A Quadratic Equilibrium Entropy Based Virtual Machine Load Balance Evaluation Algorithm

Load Balancing is an important factor handling resource during running and execution time in real time applications. Virtual machines are used for dynamically access and share the resources. As per current scenario cloud computing is played major for storage, resource accessing, resource pooling and internet based service offering. Usage of cloud computing services is dynamically increased such as online shopping, education, ticketing, etc. Many users can use the cloud resources and load balancing is used for adjusting the virtual machine and balance the node. Our proposed virtualized genetic algorithms are to provide balanced virtual machine services in Hybrid cloud. The proposed algorithm and experiments are implemented by using Cloud simulator. In this paper the experiments are done with cloud computing models, Virtual Machine allocation, load balancing and simulations. Also compare the results using response time, throughput and turnaround time using cloud sim. The accuracy can be compared with existing load balancing techniques.

Cloud computing is a recent computer technology that has virtualized infrastructure to offer secure and reliable services to users in a complex environment. The main advantage of the cloud is its scalability. Vast scalability is possible because of load balancing. Due to the advancement of the cloud, huge numbers of service provisioning requests are generated from users. Cloud needs a better and more efficient load-balancing mechanism to handle all the user’s requests. This paper proposes the principal component gradient round robin load balancing (PCGRLB) technique to balance the workload in a cloud server for handling a huge workload within a minimum time. The PCGRLB technique performs two processes: Virtual machine analysis and load balancing. The PCGRLB technique is evaluated on factors such as throughput, makespan, and response time concerning several tasks. The results show that the PCGRLB technique offers an efficient solution in achieving higher load balance, efficiency, throughput, and minimized makespan and response time than the conventional techniques.

SummarySoftware‐defined networking is a promising networking paradigm for achieving programmability and centralized control in communication networks. These features simplify network management and enable innovation in network applications and services such as routing, virtual machine migration, load balancing, security, access control, and traffic engineering. The routing application can be optimized for power efficiency by routing flows and coalescing them such that the least number of links is activated with the lowest link rates. However, in practice, flow coalescing can generally overflow the flow tables, which are implemented in a size‐limited and power‐hungry ternary content addressable memory (TCAM). In this paper, a set of practical constraints is imposed to the software‐defined networking routing problem, namely, size‐limited flow table and discrete link rate constraints, to ensure applicability in real networks. Because the problem is NP‐hard and difficult to approximate, a low‐complexity particle swarm optimization–based and power‐efficient routing (PSOPR) heuristic is proposed. Performance evaluation results revealed that PSOPR achieves more than 90% of the optimal network power consumption while requiring only 0.0045% to 0.9% of the optimal computation time in real‐network topologies. In addition, PSOPR generates shorter routes than the optimal routes generated by CPLEX.

Load balancing scheduling algorithms for virtual computing laboratories in a Desktop-As-A-Service Cloud Computing Services

This paper addresses trade off problem when cloud providers attempt to balance their own objective and users quality of service evenly. Acquiring fast processing time for some specific tasks workload most likely will satisfy users. In response, cloud providers need to accommodate it by provisioning good tasks allocation policy. Cloud computing as business view, on the other hand, allows provider to manage their physical resources smartly in order to save the energy consumption for their own merit. The approach is beneficial for private Cloud provider in which the privacy can be maintained while still leveraging the potential of public Cloud. This work would like to find a middle point for the good of both. In this paper, virtual and physical machine load balancing scheme is proposed to satisfy both side. This paper contains two different allocation policies in two different level view: Tasks scheduling view and VMs remapping view.

Cloud computing consists of a cluster of computing resources that are delivered over a network, which is accomplished by utilizing virtualization technologies to consolidate and allocate resources suitable for various different software applications. Therefore, an efficient task scheduling in the cloud would be required to improve the performance of the cloud. In this paper, implementation of a model that seeks to improve load balancing algorithm for virtual machine load balancing was performed using simulations. A method by which average burst time was used as the time quantum for the round robin load balancing algorithm to achieve more effective time sharing. Results obtained from the simulations along with performance evaluation carried out shows response time and data center processing time achieved using the improved model is slightly minimal compared to the other algorithms. This shows more effective load balancing by achieving a better overall throughput.

In order to solve the problems of unbalanced load, slow convergence speed and low utilization of virtual machine resources existing in the previous task scheduling optimization strategies, this paper proposes a task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing. Firstly, based on the principle of cloud computing task scheduling, a scheduling model using improved ant colony algorithm is proposed to avoid the optimization strategy falling into local optimization. Then, task scheduling satisfaction function is constructed by combining the three objectives of the shortest waiting time, the degree of resource load balance and the cost of task completion to search the optimal solution of task scheduling. Finally, the reward and punishment coefficient is introduced to optimize the pheromone updating rules of ant colony algorithm, which speeds up the solution speed. Besides, we use dynamic update of volatility coefficient to optimize overall performance of this strategy, and introduce virtual machine load weight coefficient in the process of local pheromone updating, so as to ensure the load balance of virtual machine. The feasibility of our algorithm is analyzed and demonstrated by experiments with Cloudsim. The experimental results show that the proposed algorithm has the fastest convergence speed, the shortest completion time, the most balanced load and the highest utilization rate of virtual machine resources compared with other methods. Therefore, our proposed task scheduling optimization strategy has the best performance.

Current virtual server-based load balancing schemes for DHT have been shown to be able to achieve excellent load balancing effectiveness. However, they face two important issues. They suffer from problems of incurring extremely high overheads, and inducing severe inconsistency in DHT routing state. We present two fundamental components, virtual server management and active stabilization, whose inclusion into these schemes essentially eliminates these problems. As a result, these schemes not only incur overheads comparable to non-virtual server-based systems, but also achieve better query performance.

Cloud simulation makes contribution to simulation system by providing on-demand, everywhere simulation services. Load Balancing is a fatal issue for Cloud simulation for the reason that an overloaded node may cause slowdown of the whole system. To acquire high performance of Cloud simulation, this paper proposes a two-stage load balancing method. The first stage aims to virtual machine (VM) load balancing by using a heuristic algorithm to allocate Federates to VMs before the simulation start. In the second stage, the VMs are dynamically migrated between physical machines (PMs) to keep the load balancing of PMs during the simulation. Experiments show that the two-stage load balancing method can significantly improve the efficiency of the HLA system on Cloud simulation platform (CSP).

The efficiency of task scheduling under cloud computing is related to the effectiveness of users. Aiming at the problems of long scheduling time, high cost consumption, and large virtual machine load in cloud computing task scheduling, an improved scheduling efficiency algorithm (called the improved whale optimization algorithm, referred to as IWC) is proposed. Firstly, a cloud computing task scheduling and distribution model with time, cost, and virtual machines as the main factors is constructed. Secondly, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; in order to better find the optimal individual, we use the inertial weight strategy for the whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence; we use the add operator and delete operator to screen individuals after each iteration which is completed and updated to improve the quality of understanding. In the simulation experiment, IWC was compared with the ant colony algorithm, particle swarm algorithm, and whale optimization algorithm under a different number of tasks. The results showed that the IWC algorithm has good results in terms of task scheduling time, scheduling cost, and virtual machine. The application is in cloud computing task scheduling.

Enterprise marketing information is stored in the form of message files or databases, which is increasing exponentially. Under the conditions of information failure, the backup is not timely, resulting in a decrease in storage efficiency. Therefore, research on the private cloud storage method of marketing information is performed based on grid technology. Moreover, grid basic services are provided through grid system management nodes and grid public service nodes, and local resource servers and enterprise grid application services are used to provide grid application services. Based on each server node in the model, the virtual machine usage is adjusted through the load forecasting process, and the mirroring backup process is applied to restore the virtual machine data under fault conditions. In addition, the model uses the longest path matching method to manage marketing information metadata based on metadata logical search and location metadata sub-table. Experimental results show that the load of different virtual machines in the marketing information storage process is less than 40%, and the load of each virtual machine is basically the same, indicating that the method proposed in the paper can enhance the balance of blocking in the information storage process and improve storage efficiency.

A network testbed with high flexibility plays an important role in testing and evaluating new network technology. Both researchers and engineers always need a network testbed which can customize network experiment on demand. Some of the existing cloud computing platforms lack network customization while some network experimental platform can not provide convenient virtual machine management functions. In this paper, we propose a preliminary implementation of the elastic customized cloud network (ECCN) platform based on Openstack project which provides cloud computing management and supports the Openflow protocol. This testbed can meet the needs of a rapid customized deployment of network resources like virtual machines, load balancers and routers in data center. Simultaneously, it provides the capability to generate and manage the customized network topology and an experimental environment where traditional network can interwork with Software Defined Network (SDN). Experiment results show that the ECCN testbed can help users deploy their own network experiment rapidly and easily with a friendly operation, as well as a flexible network design based on the highly customized experiment. In addition, ECCN guarantees the network isolation between different users based on reuse and sharing of the physical resources.

Mobile cloud computing is an emerging technology in recent years. This technology reduces battery consumption and execution time by executing mobile applications in remote cloud server. The virtual machine (VM) load balancing among cloudlets in MCC improves the performance of application in terms of response time. Genetic algorithm (GA) is popular for providing optimal solution for load balancing problems. GA can perform well in both homogeneous and heterogeneous environments. In this paper, the authors consider multi-objective genetic algorithm for load balancing in MCC (MOGALMCC) environment. In MOGALMCC, they consider distance, bandwidth, memory, and cloudlet server load to find optimal cloudlet before scheduling VM in another cloudlet. The framework MOGALMCC aims to improve response time as well as minimizes VM failure rate. The experiment result shows that proposed model performed well by reducing execution time and task waiting time at server.

<span>Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches.</span>

Aiming at the current energy consumption problems of cloud data centers and the running performance of virtual machines, an energy-aware and load balancing based dynamic migration strategy (EALB) is proposed. This strategy selects the host to be migrated, the virtual machine to be migrated, and the target host and completes the migration of the virtual machine, reducing the number of running servers. This strategy uses the gray prediction algorithm to predict the load rate of the hosts. After the migration is completed, the load of each host can be balanced. Experiments prove that the EALB strategy can obviously reduce the energy consumption of the cloud data center and balance the load among the servers.