The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is n...The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.展开更多
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...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.展开更多
Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of t...Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution,and an inefficient task scheduling algorithm results in under-or over-utilization of the resources,which in turn leads to degradation of the services.Therefore,in the proposed work,load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process.In this paper,we propose an adaptive genetic algorithm-based load balancing(GALB)-aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan,performance improvement ratio,and degree of imbalance.The concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the elimination.CloudSim simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration.展开更多
Dynamic task assignment and migration are the key technique to load balancing which plays an important role in the achievement of high performance in distributed computing system. In this paper, we describe the design...Dynamic task assignment and migration are the key technique to load balancing which plays an important role in the achievement of high performance in distributed computing system. In this paper, we describe the design and implementation of an online thread scheduling and migration system (S&M) based on a previous work of LWP -MPI. Experimental results show that performance is enhanced.展开更多
According to the advances in users’service requirements,physical hardware accessibility,and speed of resource delivery,Cloud Computing(CC)is an essential technology to be used in many fields.Moreover,the Internet of ...According to the advances in users’service requirements,physical hardware accessibility,and speed of resource delivery,Cloud Computing(CC)is an essential technology to be used in many fields.Moreover,the Internet of Things(IoT)is employed for more communication flexibility and richness that are required to obtain fruitful services.A multi-agent system might be a proper solution to control the load balancing of interaction and communication among agents.This paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number of IoT devices with low resources.Different agents are integrated based on relevant features of behavioral interaction using classification techniques to balance the workload.Aload balancing algorithm is developed to serve users’requests to improve the solution of workload problems with an efficient distribution.The activity task from IoT devices has been classified by feature selection methods in the preparatory phase to optimize the scalability ofCC.Then,the server’s availability is checked and the classified task is assigned to its suitable server in the main phase to enhance the cloud environment performance.Multi-agent load balancing framework is succeeded to cope with the importance of using large-scale requirements of CC and(low resources and large number)of IoT.展开更多
In this paper, the objective of minimum load balancing index （LBI） for the 16-bus distribution system is achieved using bacterial foraging optimization algorithm （BFOA）. The feeder reconfiguration problem is formu...In this paper, the objective of minimum load balancing index （LBI） for the 16-bus distribution system is achieved using bacterial foraging optimization algorithm （BFOA）. The feeder reconfiguration problem is formulated as a non-linear optimization problem and the optimal solution is obtained using BFOA. With the proposed reconfiguration method, the radial structure of the distribution system is retained and the burden on the optimization technique is reduced. Test results are presented for the 16-bus sample network, the proposed reconfiguration method has effectively decreased the LBI, and the BFOA technique is efficient in searching for the optimal solution.展开更多
With the advancement in the science and technology,cloud computing has become a recent trend in environment with immense requirement of infrastructure and resources.Load balancing of cloud computing environments is an...With the advancement in the science and technology,cloud computing has become a recent trend in environment with immense requirement of infrastructure and resources.Load balancing of cloud computing environments is an important matter of concern.The migration of the overloaded virtual machines(VMs)to the underloaded VM with optimized resource utilization is the effective way of the load balancing.In this paper,a new VM migration algorithm for the load balancing in the cloud is proposed.The migration algorithm proposed(EGSA-VMM)is based on exponential gravitational search algorithm which is the integration of gravitational search algorithm and exponential weighted moving average theory.In our approach,the migration is done based on the migration cost and QoS.The experimentation of proposed EGSA-based VM migration algorithm is compared with ACO and GSA.The simulation of experiments shows that the proposed EGSA-VMM algorithm achieves load balancing and reasonable resource utilization,which outperforms existing migration strategies in terms of number of VM migrations and number of SLA violations.展开更多
It is desirable in a distributed system to have the system load balanced evenly among the nodes so that the mean job response time is minimized. In this paper, we present.a dynamic load balancing mechanism (DLB). It a...It is desirable in a distributed system to have the system load balanced evenly among the nodes so that the mean job response time is minimized. In this paper, we present.a dynamic load balancing mechanism (DLB). It adopts a centralized approach and is network topology independent. The DLB mechanism employs a set of thresholds which are automatically adjusted as the system load changes. lt also provides a simple mechanism for the system to switch between periodic and instantaneous load balancing policies with ease. The performance of the proposed algorithm is evaluated by intensive simulations for various parameters. The simulAtion results show that the mean job response time in a system implementing DLB algorithm is significantly lower than the same system without load balancings. Furthermore, compared with a previously proposed algorithm, DLB algorithm demonstrates improved performance, especially when the system is heavily loaded and the load is unevenly distributed.展开更多
Load balancing is an important stage of a system using parallel computing where the aim is the balance of workload among all processors of the system. In this paper, we introduce a new load balancing algorithm with ne...Load balancing is an important stage of a system using parallel computing where the aim is the balance of workload among all processors of the system. In this paper, we introduce a new load balancing algorithm with new capabilities for parallel systems, among which is the independence of a separate route-finder algorithm between the load receiver and sender nodes. In addition to simulation of the new algorithm, due to similarity in behavior to the proposed algorithm, the central algorithm is simulated. Simulation results show that, the system performance increases with the increase of the degree of neighborhood between the processors. These results also indicate the algorithm’s high compatibility with environment changes.展开更多
Many latest high performance distributed computational environments come with high bandwidth in commu- nication. Such high bandwidth distributed systems provide unprecedented opportunities for analyzing huge datasets,...Many latest high performance distributed computational environments come with high bandwidth in commu- nication. Such high bandwidth distributed systems provide unprecedented opportunities for analyzing huge datasets, but simultaneously posts new technical challenges. For users, progressive query answering is important. For utility of systems, load balancing is critical. How we can achieve progressive and load balancing distributed computation is an interesting and promising research direction. As skyline analysis has been shown very useful in many multi-criteria decision making applications, in this paper, we study the problem of progressive and load balancing distributed skyline analysis. We propose a simple yet scalable approach which comes with several nice properties for progressive and load balancing query answering. We conduct extensive experiments which demonstrate the feasibility and effectiveness of the proposed method.展开更多
In cloud computing,a lot of challenges like the server failures,loss of confidentiality,improper workloads,etc.are still bounding the efficiency of cloud systems in real-world scenarios.For this reason,many research w...In cloud computing,a lot of challenges like the server failures,loss of confidentiality,improper workloads,etc.are still bounding the efficiency of cloud systems in real-world scenarios.For this reason,many research works are being performed to overcome the shortcoming of existing systems.Among them,load balancing seems to be the most critical issue that worsen the performance of the cloud sector,and hence there necessitates the optimal load balancing with optimal task scheduling.With the intention of accomplishing optimal load balancing by effectual task deployment,this paper plans to develop an advanced load balancing model with the assistance acquired from the metaheuristic algorithms.Usually,handling of tasks in cloud system is an NP-hard problem and moreover,nonpreemptive independent tasks are crucial in cloud computing.This paper goes with the introduction of a new optimal load balancing model by considering three major objectives:minimum makespan,priority,and load balancing,respectively.Moreover,a new single-objective function is also defined that incorporates all the three objectives mentioned above.Furthermore,the deployment of tasks must be optimal and for this a new hybrid optimization algorithm referred as Firefly Movement insistedWOA(FM-WOA)is introduced.This FM-WOA is the conceptual amalgamation of standard Whale Optimization Algorithm(WOA)and Firefly(FF)algorithm.Finally,the performances of the proposed FM-WOA model is compared over the conventional models with the intention of proving its efficiency in terms of makespan,task completion(priority),and degree of imbalance as well.展开更多
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 consider...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.展开更多
All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide...All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide application software with a reliable solution during the initial stages of load balancing.The cloud computing environment is the subject of this study.It consists of both physical and logical components(most notably cloud infrastructure and cloud storage)(in particular cloud services and cloud platforms).This intricate structure is interconnected to provide services to users and improve the overall system’s performance.This case study is one of the most important segments of cloud computing,i.e.,Load Balancing.This paper aims to introduce a new approach to balance the load among Virtual Machines(VM’s)of the cloud computing environment.The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm(BA).This proposed Modified Bat Algorithm(MBA)allows balancing the load among virtual machines.The proposed algorithm works in two variants:MBA with Overloaded Optimal Virtual Machine(MBAOOVM)and Modified Bat Algorithm with Balanced Virtual Machine(MBABVM).MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm.展开更多
Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resour...Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resources to the maximum.The resource utilization is highly dependent on the allocation of resources to the incoming request.The allocation of requests is done with respect to the physical machines present in the datacenter.While allocating the tasks to these physical machines,it needs to be allocated in such a way that no physical machine is underutilized or over loaded.To make sure of this,optimal load balancing is very important.Design/methodology/approach-The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks.The major focus of the proposed work is to optimize the load balancing in a datacenter.When optimization happens,none of the physical machine is neither overloaded nor under-utilized,hence resulting in efficient utilization of the resources.Findings-The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load(RR)ant colony optimization(ACO),artificial bee colony(ABC)with respect to the selected parameters response time,virtual machine migrations,host shut down and energy consumption.All the four parameters gave a positive result when the algorithm is simulated.Originality/value-The contribution of this paper is towards the domain of cloud load balancing.The paper is proposing a novel approach to optimize the cloud load balancing process.The results obtained show that response time,virtual machine migrations,host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study.The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.展开更多
Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the chall...Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.展开更多
Task scheduling determines the performance of NOW computing to a large extent. However, the computer system architecture, computing capability and system load are rarely proposed together. In this paper, a biggest het...Task scheduling determines the performance of NOW computing to a large extent. However, the computer system architecture, computing capability and system load are rarely proposed together. In this paper, a biggest heterogeneous scheduling algorithm is presented. It fully considers the system characteristics (from application view), structure and state. So it always can utilize all processing resource under a reasonable premise. The results of experiment show the algorithm can significantly shorten the response time of jobs.展开更多
Load distribution is a key technology in strip hot rolling process, which influences the coil＇s mierostrueture and performance. Currently, Newton-Raphson algorithm is applied to load distribution of hot tandem mills ...Load distribution is a key technology in strip hot rolling process, which influences the coil＇s mierostrueture and performance. Currently, Newton-Raphson algorithm is applied to load distribution of hot tandem mills in many hot rolling plants and has some serious defects such as having a strict restriction on initial iterative calculation value and requiring coefficient matrix of nonlinear equations to be nonsingular. To eliminate these defects and improve the online performance of the process control computer, Newton descendent numeric algorithm is introduced to this field to widen the initial value range and a new model named error conversion algorithm is put forth to deal with special conditions when the coefficient matrix is singular. Furthermore, considering the characteristics of load distribution, a condition of strip thickness distribution abnormality and corresponding solutions are provided which ensure that rolling parameters can be calculated normally. Simulation results show that the improved algorithm has overcome the defects of the Newton-Raphson algorithm and is suitable for online application.展开更多
The real problem in cluster of workstations is the changes in workstation power or number of workstations or dynmaic changes in the run time behavior of the application hamper the efficient use of resources. Dynamic l...The real problem in cluster of workstations is the changes in workstation power or number of workstations or dynmaic changes in the run time behavior of the application hamper the efficient use of resources. Dynamic load balancing is a technique for the parallel implementation of problems, which generate unpredictable workloads by migration work units from heavily loaded processor to lightly loaded processors at run time. This paper proposed an efficient load balancing method in which parallel tree computations depth first search (DFS) generates unpredictable, highly imbalance workloads and moves through different phases detectable at run time, where dynamic load balancing strategy is applicable in each phase running under the MPI(message passing interface) and Unix operating system on cluster of workstations parallel platform computing.展开更多
Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study...Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.展开更多
Cloud computing technology facilitates computing-intensive applications by providing virtualized resources which can be dynamically provisioned. However, user’s requests are varied according to different applications...Cloud computing technology facilitates computing-intensive applications by providing virtualized resources which can be dynamically provisioned. However, user’s requests are varied according to different applications’ computation ability needs. These applications can be presented as meta-job of user’s demand. The total processing time of these jobs may need data transmission time over the Internet as well as the completed time of jobs to execute on the virtual machine must be taken into account. In this paper, we presented V-heuristics scheduling algorithm for allocation of virtualized network and computing resources under user’s constraint which applied into a service-oriented resource broker for jobs scheduling. This scheduling algorithm takes into account both data transmission time and computation time that related to virtualized network and virtual machine. The simulation results are compared with three different types of heuristic algorithms under conventional network or virtual network conditions such as MCT, Min-Min and Max-Min. e evaluate these algorithms within a simulated cloud environment via an abilenenetwork topology which is real physical core network topology. These experimental results show that V-heuristic scheduling algorithm achieved significant performance gain for a variety of applications in terms of load balance, Makespan, average resource utilization and total processing time.展开更多
文摘The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.
文摘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.
文摘Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution,and an inefficient task scheduling algorithm results in under-or over-utilization of the resources,which in turn leads to degradation of the services.Therefore,in the proposed work,load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process.In this paper,we propose an adaptive genetic algorithm-based load balancing(GALB)-aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan,performance improvement ratio,and degree of imbalance.The concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the elimination.CloudSim simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration.
文摘Dynamic task assignment and migration are the key technique to load balancing which plays an important role in the achievement of high performance in distributed computing system. In this paper, we describe the design and implementation of an online thread scheduling and migration system (S&M) based on a previous work of LWP -MPI. Experimental results show that performance is enhanced.
文摘According to the advances in users’service requirements,physical hardware accessibility,and speed of resource delivery,Cloud Computing(CC)is an essential technology to be used in many fields.Moreover,the Internet of Things(IoT)is employed for more communication flexibility and richness that are required to obtain fruitful services.A multi-agent system might be a proper solution to control the load balancing of interaction and communication among agents.This paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number of IoT devices with low resources.Different agents are integrated based on relevant features of behavioral interaction using classification techniques to balance the workload.Aload balancing algorithm is developed to serve users’requests to improve the solution of workload problems with an efficient distribution.The activity task from IoT devices has been classified by feature selection methods in the preparatory phase to optimize the scalability ofCC.Then,the server’s availability is checked and the classified task is assigned to its suitable server in the main phase to enhance the cloud environment performance.Multi-agent load balancing framework is succeeded to cope with the importance of using large-scale requirements of CC and(low resources and large number)of IoT.
文摘In this paper, the objective of minimum load balancing index （LBI） for the 16-bus distribution system is achieved using bacterial foraging optimization algorithm （BFOA）. The feeder reconfiguration problem is formulated as a non-linear optimization problem and the optimal solution is obtained using BFOA. With the proposed reconfiguration method, the radial structure of the distribution system is retained and the burden on the optimization technique is reduced. Test results are presented for the 16-bus sample network, the proposed reconfiguration method has effectively decreased the LBI, and the BFOA technique is efficient in searching for the optimal solution.
文摘With the advancement in the science and technology,cloud computing has become a recent trend in environment with immense requirement of infrastructure and resources.Load balancing of cloud computing environments is an important matter of concern.The migration of the overloaded virtual machines(VMs)to the underloaded VM with optimized resource utilization is the effective way of the load balancing.In this paper,a new VM migration algorithm for the load balancing in the cloud is proposed.The migration algorithm proposed(EGSA-VMM)is based on exponential gravitational search algorithm which is the integration of gravitational search algorithm and exponential weighted moving average theory.In our approach,the migration is done based on the migration cost and QoS.The experimentation of proposed EGSA-based VM migration algorithm is compared with ACO and GSA.The simulation of experiments shows that the proposed EGSA-VMM algorithm achieves load balancing and reasonable resource utilization,which outperforms existing migration strategies in terms of number of VM migrations and number of SLA violations.
文摘It is desirable in a distributed system to have the system load balanced evenly among the nodes so that the mean job response time is minimized. In this paper, we present.a dynamic load balancing mechanism (DLB). It adopts a centralized approach and is network topology independent. The DLB mechanism employs a set of thresholds which are automatically adjusted as the system load changes. lt also provides a simple mechanism for the system to switch between periodic and instantaneous load balancing policies with ease. The performance of the proposed algorithm is evaluated by intensive simulations for various parameters. The simulAtion results show that the mean job response time in a system implementing DLB algorithm is significantly lower than the same system without load balancings. Furthermore, compared with a previously proposed algorithm, DLB algorithm demonstrates improved performance, especially when the system is heavily loaded and the load is unevenly distributed.
文摘Load balancing is an important stage of a system using parallel computing where the aim is the balance of workload among all processors of the system. In this paper, we introduce a new load balancing algorithm with new capabilities for parallel systems, among which is the independence of a separate route-finder algorithm between the load receiver and sender nodes. In addition to simulation of the new algorithm, due to similarity in behavior to the proposed algorithm, the central algorithm is simulated. Simulation results show that, the system performance increases with the increase of the degree of neighborhood between the processors. These results also indicate the algorithm’s high compatibility with environment changes.
基金Supported by the Doctoral Research Foundation of the Natural Science Foundation of Guangdong Province under Grant No.8451064101000054the National Natural Science Foundation of China under Grant Nos. 60773198,60703111+3 种基金Natural Science Foundation of Guangdong Province under Grant Nos. 06104916,8151027501000021Research Foundation of Science and Technology PlanProject in Guangdong Province under Grant No. 2008B050100040Program for New Century Excellent Talents in University ofChina under Grant No. NCET-06-0727the Fundamental Research Funds for the Central Universities,SCUT,under Grant No.2009ZM0008
文摘Many latest high performance distributed computational environments come with high bandwidth in commu- nication. Such high bandwidth distributed systems provide unprecedented opportunities for analyzing huge datasets, but simultaneously posts new technical challenges. For users, progressive query answering is important. For utility of systems, load balancing is critical. How we can achieve progressive and load balancing distributed computation is an interesting and promising research direction. As skyline analysis has been shown very useful in many multi-criteria decision making applications, in this paper, we study the problem of progressive and load balancing distributed skyline analysis. We propose a simple yet scalable approach which comes with several nice properties for progressive and load balancing query answering. We conduct extensive experiments which demonstrate the feasibility and effectiveness of the proposed method.
文摘In cloud computing,a lot of challenges like the server failures,loss of confidentiality,improper workloads,etc.are still bounding the efficiency of cloud systems in real-world scenarios.For this reason,many research works are being performed to overcome the shortcoming of existing systems.Among them,load balancing seems to be the most critical issue that worsen the performance of the cloud sector,and hence there necessitates the optimal load balancing with optimal task scheduling.With the intention of accomplishing optimal load balancing by effectual task deployment,this paper plans to develop an advanced load balancing model with the assistance acquired from the metaheuristic algorithms.Usually,handling of tasks in cloud system is an NP-hard problem and moreover,nonpreemptive independent tasks are crucial in cloud computing.This paper goes with the introduction of a new optimal load balancing model by considering three major objectives:minimum makespan,priority,and load balancing,respectively.Moreover,a new single-objective function is also defined that incorporates all the three objectives mentioned above.Furthermore,the deployment of tasks must be optimal and for this a new hybrid optimization algorithm referred as Firefly Movement insistedWOA(FM-WOA)is introduced.This FM-WOA is the conceptual amalgamation of standard Whale Optimization Algorithm(WOA)and Firefly(FF)algorithm.Finally,the performances of the proposed FM-WOA model is compared over the conventional models with the intention of proving its efficiency in terms of makespan,task completion(priority),and degree of imbalance as well.
基金supported by Scientific Research Foundation for the Returned Overseas Chinese ScholarsState Education Ministry under Grant No.2010-2011 and Chinese Post-doctoral Research Foundation
文摘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.
基金We deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project Number(TURSP-2020/313),Taif University,Taif,Saudi Arabia.
文摘All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide application software with a reliable solution during the initial stages of load balancing.The cloud computing environment is the subject of this study.It consists of both physical and logical components(most notably cloud infrastructure and cloud storage)(in particular cloud services and cloud platforms).This intricate structure is interconnected to provide services to users and improve the overall system’s performance.This case study is one of the most important segments of cloud computing,i.e.,Load Balancing.This paper aims to introduce a new approach to balance the load among Virtual Machines(VM’s)of the cloud computing environment.The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm(BA).This proposed Modified Bat Algorithm(MBA)allows balancing the load among virtual machines.The proposed algorithm works in two variants:MBA with Overloaded Optimal Virtual Machine(MBAOOVM)and Modified Bat Algorithm with Balanced Virtual Machine(MBABVM).MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm.
文摘Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resources to the maximum.The resource utilization is highly dependent on the allocation of resources to the incoming request.The allocation of requests is done with respect to the physical machines present in the datacenter.While allocating the tasks to these physical machines,it needs to be allocated in such a way that no physical machine is underutilized or over loaded.To make sure of this,optimal load balancing is very important.Design/methodology/approach-The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks.The major focus of the proposed work is to optimize the load balancing in a datacenter.When optimization happens,none of the physical machine is neither overloaded nor under-utilized,hence resulting in efficient utilization of the resources.Findings-The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load(RR)ant colony optimization(ACO),artificial bee colony(ABC)with respect to the selected parameters response time,virtual machine migrations,host shut down and energy consumption.All the four parameters gave a positive result when the algorithm is simulated.Originality/value-The contribution of this paper is towards the domain of cloud load balancing.The paper is proposing a novel approach to optimize the cloud load balancing process.The results obtained show that response time,virtual machine migrations,host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study.The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.
基金jointly supported by the Jiangsu Postgraduate Research and Practice Innovation Project under Grant KYCX22_1030,SJCX22_0283 and SJCX23_0293the NUPTSF under Grant NY220201.
文摘Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.
文摘Task scheduling determines the performance of NOW computing to a large extent. However, the computer system architecture, computing capability and system load are rarely proposed together. In this paper, a biggest heterogeneous scheduling algorithm is presented. It fully considers the system characteristics (from application view), structure and state. So it always can utilize all processing resource under a reasonable premise. The results of experiment show the algorithm can significantly shorten the response time of jobs.
基金Item Sponsored by National Natural Science Foundation of China (50504007)
文摘Load distribution is a key technology in strip hot rolling process, which influences the coil＇s mierostrueture and performance. Currently, Newton-Raphson algorithm is applied to load distribution of hot tandem mills in many hot rolling plants and has some serious defects such as having a strict restriction on initial iterative calculation value and requiring coefficient matrix of nonlinear equations to be nonsingular. To eliminate these defects and improve the online performance of the process control computer, Newton descendent numeric algorithm is introduced to this field to widen the initial value range and a new model named error conversion algorithm is put forth to deal with special conditions when the coefficient matrix is singular. Furthermore, considering the characteristics of load distribution, a condition of strip thickness distribution abnormality and corresponding solutions are provided which ensure that rolling parameters can be calculated normally. Simulation results show that the improved algorithm has overcome the defects of the Newton-Raphson algorithm and is suitable for online application.
基金Natural Science Foundation of China (No.60 173 0 3 1)
文摘The real problem in cluster of workstations is the changes in workstation power or number of workstations or dynmaic changes in the run time behavior of the application hamper the efficient use of resources. Dynamic load balancing is a technique for the parallel implementation of problems, which generate unpredictable workloads by migration work units from heavily loaded processor to lightly loaded processors at run time. This paper proposed an efficient load balancing method in which parallel tree computations depth first search (DFS) generates unpredictable, highly imbalance workloads and moves through different phases detectable at run time, where dynamic load balancing strategy is applicable in each phase running under the MPI(message passing interface) and Unix operating system on cluster of workstations parallel platform computing.
基金This work was supported by Shandong medical and health science and technology development plan project(No.202012070393).
文摘Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.
文摘Cloud computing technology facilitates computing-intensive applications by providing virtualized resources which can be dynamically provisioned. However, user’s requests are varied according to different applications’ computation ability needs. These applications can be presented as meta-job of user’s demand. The total processing time of these jobs may need data transmission time over the Internet as well as the completed time of jobs to execute on the virtual machine must be taken into account. In this paper, we presented V-heuristics scheduling algorithm for allocation of virtualized network and computing resources under user’s constraint which applied into a service-oriented resource broker for jobs scheduling. This scheduling algorithm takes into account both data transmission time and computation time that related to virtualized network and virtual machine. The simulation results are compared with three different types of heuristic algorithms under conventional network or virtual network conditions such as MCT, Min-Min and Max-Min. e evaluate these algorithms within a simulated cloud environment via an abilenenetwork topology which is real physical core network topology. These experimental results show that V-heuristic scheduling algorithm achieved significant performance gain for a variety of applications in terms of load balance, Makespan, average resource utilization and total processing time.
