.

The mass, center of mass and inertias of each link are calculated using optimum design variables. These optimum parameters improve the dynamic performance of the cleaning unit. The optimal Pareto set for the balancing problem is measured and outlined in this paper. The designer can choose any solution from the set and balance any real planar mechanism.

The efficiency of the proposed approach is tested through the existing cleaning mechanism of the thresher machine. It is found that the NSJAYA is computationally more efficient than the GA and Jaya algorithm. ADAMS software is used for the simulation of the mechanism.

A Description of the Early Problems Encountered by Rolls‐Royce Ltd., which Led to the Appointment of a Specialist ‘Rotor Balance’ Engineer, and a Review of the Company's Current Balancing Techniques. THE advent of the gas turbine aero engine brought a state of passenger comfort, never before experienced, into the field of civil air transport; this was possible because the unbalanced forces due to reciprocating masses are entirely absent and the purely rotating masses of a turbine engine can, theoretically, be brought into perfect balance. The resulting smooth running engines produce lower levels of passenger fatigue due both to physical effects (i.e. a reduced feeling of ‘pins and needles’ in those parts of the anatomy touching the cabin floor or scat) and to aural effects (i.e. a lower noise level from engine buzz or cabin panels and fittings resonances).