Improved Load Balancing Algorithm Research Articles

Big data cloud platform server load balancing algorithm based on improved chaotic partition algorithm

Due to the heterogeneity of big data cloud platform and the uneven distribution of data between cloud servers, when cloud server cluster processes a large number of tasks, the node load is often uneven. To solve this problem, a load balancing algorithm based on improved chaotic partition algorithm for big data cloud platform server is proposed. According to the statistics of the average resource consumption of various services provided by the cluster, combined with the running time and resource occupation of tasks on the server, the total remaining task load of the server at a certain time point is predicted, so as to obtain the actual task load status of the node, and correct the task load in time. Experiments show that the load balancing algorithm of big data cloud platform server based on improved chaotic partition algorithm can effectively balance the load of multi task heterogeneous cloud servers, and has high feasibility, Then, based on this improved load balancing algorithm, we can also extend it to the application of multi-objective algorithm, such as robot path planning, target allocation scheduling algorithm and so on. There will be similar and reasonable performance compared with the same period last year.

WITHDRAWN: Optimized Adaptive load balancing algorithm in cloud computing

Abstract As we know that Internet speed creation in cloud computing clearly positions the scheduling algorithm in today's lifestyles very critically. In the cloud world, the right load balancing technology is essential to execute the system and manage resources. In a dispensed system, sources with varying configurations and capabilities are very difficult to obtain. In order to optimise certain final outcomes, the load balance machine will pose the obstacle to the resource that, based solely on certain specific goals, takes on essential targets. The history of cloud activities simplifies and demonstrates the scheduling system, optimization problems focused mostly on QoS in cloud architecture. At Level-1, the “planning algorithm” based on the Service Level Agreement (SLA) contains full information about the assignments and distribution to the cluster. In Level 2, the monitoring strategy for idle servers balance the demand on separate servers in each cluster. Most significantly, the current load balancing strategies will be remembered and an improved load balancing algorithm will be increased that will mainly support the cloud world. This text delineates a comparative look at the cloud computing world to classify these scheduling algorithms.

Performance Analysis of an Improved Load Balancing Algorithm in Cloud Computing

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.

A NOVEL APPROACH OF JOB ALLOCATION USING MULTIPLE PARAMETERS IN IN CLOUD ENVIRONMENT

Cloud computing is Internet ("cloud") based development and use of computer technology ("computing"). It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. This research deals with the balancing of work load in cloud environment. Load balancing is one of the essential factors to enhance the working performance of the cloud service provider. Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. We propose an improved load balancing algorithm for job scheduling in the Grid environment. Hence, in this research work, a multi-objective load balancing algorithm has been proposed to avoid deadlocks and to provide proper utilization of all the virtual machines (VMs) while processing the requests received from the users by VM classification. The capacity of virtual machine is computed based on multiple parameters like MIPS, RAM and bandwidth. Heterogeneous virtual machines of different MIPS and processing power in multiple data centers with different hosts have been created in cloud simulator. The VM’s are divided into 2 clusters using K-Means clustering mechanism in terms of processor MIPS, memory and bandwidth. The cloudlets are divided into two categories like High QOS and Low QOS based on the instruction size. The cloudlet whose task size is greater than the threshold value will enter into High QOS and cloudlet whose task size is lesser than the threshold value will enter into Low QOS. Submit the job of the user to the datacenter broker. The job of the user is submitted to the broker and it will first find the suitable VM according to the requirements of the cloudlet and will match VM depending upon its availability. Multiple parameters have been evaluated like waiting time, turnaround time, execution time and processing cost. This modified algorithm has an edge over the original approach in which each cloudlet build their own individual result set and it is later on built into a complete solution.

Load Balancing in Cloud-based Flow Security System

In the context of load balancing [1], Lu et al. proposed a novel class of algorithms called Join-Idle-Queue (JIQ) for distributed load balancing in large systems, where a group of dispatchers distribute jobs to a cluster of parallel servers. Each dispatcher maintains a queue called Idle-Queue for idle servers. When a job arrives to a dispatcher at random, the dispatcher sends it to a server which in the head of the Idle-Queue, or to a random server if the Idle-Queue is empty. Meanwhile conversely, when a server has no jobs, it actively requests to be placed on an Idle-Queue. Although this algorithm was shown to be quite effective [2,3], it is defective in some respects. This paper presents several new improvements, which makes the algorithm more effective, especially in Cloud-based Flow Security System (CFS). These improvements focus primarily on these aspects, such as the definition of idle server, data concurrency while choosing an Idle-Queue, etc. The experimental results show that the improved load balancing algorithm can improve the utilization of resources in a certain extent.

AN ENHANCED TASK ALLOCATION STRATEGY IN CLOUD ENVIRONMENT

Cloud computing is a vigorous technology by which a user can get software, application, operating system and hardware as a service without actually possessing it and paying only according to the usage. Cloud Computing is a hot topic of research for the researchers these days. With the rapid growth of Interne technology cloud computing have become main source of computing for small as well big IT companies. In the cloud computing milieu the cloud data centers and the users of the cloud-computing are globally situated, therefore it is a big challenge for cloud data centers to efficiently handle the requests which are coming from millions of users and service them in an efficient manner. Load balancing ensures that no single node will be overloaded and used to distribute workload among multiple nodes. It helps to improve system performance and proper utilization of resources. We propose an improved load balancing algorithm for job scheduling in the cloud environment using K-Means clustering of cloudlets and virtual machines in the cloud environment. All the cloudlets given by the user are divided into 3 clusters depending upon client’s priority, cost and instruction length of the cloudlet. The virtual machines inside the datacenter hosts are also grouped into multiple clusters depending upon virtual machine capacity in terms of processor, memory, and bandwidth. Sorting is applied at both the ends to reduce the latency. Multiple number of experiments have been conducted by taking different configurations of cloudlets and virtual machine. Various parameters like waiting time, execution time, turnaround time and the usage cost have been computed inside the cloudsim environment to demonstrate the results. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.

ENHANCED DYNAMIC RESOURCE ALLOCATION SCHEME BASED ON PACKAGE LEVEL ACCESS IN CLOUD COMPUTING

Cloud computing is distributed computing, storing, sharing and accessing data over the Internet. It provides a pool of shared resources to the users available on the basis of pay as you go service that means users pay only for those services which are used by him according to their access times. This research work deals with the balancing of work load in cloud environment. Load balancing is one of the essential factors to enhance the working performance of the cloud service provider. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. We propose an improved load balancing algorithm for job scheduling in the cloud environment using load distribution table in which the current status, current package, VM Capacity and the number of cloudlets submitted to each and every virtual machine will be stored. Submit the job of the user to the datacenter broker. Data center broker will first find the suitable Vm according to the requirements of the cloudlet and will match and find the most suitable Vm according to its availability or the machine with least load in the distribution table. Multiple number of experiments have been conducted by taking different configurations of cloudlets and virtual machine. Various parameters like waiting time, execution time, turnaround time and the usage cost have been computed inside the cloudsim environment to demonstrate the results. The main contributions of the research work is to balance the entire system load while trying to minimize the make span of a given set of jobs. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.

ALLOCATION OF HETEROGENOUS CLOUDLETS ON PRIORITY BASIS IN CLOUD ENVIRONMENT

Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. In the natural environment, the cloudlets will be processed in the FIFO (First in First Out approach). We propose an improved load balancing algorithm for job scheduling in the Grid environment. Hence, in this research work, various types of leases have been assigned to the cloudlets like cancellable, suspendable and non-preemtable. The leases have been assigned on the basis of cost assigned to them and the requirement specified by the user. The datacenter broker will receive the list of all the virtual machines and will categorize them into two classes i.e. Class A and Class B. Class A will have high end virtual machines and will process the non-preemptable cloudlets. Class B will contain the low end virtual machines and will process the suspendable and cancellable cloudlets. The machines in each class will be further sorted in descending order according to their MIPS. Multiple parameters have been evaluated like waiting time, turnaround time, execution time and processing cost. Further, this research also provides the anticipated results with the implementation of the proposed algorithm. In the cloud storage, load balancing is a key issue. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. The main contributions of the research work are to balance the entire system load while trying to minimize the make span of a given set of jobs. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.

Load Balancing Algorithm for Web Server Based on Weighted Minimal Connections

We have analyzed the clustering technology of Web server and studid the load balancing algorithm, due the lack of the existing load balancing algorithm can not accurately reflecting the size of the server load and manually setting the weight and other aspects, in this paper ,we propose an improved weighted minimum join Improved Load Balancing Algorithm for Web server cluster. On the one hand,The algorithm takes into account server utilization, memory utilization and network bandwidth mutilation and other factors, combined with web server’s own characteristics,Accouding to the real-time load of the server to dynamically change the size of the weight, and on the other hand, assigns the new connection request according to the weighted minimum connection number algorithm. Through the use of simulation software OPNET, the algorithm not only reduces server latency, reduces HTTP response time,and can effectively improve the load balancing efficiency.

A NOVEL APPROACH OF OPTIMIZING PERFORMANCE USING K-MEANS CLUSTERING IN CLOUD COMPUTING

Cloud computing is distributed computing, storing, sharing and accessing data over the Internet. It provides a pool of shared resources to the users available on the basis of pay as you go service that means users pay only for those services which are used by him according to their access times. Load balancing ensures that no single node will be overloaded and used to distribute workload among multiple nodes. It helps to improve system performance and proper utilization of resources. We propose an improved load balancing algorithm for job scheduling in the cloud environment using K-Means clustering of cloudlets and virtual machines in the cloud environment. All the cloudlets given by the user are divided into 3 clusters depending upon client’s priority, cost and instruction length of the cloudlet. The virtual machines inside the datacenter hosts are also grouped into multiple clusters depending upon virtual machine capacity in terms of processor, memory, and bandwidth. Sorting is applied at both the ends to reduce the latency. Multiple number of experiments have been conducted by taking different configurations of cloudlets and virtual machine. Various parameters like waiting time, execution time, turnaround time and the usage cost have been computed inside the cloudsim environment to demonstrate the results. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.Â

An efficient load balancing approach for N-hierarchical web server cluster

Aiming at the load imbalance and poor scalability in single-tier Web server clusters, an efficient load balancing approach is proposed for constructing an N-hierarchical (multi-tier) Web server cluster. In each layer, multiple load balancers are set to receive the user requests simultaneously, and different load balancing algorithms are used to construct the high-scalable Web cluster system. At the same time, an improved load balancing algorithm is proposed, which can dynamically calculate weights according to the utilization of the server resources, and reasonably distribute the loads for each server according to the load status of the servers. The experimental results show that the proposed approach can greatly decrease the load imbalance among the Web servers and reduce the response time of the entire Web cluster system.

Analyzing and Improving Load Balancing Algorithm of MooseFS

Cloud storage is the development direction of data storage nowadays, and MooseFS provides a good solution for cloud storage. But as a kind of typical distributed file system, the problem of load balancing among chunk servers affects the use of MooseFS. Though MooseFS has provided certain load balancing capability, but it takes into account only the consumed space, and which causes the chunk servers with larger space were overburdened. In this study, we analyzed the load balancing algorithm among chunk servers in MooseFS, and proposed an improved load balancing algorithm. Through the experiment and comparative analysis, the improved algorithm enhances the load balancing performance of MooseFS obviously.

An Improved Load Balancing Algorithm of Multi-TPM

This paper have accomplished Trusted Platform Module test and have succeeded in porting TPM Emulator from Linux to Windows. According to the TLBA Algorithm, this paper proposes an improved load balancing algorithm of TPM (A-TLBA). A-TLBA makes further optimization and perfection to TLBA. It reflects in the choice of the threshold and considered the heterogeneous environment. A-TLBA improves the efficiency of TPM, reduces the waste of resources effectively and increases the robustness of the algorithm.