The Multi-Server Load Balancing Systems Research in Large-Website Construction

2015 ◽  
Vol 713-715 ◽  
pp. 2378-2381 ◽  
Author(s):  
Ming Hai Yao ◽  
Na Wang ◽  
Jin Shan Li
Keyword(s):  
Load Balancing ◽  
Response Time ◽  
Cluster Method ◽  
System Response ◽  
Oracle Database ◽  
Server Cluster ◽  
Systems Research ◽  
Cpu Utilization ◽  
System Response Time ◽  
Load Balancing Algorithm

The load balancing method based on the Linux server cluster is proposed in large-website construction. The load balancing of server cluster is achieved by SSH framework, Oracle database and Ajax technologies. The load balancing of server cluster method can’t only improve performance, scalability, availability of system and reduce the execution time of multi-tasking but also eliminates network bottlenecks and improve the flexibility and reliability of the network. In order to verify the validity of the algorithm, large number of experimental data is used in the experiment. We propose a method of load balancing algorithm is compared with the traditional non-load-balancing algorithm for CPU utilization and system response time in the experiments. The experimental results show that the load balancing technology can reduce system response time and CPU utilization of server.

scholarly journals Load Balancing Mechanisms in Amazon Web Services using Meta Heuristic Rules

2019 ◽  
Vol 8 (2S11) ◽  
pp. 4071-4075
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Waiting Time ◽  
Virtual Machines ◽  
Machine Learning Techniques ◽  
System Response ◽  
Load Balancing Algorithm

Cloud computing is defined as the resource that can be delivered or accessed by the local host from the remote server via the internet. Cloud providers typically use a "pay-as-you-go" model. The evolution of cloud computing has led to the evolution of modern environment due to abundance and advancement of computing and communication infrastructure. During user request, and system response generation, an amount load will be assigned in the cloud computing, where it may be over or under load. Due to heavy load, power consumption and energy management problems are created, and it makes system failure and lead data loss. Though, an efficient load balancing method is compulsory to overcome all mentioned problems. The objective of this work is to develop a metaheuristic load balancing algorithm to migrate multi-server for load balancing and machine learning techniques is used to increase the cloud resource utilization and minimize the make-span time of the task. Using an unsupervised machine learning technique, it is possible to predict the correct response time and waiting time of the servers by getting the prior knowledge about the virtual machines and its clusters. And this work involves to calculate the accuracy rate of the Round-Robin load balancing algorithm and then compared it with a proposed algorithm. By this work, the response time and waiting time can be minimized and also it increases the resource utilization and minimize the make- span time of the task.

scholarly journals Analysis of work load by computer system response time in different type of VDT tasks

1985 ◽  
Vol 21 (4) ◽  
pp. 211-217 ◽  
Author(s):  
Akinori KOMATSUBARA ◽  
Yoshimi YOKOMIZO ◽  
Sakae YAMAMOTO ◽  
Kageyu NORO
Keyword(s):  
Response Time ◽  
Computer System ◽  
Work Load ◽  
System Response ◽  
System Response Time

Study on Network Load Balancing Algorithm Based on Server Cluster System

2012 ◽  
Vol 182-183 ◽  
pp. 1978-1981 ◽  
Author(s):  
Li Lan ◽  
Chu Huan Qi
Keyword(s):  
Load Balancing ◽  
Load Balance ◽  
Cluster System ◽  
Utilization Efficiency ◽  
Server Cluster ◽  
Use Of Resources ◽  
Network Load ◽  
System Resource ◽  
Network Load Balancing ◽  
Load Balancing Algorithm

The utilization efficiency of system resources is a key issue for cluster system while load balance is an important tool to realize the efficient use of resources. Based on server cluster system, this paper puts forwards an improved self-adaptive algorithm for network load balancing. Simulation results show that the algorithm can improve the utilization efficiency of system resource and reduce the server’s response time so as to achieve the request of real time when dealing with tasks and high availability of system.

The Impact of Computer Assistance on District Office Operations

1979 ◽  
Vol 23 (1) ◽  
pp. 586-590
Author(s):  
L. Dan Massey ◽  
Jerry T. Lawler
Keyword(s):  
Cost Effectiveness ◽  
Response Time ◽  
Capital Investment ◽  
System Response ◽  
Computer Assisted ◽  
Social Security Administration ◽  
Potential Cost ◽  
Computer Assistance ◽  
System Response Time ◽  
The Impact

To predict the potential cost effectiveness of computer assisted information processing in the District Offices (DOs) of the Social Security Administration (SSA), we developed a model of manpower allocation in present DO operations. We adapted existing SSA management data sources to quantify this baseline model. We also constructed a description of the changes in manpower requirements expected to occur on introduction of a hypothetical computer assisted system. We used analyses of user functional requirements to determine overall system capabilities. We used the results of user performance trials on a simulated system to quantify this change model. Significant reductions in manpower for processing of individual claims in the DO appear to be attainable. Computer system response time was identified as a controllable design characteristic with a strong effect on manpower requirements. Introduction of computer assistance to the DO could free manpower from mechanical clerical activities for application to more creative and professional functions. This manpower could be applied to expansion of SSA services and to improvement of DO service quality. An overall increase of 25% to 33% in workload processing capacity appears potentially attainable with computer assistance at current DO manpower levels. Suitable workstations might be based on use of individual personal computers or on sharing the resources of a single larger machine among a number of users. Consideration of relative costs and benefits suggests that the personal computer approach, while initially more costly, offers better control over system response time, which can have a major effect on system acceptance and cost effectiveness. It is likely that computer assistance for DOs will be deployed through workstations based on individual personal machines. Full recovery of capital investment can potentially be achieved in two years of actual operation.

A Proficient Approach for Load Balancing in Cloud Computing-Join Minimum Loaded Queue

2020 ◽  
Vol 11 (1) ◽  
pp. 12-36
Author(s):  
Minakshi Sharma ◽  
Rajneesh Kumar ◽  
Anurag Jain
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Cloud Service ◽  
Cloud Environment ◽  
Cloud Service Provider ◽  
Qos Parameters ◽  
Load Balancing Algorithm

Cloud load balancing is done to persist the services in the cloud environment along with quality of service (QoS) parameters. An efficient load balancing algorithm should be based on better optimization of these QoS parameters which results in efficient scheduling. Most of the load balancing algorithms which exist consider response time or resource utilization constraints but an efficient algorithm must consider both perspectives from the user side and cloud service provider side. This article presents a load balancing strategy that efficiently allocates tasks to virtualized resources to get maximum resource utilization in minimum response time. The proposed approach, join minimum loaded queue (JMLQ), is based on the existing join idle queue (JIQ) model that has been modified by replacing idle servers in the I-queues with servers having one task in execution list. The results of simulation in CloudSim verify that the proposed approach efficiently maximizes resource utilization by reducing the response time in comparison to its other variants.

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