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.

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.

Dynamic load balancing based on restricted multicast tree in triplet-based hierarchical interconnection network

To solve the load balancing problem in a triplet-based hierarchical interconnection network（THIN） system, a dynamic load balancing （DLB）algorithm--THINDLBA, which adopts multicast tree （MT）technology to improve the efficiency of interchanging load information, is presented. To support the algorithm, a complete set of DLB messages and a schema of maintaining DLB information in each processing node are designed. The load migration request messages from the heavily loaded node （HLN）are spread along an MT whose root is the HLN. And the lightly loaded nodes（LLNs） covered by the MT are the candidate destinations of load migration; the load information interchanged between the LLNs and the HLN can be transmitted along the MT. So the HLN can migrate excess loads out as many as possible during a one time execution of the THINDLBA, and its load state can be improved as quickly as possible. To avoid wrongly transmitted or redundant DLB messages due to MT overlapping, the MT construction is restricted in the design of the THINDLBA. Through experiments, the effectiveness of four DLB algorithms are compared, and the results show that the THINDLBA can effectively decrease the time costs of THIN systems in dealing with large scale computeintensive tasks more than others.

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.

A Dynamic Load Balancing Method of Cloud-Center Based on SDN

In order to balancing based on data achieve dynamic load flow level, in this paper, we apply SDN technology to the cloud data center, and propose a dynamic load balancing method of cloud center based on SDN. The approach of using the SDN technology in the current task scheduling flexibility, accomplish real-time monitoring of the service node flow and load condition by the OpenFlow protocol. When the load of system is imbalanced, the controller can allocate globally network resources. What＇s more, by using dynamic correction, the load of the system is not obvious tilt in the long run. The results of simulation show that this approach can realize and ensure that the load will not tilt over a long period of time, and improve the system throughput.

MDSLB:A new static load balancing method for parallel molecular dynamics simulations

Large-scale parallelization of molecular dynamics simulations is facing challenges which seriously affect the simula- tion efficiency, among which the load imbalance problem is the most critical. In this paper, we propose, a new molecular dynamics static load balancing method （MDSLB）. By analyzing the characteristics of the short-range force of molecular dynamics programs running in parallel, we divide the short-range force into three kinds of force models, and then pack- age the computations of each force model into many tiny computational units called ＂cell loads＂, which provide the basic data structures for our load balancing method. In MDSLB, the spatial region is separated into sub-regions called ＂local domains＂, and the cell loads of each local domain are allocated to every processor in turn. Compared with the dynamic load balancing method, MDSLB can guarantee load balance by executing the algorithm only once at program startup without migrating the loads dynamically. We implement MDSLB in OpenFOAM software and test it on TianHe-lA supercomputer with 16 to 512 processors. Experimental results show that MDSLB can save 34%-64% time for the load imbalanced cases.

LOW-COST HIGH PERFORMANCE CLUSTER OF WORK-STATIONS BASED ON DYNAMIC LOAD BALANCING FOR PARALLEL DEPTH-FIRST SEARCH(DFS)

This paper presented an idea to replace the traditionally expensive parallel machines by heterogeneous cluster of workstations. To emphasise the usability of cluster of workstations platform for parallel and distributed computing, also the paper presented the status report on the effort and experiences for the implementation of a dynamic load balancing for parallel tree computation depth first search(DFS) on the cluster of a workstations project. It compared the speedup performance obtained from our platform with that obtained from the traditional one. The speedup results show that cluster of workstations can be a serious alternative to the expensive parallel machines.

CLUSTER OF WORKSTATIONS BASED ON DYNAMIC LOAD BALANCING FOR PARALLEL TREE COMPUTATION DEPTH-FIRST-SEARCH

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.

Optimization of Load Balancing Algorithm for Virtual Machine Dynamic Migration under Mobile Cloud Computing

As one of the key technologies of cloud computing,the virtualization technology can virtualize all kinds of resources and integrate them into the unified planning of the cloud computing management platform.The migration of virtual machines is one of the important technologies of virtual machine applications.However,there are still many deficiencies in the implementation of load balancing by virtual machine dynamic migration in cloud computing.Traditional triggering strategy thresholds are mostly fixed.If there is an instantaneous peak,it will cause migration,which will cause a waste of resources.In order to solve this problem,based on improving the dynamic migration framework,this paper proposes node selection optimization algorithm and node load balancing strategy and designs a prediction module,which uses a one-time smooth prediction to avoid the shortcoming of peak load moment.The simulation experiments and conclusions analysis results show that the fusion algorithm has performance advantages obvious.

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.

Implementation Study of Dynamic Load Balancing Algorithm of Parallel Tree Computation on Clusters of Heterogeneous Workstation

The rapid growth of interconnected high performance workstations has produced a new computing paradigm called clustered of workstations computing. In these systems load balance problem is a serious impediment to achieve good performance. The main concern of this paper is the implementation of dynamic load balancing algorithm, asynchronous Round Robin (ARR), for balancing workload of parallel tree computation depth-first-search algorithm on Cluster of Heterogeneous Workstations (COW) Many algorithms in artificial intelligence and other areas of computer science are based on depth first search in implicitty defined trees. For these algorithms a load-balancing scheme is required, which is able to evenly distribute parts of an irregularly shaped tree over the workstations with minimal interprocessor communication and without prior knowledge of the tree’s shape. For the (ARR) algorithm only minimal interprocessor communication is needed when necessary and it runs under the MPI (Message passing interface) that allows parallel execution on heterogeneous SUN cluster of workstation platform. The program code is written in C language and executed under UNIX operating system (Solaris version).

Dynamic Load Balancing with Overlay-Based Reconfiguration for Wireless Sensor Networks

Wireless sensor networks are characterized by multihop wireless links and resource constrained nodes. In terms of data collection and forwarding scheduling, this paper investigates the load balancing in sensor nodes and wireless link based on the performance of wireless sensor networks. Leveraging the property of dissimilarity distribution, a method to quantitatively evaluate the benefits of load balancing is presented, in order to access the profitability. Then a novel Dynamic Load Balancing of Overlay-based WSN (DLBO) algorithm has been put forward. In particular, the tradeoff between transferring ratio and the load imbalance among nodes is discussed. The load balancing method in this paper outperforms others based on balancing factor, different nodes number and data scales of applications. The proposed model and analytical results can be effectively applied for reliability analysis for other wireless applications (e.g., persistent data delivery is involved).

Novel Models and Algorithms of Load Balancing for Variable-structured Collaborative Simulation under HLA/RTI

High level architecture(HLA) is the open standard in the collaborative simulation field. Scholars have been paying close attention to theoretical research on and engineering applications of collaborative simulation based on HLA/RTI, which extends HLA in various aspects like functionality and efficiency. However, related study on the load balancing problem of HLA collaborative simulation is insufficient. Without load balancing, collaborative simulation under HLA/RTI may encounter performance reduction or even fatal errors. In this paper, load balancing is further divided into static problems and dynamic problems. A multi-objective model is established and the randomness of model parameters is taken into consideration for static load balancing, which makes the model more credible. The Monte Carlo based optimization algorithm(MCOA) is excogitated to gain static load balance. For dynamic load balancing, a new type of dynamic load balancing problem is put forward with regards to the variable-structured collaborative simulation under HLA/RTI. In order to minimize the influence against the running collaborative simulation, the ordinal optimization based algorithm(OOA) is devised to shorten the optimization time. Furthermore, the two algorithms are adopted in simulation experiments of different scenarios, which demonstrate their effectiveness and efficiency. An engineering experiment about collaborative simulation under HLA/RTI of high speed electricity multiple units(EMU) is also conducted to indentify credibility of the proposed models and supportive utility of MCOA and OOA to practical engineering systems. The proposed research ensures compatibility of traditional HLA, enhances the ability for assigning simulation loads onto computing units both statically and dynamically, improves the performance of collaborative simulation system and makes full use of the hardware resources.

An Advanced Dynamic Scheduling for Achieving Optimal Resource Allocation

Cloud computing distributes task-parallel among the various resources.Applications with self-service supported and on-demand service have rapid growth.For these applications,cloud computing allocates the resources dynami-cally via the internet according to user requirements.Proper resource allocation is vital for fulfilling user requirements.In contrast,improper resource allocations result to load imbalance,which leads to severe service issues.The cloud resources implement internet-connected devices using the protocols for storing,communi-cating,and computations.The extensive needs and lack of optimal resource allo-cating scheme make cloud computing more complex.This paper proposes an NMDS(Network Manager based Dynamic Scheduling)for achieving a prominent resource allocation scheme for the users.The proposed system mainly focuses on dimensionality problems,where the conventional methods fail to address them.The proposed system introduced three–threshold mode of task based on its size STT,MTT,LTT(small,medium,large task thresholding).Along with it,task mer-ging enables minimum energy consumption and response time.The proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme(EDS)and Decentralized Virtual Machine Migration(DVM).With a Network Manager-based Dynamic Scheduling,the proposed model achieves excellence in resource allocation compared to the other existing models.The obtained results shows the proposed system effectively allocate the resources and achieves about 94%of energy efficient than the other models.The evaluation metrics taken for comparison are energy consumption,mean response time,percentage of resource utilization,and migration.

MDLB:a metadata dynamic load balancing mechanism based on reinforcement learning

With the growing amount of information and data, object-oriented storage systems have been widely used in many applications, including the Google File System, Amazon S3, Hadoop Distributed File System, and Ceph, in which load balancing of metadata plays an important role in improving the input/output performance of the entire system. Unbalanced load on the metadata server leads to a serious bottleneck problem for system performance. However, most existing metadata load balancing strategies, which are based on subtree segmentation or hashing, lack good dynamics and adaptability. In this study, we propose a metadata dynamic load balancing(MDLB) mechanism based on reinforcement learning(RL). We learn that the Q_learning algorithm and our RL-based strategy consist of three modules, i.e., the policy selection network, load balancing network, and parameter update network. Experimental results show that the proposed MDLB algorithm can adjust the load dynamically according to the performance of the metadata servers, and that it has good adaptability in the case of sudden change of data volume.

QoS-Aware and Resource-Efficient Dynamic Slicing Mechanism for Internet of Things

With the popularization of terminal devices and services in Internet of things(IoT),it will be a challenge to design a network resource allocation method meeting various QoS requirements and effectively using substrate resources.In this paper,a dynamic network slicing mechanism including virtual network(VN)mapping and VN reconfiguration is proposed to provide network slices for services.Firstly,a service priority model is defined to create queue for resource allocation.Then a slice including Virtual Network Function(VNF)placement and routing with optimal cost is generated by VN mapping.Next,considering temporal variations of service resource requirements,the size of network slice is adjusted dynamically to guarantee resource utilization in VN reconfiguration.Additionally,load balancing factors are designed to make traffic balanced.Simulation results show that dynamic slicing mechanism not only saves 22%and 31%cost than static slicing mechanism with extending shortest path(SS_ESP)and dynamic slicing mechanism with embedding single path(DS_ESP),but also maintains high service acceptance rate.

Dynamic Distribution Model with Prime Granularity for Parallel Computing

Dynamic distribution model is one of the best schemes for parallel volume rendering. How- ever, in homogeneous cluster system.since the granularity is traditionally identical, all processors communicate almost simultaneously and computation load may lose balance. Due to problems above, a dynamic distribution model with prime granularity for parallel computing is presented. Granularities of each processor are relatively prime, and related theories are introduced. A high parallel performance can be achieved by minimizing network competition and using a load balancing strategy that ensures all processors finish almost simultaneously. Based on Master-Slave-Gleaner ( MSG) scheme, the parallel Splatting Algorithm for volume rendering is used to test the model on IBM Cluster 1350 system. The experimental results show that the model can bring a considerable improvement in performance, including computation efficiency, total execution time, speed, and load balancing.

Global Dynamic Load Balancing Based on Data-omitting （GDLBD）

E-mail, WWW, FTP, BT and QQlive, etc. axe used more and more universal because the advantage of Internet, but the data-omitting phenomenon is a headache problem. In this paper, we consider the problem of allocating a large number of independent, unequal-sized loads exchanged between servers and clients or between themselves when there are data-omitting, and we describe the dynamic load balancing problems by intro- ducing some parameters αij, we use an undirected graph to model the platform, where servers （CPU time, disk memory） can have different speeds of computation and communication. Because the number of loads is large, we focus on the question of determining the optimal dynamic load balancing scheduling strategy （splittable strategy） for each processor （the fraction of time spent computing and the fraction of time spent communication with each neighbor）. We show that finding the optimal dynamic load balancing state can be solved using a linear programming approach by adding more constrains and, thus, in polynomial time. And make the execute time minimization.

基于云计算的舰船通信网络动态负载均衡控制方法

针对舰船通信网络通信环境复杂、用户需求不确定、影响通信均衡性等问题、提出基于云计算的舰船通信网络动态负载均衡控制方法。利用Ryu控制器获取监控舰船通信网络状态,获取网络负载。创建云计算平台虚拟机,实现执行舰船通信网络动态负载均衡任务调度任务。选取最高迁移概率的物理机结果作为云计算平台虚拟机迁移结果,通过虚拟机的迁移与关闭操作,更新虚拟机的综合权值,实现舰船通信网络动态负载均衡控制。实验结果表明,该方法可实现舰船通信网络的动态负载均衡控制,负载均衡度高于0.7,可有效提升舰船通信网络的资源利用率。

基于回溯树的动态容重比平衡货物配载研究

针对零担物流货物装载率低下的现状,研究货物有限条件下多车多品种货物的货物配载问题,构建以最小化车辆数量、最大化车辆总容积和总载重量利用率为目标的多目标数学模型,考虑到单件货物的装载会对车辆以及车辆集合的剩余容积和剩余载重量产生影响,设计基于动态容重比平衡法和装载回溯树的启发式算法,对算法结果进行非支配排序,得到帕累托解集,计算帕累托解集内每个解的拥挤距离,并选择拥挤距离最大的解为最优装载方案。以算法计算精度和时间复杂度为标准,将所设计算法与NSGA-2、CPLEX算法进行对比分析,算例结果验证了算法的有效性。