Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic

Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively.Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment.The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand.Elasticity in cloud computing is one of the fundamental properties,and elastic load balancing automatically distributes incoming load to multiple virtual machines.This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing.In this article,a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources.A Simulator in Matlab is used to test the effectiveness and correctness of the proposed model.The simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches.

Adaptive Control of Active Balancing System for a Fast Speed-varying Jeffcott Rotor with Actuator Time Delay

Due to actuator time delay existing in an adaptive control of the active balancing system for a fast speed-varying Jeffcott rotor, if an unsynchronized control force (correction imbalance) is applied to the system, it may lead to degradation in control efficiency and instability of the control system. In order to avoid these shortcomings, a simple adaptive controller was designed for a strictly positive real rotor system with actuator time delay, then a Lyapunov-Krasovskii functional was constructed after an appropriate transform of this sys-tem model, the stability conditions of this adaptive control system with actuator time delay were derived. After adding a filter function, the active balancing system for the fast speed-varying Jeffcott rotor with actuator time delay can easily be converted to a strictly positive real system, and thus it can use the above adaptive controller satisfying the stability conditions. Finally, numerical simulations show that the adaptive controller proposed works very well to perform the active balancing for the fast speed-varying Jeffcott rotor with actuator time delay.

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.

Long-term combined application of organic and inorganic fertilizers increases crop yield sustainability by improving soil fertility in maize-wheat cropping systems

Organic material combined with inorganic fertilizer has been shown to greatly improve crop yield and maintain soil fertility globally. However, it remains unclear if crop yield and soil fertility can be sustained in the long term under the combined application of organic and inorganic fertilizers. Three long-term field trials were conducted to investigate the effects of organic amendments on the grain sustainable yield index(SYI), soil fertility index(SFI)and nutrient balance in maize–wheat cropping systems of central and southern China during 1991–2019. Five treatments were included in the trials: 1) no fertilization(control);2) balanced mineral fertilization(NPK);3) NPK plus manure(NPKM);4) high dose of NPK plus manure(1.5NPKM);and 5) NPK plus crop straw(NPKS). Over time, the grain yields of wheat and maize showed an increasing trend in all four fertilization treatments at the Yangling(YL) and Zhengzhou(ZZ) locations, while they declined at Qiyang(QY). The grain yield in the NPKM and 1.5NPKM treatments gradually exceeded that of the NPK and NPKS treatments at the QY site. The largest SYI was recorded in the NPKM treatment across the three sites, suggesting that inorganic fertilizer combined with manure can effectively improve crop yield sustainability. Higher SYI values were recorded at the YL and ZZ sites than at the QY site, possibly because the soil was more acid at QY. The key factors affecting grain yield were soil available phosphorus(AP) and available potassium(AK) at the YL and ZZ sites, and pH and AP at the QY site.All fertilization treatments resulted in soil N and P surpluses at the three sites, but soil K surpluses were recorded only at the QY site. The SFI was greater in the 1.5NPKM, NPKM and NPKS treatments than in the NPK treatment by 13.3–40.0 and 16.4–63.6% at the YL and ZZ sites, respectively, and was significantly higher in the NPKM and 1.5NPKM treatments than in the NPK and NPKS treatments at the QY site. A significant, positive linear relationship was found between SFI and crop yield, and SYI and nutrient balance, indicating that grain yield and its sustainability significantly increased with increasing soil fertility. The apparent N, P and K balances positively affected SFI.This study suggests that the appropriate amount of manure mixed with mineral NPK fertilizer is beneficial to the development of sustainable agriculture, which effectively increases the crop yield and yield sustainability by improving soil fertility.

Design of the Control System of Adaptive Balancing Device Based on PLC

Due to the well condition and the un-expected imbalance movement of the pumping unit in use, the energy consumes a lot. The existing balancing equipment cannot adjust and monitor the pumping units in real time. Therefore this paper introduces the new adaptive balancing equipment—fan-shaped adaptive balancing intelligent device, projects a design of such control system based on PLC, and determines the principle of the control system, the execution software and the design flow. Site commissioning effect on Daqing Oilfield shows this fan-shaped adaptive balancing intelligent device can effectively adjust and monitor the pumping unit in real time, the balance even adjusts from 0.787 to 0.901, and integrated energy saving rate is 14.2%. It is approved that this control device is professionally designed, with strong compatibility, and high reliability.

Dynamic Load Balancing Based on Restricted Multicast Tree in Homogeneous Multiprocessor Systems

To decrease the cost of exchanging load information among processors, a dynamic load-balancing （DLB） algorithm which adopts multieast tree technology is proposed. The muhieast tree construction rules are also proposed to avoid wrongly transferred or redundant DLB messages due to the overlapping of multicast trees. The proposed DLB algorithm is distributed controlled, sender initiated and can help heavily loaded processors with complete distribution of redundant loads with minimum number of executions. Experiments were executed to compare the effects of the proposed DLB algorithm and other three ones, the results prove the effectivity and practicability of the proposed algorithm in dealing with great scale compute-intensive tasks.

Research on data load balancing technology of massive storage systems for wearable devices

Because of the limited memory of the increasing amount of information in current wearable devices,the processing capacity of the servers in the storage system can not keep up with the speed of information growth,resulting in low load balancing,long load balancing time and data processing delay.Therefore,a data load balancing technology is applied to the massive storage systems of wearable devices in this paper.We first analyze the object-oriented load balancing method,and formally describe the dynamic load balancing issues,taking the load balancing as a mapping problem.Then,the task of assigning each data node and the request of the corresponding data node’s actual processing capacity are completed.Different data is allocated to the corresponding data storage node to complete the calculation of the comprehensive weight of the data storage node.According to the load information of each data storage node collected by the scheduler in the storage system,the load weight of the current data storage node is calculated and distributed.The data load balancing of the massive storage system for wearable devices is realized.The experimental results show that the average time of load balancing using this method is 1.75h,which is much lower than the traditional methods.The results show the data load balancing technology of the massive storage system of wearable devices has the advantages of short data load balancing time,high load balancing,strong data processing capability,short processing time and obvious application.

A Study on PC/104 Embedded Automatic Balancing Control System

In this paper,an embedded real-time control system for automatic rotor balancing was studied.Benefiting from the modular design,this system can be easily re-constituted or expanded under different working conditions.The special designed hardware resists harsh environment.As an embedded application,it was very important to save system consumptions on both hardware and software,so the algorithms for unbalance vibration identification and attenuation were deduced,meantime a unified fast algorithm structure was achieved through the geometric analysis.Based on this structure,the signal processing algorithm was tested by an open data source,while the control algorithm was simulated using a basic rotor model,and then connected to a hyper gravity machine running online auto-balancing.The result confirms that the unbalancing vibration is effectively restrained.

Balancing Chemical Equations by Systems of Linear Equations

In this paper, a formal and systematic method for balancing chemical reaction equations was presented. The results satisfy the law of conservation of matter, and confirm that there is no contradiction to the existing way(s) of balancing chemical equations. A chemical reaction which possesses atoms with fractional oxidation numbers that have unique coefficients was studied. In this paper, the chemical equations were balanced by representing the chemical equation into systems of linear equations. Particularly, the Gauss elimination method was used to solve the mathematical problem with this method, it was possible to handle any chemical reaction with given reactants and products.

Non-Cooperative Differential Game Based Load Balancing Algorithm in Radio-Over-Fibre System

Load balancing plays a critical role in a cellular network. As one kind of cellular network, Radio-over-Fibre （RoF） system can provide ubiquitous high data-rate transmissions, which has attracted many attentions, but it also suffer load unbalancing problem. In order to improve the system performance, in this paper, we propose a novel loading balance scheme in RoF system based on differential game theory. The scheme formulates the load allocated to each RAP （Radio Access Point） as a Nasb Equilibrium, using non-cooperative differential game to obtain the optical load allocation of each RAP. The simulations performed show that the non-cooperative differential game algorithm is applicable and the optimal load solution can be achieved.

Novel load balancing DHT scheme for the heterogeneous system

The capacities of the nodes in the peer-to-peer system are strongly heterogeneous, hence one can benefit from distributing the load, based on the capacity of the nodes. At first a model is discussed to evaluate the load balancing of the heterogeneous system, and then a novel load balancing scheme is proposed based on the concept of logical servers and the randomized binary tree, and theoretical guarantees are given. Finally, the feasibility of the scheme using extensive simulations is proven.

Coordinated Capacitor Voltage Balancing Method for Cascaded H-bridge Inverter with Supercapacitor and DC-DC Stage

Cascaded H-bridge inverter(CHBI) with supercapacitors(SCs) and dc-dc stage shows significant promise for medium to high voltage energy storage applications. This paper investigates the voltage balance of capacitors within the CHBI, including both the dc-link capacitors and SCs. Balance control over the dc-link capacitor voltages is realized by the dcdc stage in each submodule(SM), while a hybrid modulation strategy(HMS) is implemented in the H-bridge to balance the SC voltages among the SMs. Meanwhile, the dc-link voltage fluctuations are analyzed under the HMS. A virtual voltage variable is introduced to coordinate the balancing of dc-link capacitor voltages and SC voltages. Compared to the balancing method that solely considers the SC voltages, the presented method reduces the dc-link voltage fluctuations without affecting the voltage balance of SCs. Finally, both simulation and experimental results verify the effectiveness of the presented method.

Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication interfaces.These systems promote reliable and remote interactions between patients and healthcare professionals.However,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource availability.We propose a hybrid mobile cloud computing(HMCC)architecture to address these challenges.Furthermore,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture.We compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption performance.Challenging issues for cloudbased healthcare systems are discussed in detail.

Dynamic balancing of dual-rotor system with very little rotating speed difference

Unbalanced vibration in dual-rotor rotating machinery was studied with numerical simulations and experiments. A new method is proposed to separate vibration signals of inner and outer rotors for a system with very little difference in rotating speeds. Magnitudes and phase values of unbalance defects can be obtained directly by sampling the vibration signal synchronized with reference signal. The balancing process is completed by the reciprocity influence coefficients of inner and outer rotors method. Results showed the advantage of such method for a dual-rotor system as compared with conventional balancing.

Dynamic load balancing with learning model for Sudoku solving system

This paper proposes a dynamic load balancing with learning model for a Sudoku problem solving system that has multiple workers and multiple solvers.The objective is to minimise the total processing time of problem solving.Our load balancing with learning model distributes each Sudoku problem to an appropriate pair of worker and solver when it is received by the system.The information of the estimated solution time for a specific number of given input values,the estimated finishing time of each worker,and the idle status of each worker is used to determine the worker-solver pairs.In addition,the proposed system can estimate the waiting period for each problem.Test results show that the system has shorter processing time than conventional alternatives.

Frequency Control of Power System with Renewable Power Sources by HVDC Interconnection Line and Battery Considering Energy Balancing

Recently, introduction of renewable energy sources like wind power generation and photovoltaic power generation has been increasing from the viewpoint of environmental problems. However, renewable energy power supplies have unstable output due to the influence of weather conditions such as wind speed variations, which may cause fluctuations of voltage and frequency in the power system. This paper proposes fuzzy PD based virtual inertia control system to decrease frequency fluctuations in power system caused by fluctuating output of renewable energy sources. The proposed new method is based on the coordinated control of HVDC interconnection line and battery, and energy balancing control is also incorporated in it. Finally, it is concluded that the proposed system is very effective for suppressing the frequency fluctuations of the power system due to the large-scale wind power generation and solar power generation and also for keeping the energy balancing in the HVDC transmission line.

SINR-balancing based cooperative spectrum sharing scheme for MIMO cognitive radio systems

In order to improve the system capacity of the primary user （PU） and secondary user （SU） of multiple-input-multiple-output （MIMO） cognitive radio （CR） system, a signal to interference plus noise ratio balancing （SINR-balancing） posed, in which PU leases a fraction of based cooperative spectrum sharing （CSS） scheme is proits transmission time to SU in exchange for the SU relaying the PU＇ s data cooperatively. The SINR-balancing based corresponding beamforming vectors are designed and time-division is also optimized for the proposed scheme. Simulation results show that compared to conventional opportunistic spectrum sharing （OSS） scheme, the proposed CSS scheme can effectively enhance the system performance of both PU and SU and provide an effective cooperation mechanism for PU and SU to determine whether to request cooperation.

A Modified Multi-Agent System for Simple Assembly Line Balancing

In this paper, a modified multi-agent system for assembly line balancing is proposed. Each worker in the assembly line is regarded as an agent, and two neighboring agents exchange information about the allocated tasks. To balance the workload, an agent with a smaller workload sends a request message to his/her neighboring agent, who has a larger workload, to exchange tasks between them. Without any centralized control mechanism, each agent behaves to achieve their goal, which is to balance the workload. A tabu list and cooling control are also incorporated. However, the effectiveness of the previous system is limited, and the system depends on problems to be solved. As such, a modified system is proposed. In the proposed system, the cycle time is used when considering the proposal of exchange of allocated tasks instead of the task time allocated to the neighboring workers. Also, in the proposed system, the length of tabu list is determined dynamically based on the current number of possible exchanges, and the best cycle time in the search with cooling at medium speed is recorded for the second search that is finished when the current cycle time reaches the recorded cycle time. The effectiveness of the modified system is investigated by solving problems for various conditions. The results show that the proposed system is effective regardless of the problems that are encountered.

Analytical Load Balancing Model in Distributed Open Flow Controller System

The Software Defined Network (SDN) is a concept based on a decoupling between the control plan and the data plan of a network. Thus, the network becomes programmable and can be coupled to the business applications of the users. The study that is discussed in this article looks at load planning and balancing in distributed controllers. To do this, a model and theoretical methods of performance evaluation related to appropriate software tools, to predict and control the quality of service offered to users is exposed. This paper exposed also a distributed architecture of controllers and then a module based on an adaptive load balancing algorithm that is fault tolerant and fluctuates controller loads. The experiments show a significant gain in efficiency of our solution.

Distributed Graph Database Load Balancing Method Based on Deep Reinforcement Learning

This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies.Unlike indepen-dent batch tasks,workflows typically consist of multiple subtasks with intrinsic correlations and dependencies.It necessitates the distribution of various computational tasks to appropriate computing node resources in accor-dance with task dependencies to ensure the smooth completion of the entire workflow.Workflow scheduling must consider an array of factors,including task dependencies,availability of computational resources,and the schedulability of tasks.Therefore,this paper delves into the distributed graph database workflow task scheduling problem and proposes a workflow scheduling methodology based on deep reinforcement learning(DRL).The method optimizes the maximum completion time(makespan)and response time of workflow tasks,aiming to enhance the responsiveness of workflow tasks while ensuring the minimization of the makespan.The experimental results indicate that the Q-learning Deep Reinforcement Learning(Q-DRL)algorithm markedly diminishes the makespan and refines the average response time within distributed graph database environments.In quantifying makespan,Q-DRL achieves mean reductions of 12.4%and 11.9%over established First-fit and Random scheduling strategies,respectively.Additionally,Q-DRL surpasses the performance of both DRL-Cloud and Improved Deep Q-learning Network(IDQN)algorithms,with improvements standing at 4.4%and 2.6%,respectively.With reference to average response time,the Q-DRL approach exhibits a significantly enhanced performance in the scheduling of workflow tasks,decreasing the average by 2.27%and 4.71%when compared to IDQN and DRL-Cloud,respectively.The Q-DRL algorithm also demonstrates a notable increase in the efficiency of system resource utilization,reducing the average idle rate by 5.02%and 9.30%in comparison to IDQN and DRL-Cloud,respectively.These findings support the assertion that Q-DRL not only upholds a lower average idle rate but also effectively curtails the average response time,thereby substantially improving processing efficiency and optimizing resource utilization within distributed graph database systems.