Systematic Review:Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Cloud Computing has the ability to provide on-demand access to a shared resource pool.It has completely changed the way businesses are managed,implement applications,and provide services.The rise in popularity has led to a significant increase in the user demand for services.However,in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization.This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms.Specifically,metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic.In a cloud-based context,this paper describes load balancing measurements,including the benefits and drawbacks associated with the selected load balancing techniques.It also summarizes the algorithms based on implementation,time complexity,adaptability,associated issue(s),and targeted QoS parameters.Additionally,the analysis evaluates the tools and instruments utilized in each investigated study.Moreover,comparative analysis among static,traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed.Finally,the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed.

A Local Sparse Screening Identification Algorithm with Applications

Extracting nonlinear governing equations from noisy data is a central challenge in the analysis of complicated nonlinear behaviors.Despite researchers follow the sparse identification nonlinear dynamics algorithm(SINDy)rule to restore nonlinear equations,there also exist obstacles.One is the excessive dependence on empirical parameters,which increases the difficulty of data pre-processing.Another one is the coexistence of multiple coefficient vectors,which causes the optimal solution to be drowned in multiple solutions.The third one is the composition of basic function,which is exclusively applicable to specific equations.In this article,a local sparse screening identification algorithm(LSSI)is proposed to identify nonlinear systems.First,we present the k-neighbor parameter to replace all empirical parameters in data filtering.Second,we combine the mean error screening method with the SINDy algorithm to select the optimal one from multiple solutions.Third,the time variable t is introduced to expand the scope of the SINDy algorithm.Finally,the LSSI algorithm is applied to recover a classic ODE and a bi-stable energy harvester system.The results show that the new algorithm improves the ability of noise immunity and optimal parameters identification provides a desired foundation for nonlinear analyses.

A study on the dynamic tie points ASI algorithm in the Arctic Ocean

Sea ice concentration is an important parameter for polar sea ice monitoring. Based on 89 GHz AMSR-E （Advanced Microwave Scanning Radiometer for Earth Observing System） data, a gridded high-resolution passive microwave sea ice concentration product can be obtained using the ASI （the Arctic Radiation And Turbulence Interaction Study （ARTIST） Sea Ice） retrieval algorithm. Instead of using fixed-point values, we developed ASi algorithm based on daily changed tie points, called as the dynamic tie point ASI algorithm in this study. Here the tie points are expressed as the brightness temperature polarization difference of open water and 100% sea ice. In 2010, the yearly-averaged tie points of open water and sea ice in Arctic are estimated to be 50.8 K and 7.8 K, respectively. It is confirmed that the sea ice concentrations retrieved by the dynamic tie point ASI algorithm can increase （decrease） the sea ice concentrations in low-value （high-value） areas. This improved the sea ice concentrations by present retrieval algorithm from microwave data to some extent. Comparing with the products using fixed tie points, the sea ice concentrations retrieved from AMSR-E data by using the dynamic tie point ASI algorithm are closer to those obtained from MODIS （Moderate-resolution Imaging Spectroradiometer） data. In 40 selected cloud-free sample regions, 95% of our results have smaller mean differences and 75% of our results have lower root mean square （RMS） differences compare with those by the fixed tie points.

An Improved Artificial Immune Algorithm with a Dynamic Threshold

An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody＇s fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence.

Dynamic and Integrated Load-Balancing Scheduling Algorithm for Cloud Data Centers

One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consideration. We introduce a Dynamic and Integrated Resource Scheduling algorithm (DAIRS) for Cloud data centers. Unlike traditional load-balance scheduling algorithms which often consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.

Finding Global Minima with a New Dynamical Evolutionary Algorithm

A new dynamical evolutionary algorithm (DEA) based on the theory of statistical mechanics is presented. This algorithm is very different from the traditional evolutionary algorithm and the two novel features are the unique of selecting strategy and the determination of individuals that are selected to crossover and mutate. We use DEA to solve a lot of global optimization problems that are nonlinear, multimodal and multidimensional and obtain satisfactory results.

Dynamic bandwidth allocation algorithm for full-band utilization

To improve and optimize the bandwidth utilization for multi-service packet transporting system, a kind of Dynamic Full Bandwidth Utilized （DFBU） allocation algorithm allowing a single link to use far beyond its fair share bandwidth is presented. Three important parameters as the bound on max and minimum bandwidth, the maximum packet delay and the minimum bandwidth utilization are discussed and analyzed. Results of experiments show that the DFBU-algorithm is capable of making a single link in the system use all the spare bandwidth （up to full-bandwidth） while the performance of fairness and QoS requirement is still guaranteed.

An improved genetic algorithm with dynamic topology

The genetic algorithm （GA） is a nature-inspired evolutionary algorithm to find optima in search space via the interac- tion of individuals. Recently, researchers demonstrated that the interaction topology plays an important role in information exchange among individuals of evolutionary algorithm. In this paper, we investigate the effect of different network topolo- gies adopted to represent the interaction structures. It is found that GA with a high-density topology ends up more likely with an unsatisfactory solution, contrarily, a low-density topology can impede convergence. Consequently, we propose an improved GA with dynamic topology, named DT-GA, in which the topology structure varies dynamically along with the fitness evolution. Several experiments executed with 15 well-known test functions have illustrated that DT-GA outperforms other test GAs for making a balance of convergence speed and optimum quality. Our work may have implications in the combination of complex networks and computational intelligence.

Dynamic A^*path finding algorithm and 3D lidar based obstacle avoidance strategy for autonomous vehicles

This paper presents a novel dynamic A^*path finding algorithm and 3D lidar based local obstacle avoidance strategy for an autonomous vehicle.3D point cloud data is collected and analyzed in real time.Local obstacles are detected online and a 2D local obstacle grid map is constructed at 10 Hz/s.The A^*path finding algorithm is employed to generate a local path in this local obstacle grid map by considering both the target position and obstacles.The vehicle avoids obstacles under the guidance of the generated local path.Experiment results have shown the effectiveness of the obstacle avoidance navigation algorithm proposed.

A Dynamic Leader Election Algorithm for Decentralized Networks

Leader election algorithms play an important role in orchestrating different processes on distributed systems, including next-generation transportation systems. This leader election phase is usually triggered after the leader has failed and has a high overhead in performance and state recovery. Further, these algorithms are not generally applicable to cloud-based native microservices-based applications where the resources available to the group and resources participating in a group continuously change and the current leader may exit the system with prior knowledge of the exit. Our proposed algorithm, the dynamic leader selection algorithm, provides several benefits through selection (not, election) of a set of future leaders which are then alerted prior to the failure of the current leadership and handed over the leadership. A specific illustration of this algorithm is provided with reference to a peer-to-peer distribution of autonomous cars in a 5G architecture for transportation networks. The proposed algorithm increases the efficiencies of applications that use the leader election algorithm and finds broad applicability in microservices-based applications.

Application of Dynamic Programming Algorithm Based on Model Predictive Control in Hybrid Electric Vehicle Control Strategy

A good hybrid vehicle control strategy cannot only meet the power requirements of the vehicle,but also effectively save fuel and reduce emissions.In this paper,the construction of model predictive control in hybrid electric vehicle is proposed.The solving process and the use of reference trajectory are discussed for the application of MPC based on dynamic programming algorithm.The simulation of hybrid electric vehicle is carried out under a specific working condition.The simulation results show that the control strategy can effectively reduce fuel consumption when the torque of engine and motor is reasonably distributed,and the effectiveness of the control strategy is verified.

THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL

In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the "on-off" switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization "on-off" switches in the forcing term, the impacts of "on-off" switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail.

Dynamic self-adaptive ANP algorithm and its application to electric field simulation of aluminum reduction cell

Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.

Novel Voltage Scaling Algorithm Through Ant Colony Optimization for Embedded Distributed Systems

Dynamic voltage scaling (DVS), supported by many DVS-enabled processors, is an efficient technique for energy-efficient embedded systems. Many researchers work on DVS and have presented various DVS algorithms, some with quite good results. However, the previous algorithms either have a large time complexity or obtain results sensitive to the count of the voltage modes. Fine-grained voltage modes lead to optimal results, but coarse-grained voltage modes cause less optimal one. A new algorithm is presented, which is based on ant colony optimization, called ant colony optimization voltage and task scheduling (ACO-VTS) with a low time complexity implemented by parallelizing and its linear time approximation algorithm. Both of them generate quite good results, saving up to 30% more energy than that of the previous ones under coarse-grained modes, and their results don’t depend on the number of modes available.

Dynamic airspace sectorization via improved genetic algorithm

This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.

Genetic algorithm-finite element method inversion of the factors determining the recent tectonic stress field of part of East Asia area

Genetic algorithm finite element method (GA FEM) is applied to the study of tectonic stress field of part of East Asia area. From the observed stress distribution, 2 D elastic plane stress inversion is made to deduce the boundary forces and investigate controlling factors. It is suggested that the continent continent collision is the dominant factor controlling the Chinese tectonic stress field. The ocean continent convergence along the subduction zone is an important factor. There exists tensile boundary force along the marginal sea.

DYNAMIC BANDWIDTH ALLOCATION ALGORITHM UTILIZING FULL BAND

A kind of Dynamic Full Bandwidth Utilized (DFBU) allocation algorithm is introduced. This algorithm allows a single link to use bandwidth far beyond its fair share bandwidth in a multi-service packet transporting system. Three important parameters as the bound on maximum and minimum bandwidth, the maximum packet delay and the minimum band width utilization are discussed and analyzed. Results of experiments show that the DFBU-algorithm is capable of making a single link in the system to use all the spare bandwidth (up to full-bandwidth) while the performance of fairness and QoS requirement is still guaranteed.

Parameter optimization of electric bus transmission system based on dynamical evolutionary algorithm

The transmission ratio is the key parameters influence power performance and economic performance of electric vehicle （EV）. As a class of heuristic algorithms, Dynamical Evolutionary Algorithm （DEA） is suitable to solve multi-objective optimization problems. This paper presents a new method to optimize the transmission ratio using DEA. The fuzzy constraints and objective function of transmission ratio are established for parameter optimization problem of electric bus transmission. DEA is used to solve the optimiza- tion problem. The transmission system is also designed based on the optimization result. Optimization and test results show that the dynamical evolutionary algorithm is an effective method to solve transmission parameter optimization problems.

Study of the Interception Scheme Based on A* Path Finding Algorithm in Computer Game

In the game, we generally use A* algorithm for pathfinding according to the target location and map information to realize the tracking and interception task. This approach is simple to implement, but is often less than ideal in its presentation, because it does not allow game units to predictably choose paths based on dynamic targets or obstacles, and may lead to blocked channels or difficulty in choosing the actual optimal path. This paper focuses on analyzing the need of a unit when intercepting another unit in the game, and make an interception scheme which can adapt itself to dynamic obstacles and moving target thus presents artificial intelligence to some extent.

Research on Full Vector Dynamic Balancing Algorithm for Rotors

Influence coefficient method and the modal balancing method are often used in the dynamic balancing in the past days. These methods sometimes exist a lot of big measurement errors. So, in order to make these errors much smaller, and to use the vibration information of the rotor more sufficiently, at last, we put forward the full vector dynamic balancing algorithm. Though the theoretical analysis, and the experiment tests, we can compare with the new method and the old method , study the relationship between the dynamic balancing and the rotation equipment, and the direction of the development. The full vector dynamic balancing algorithm theory can be inferred from the Jeffcott rotor. To compare with the methods which are mentioned before, we can find that the full vector dynamic balancing algorithm is much better than the influence coefficient method and the modal balancing method. We can use the MATLAB program to prove that the full vector dynamic balancing algorithm is much better. So the conclusion is completely right.