scholarly journals Proposed Load Balancing Algorithm to Reduce Response Time and Processing Time on Cloud Computing

2018 ◽  
Vol 10 (3) ◽  
pp. 87-98 ◽  
Author(s):  
Nguyen Xuan Phi ◽  
Cao Trung Tin ◽  
Luu Nguyen Ky Thu ◽  
Tran Cong Hung
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Processing Time ◽  
Load Balancing Algorithm

scholarly journals Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

2022 ◽  
Vol 12 (2) ◽  
pp. 2087
Author(s):  
Noha G. Elnagar ◽  
Ghada F. Elkabbany ◽  
Amr A. Al-Awamry ◽  
Mohamed B. Abdelhalim
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Processing Time ◽  
Virtual Machines ◽  
Active Monitoring ◽  
Load Fluctuation ◽  
Load Balancing Algorithm ◽  
And Performance ◽  
Load Balancer

<span lang="EN-US">Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms.</span>

scholarly journals Cloudlet Networks Performance Analysis and Improvement

2017 ◽  
Vol 3 (2) ◽  
pp. 22
Author(s):  
Jameela Abdulla Hassan ◽  
Fahad Al-Dosari
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Processing Time ◽  
Mobile Cloud Computing ◽  
Round Robin ◽  
Share Data ◽  
Index Terms ◽  
Load Balancing Algorithm ◽  
And Storage

Abstract— Cloud computing is a Participation in the process and storage operations across distant servers that are shared by many organizations and users and thus be transferred from an application to a service. The organization can share data over the Internet and user can pay only for the resources that will be used only. While cloud computing has disadvantages, there are some advantages for cloudlets have over cloud computing which include: lower network latency and users having full ownership of the data shared. When the need of data to be stored in the servers grows quickly, the workload in every resource will grow too. So, we need a load balancing algorithm and the load balancing is important issue in the cloud environment. Load balancing defined as a technique that divides the extra load equally across all the resources to ensure that no one resource overloaded. . So the performance of the cloud can be improved by having an excellent load balancing strategy. For that we will discuss the existing load balancing algorithms in cloud computing and propose algorithm to improve round robin algorithm by CloudAnalyst simulator  based on a factor of  response time and processing time  and the proposed algorithm was found to be best in response time and processing time when we compare it with round robin algorithms.   Index Terms— Cloud Computing, CloudAnalyst, Load Balance, Mobile Cloud Computing, Cloudlet Networks.

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.

scholarly journals Proposing a load balancing algorithm with an integrative approach to reduce response time and service process time in data centers

2019 ◽  
Vol 16 (4) ◽  
pp. 627-637
Author(s):  
Sanaz Hosseinzadeh Sabeti ◽  
Maryam Mollabgher
Keyword(s):  
Load Balancing ◽  
Data Processing ◽  
Response Time ◽  
Virtual Machine ◽  
Data Center ◽  
Processing Time ◽  
Data Centers ◽  
Virtual Machines ◽  
Overall Response ◽  
Load Balancing Algorithm

Goal: Load balancing policies often map workloads on virtual machines, and are being sought to achieve their goals by creating an almost equal level of workload on any virtual machine. In this research, a hybrid load balancing algorithm is proposed with the aim of reducing response time and processing time. Design / Methodology / Approach: The proposed algorithm performs load balancing using a table including the status indicators of virtual machines and the task list allocated to each virtual machine. The evaluation results of response time and processing time in data centers from four algorithms, ESCE, Throttled, Round Robin and the proposed algorithm is done. Results: The overall response time and data processing time in the proposed algorithm data center are shorter than other algorithms and improve the response time and data processing time in the data center. The results of the overall response time for all algorithms show that the response time of the proposed algorithm is 12.28%, compared to the Round Robin algorithm, 9.1% compared to the Throttled algorithm, and 4.86% of the ESCE algorithm. Limitations of the investigation: Due to time and technical limitations, load balancing has not been achieved with more goals, such as lowering costs and increasing productivity. Practical implications: The implementation of a hybrid load factor policy can improve the response time and processing time. The use of load balancing will cause the traffic load between virtual machines to be properly distributed and prevent bottlenecks. This will be effective in increasing customer responsiveness. And finally, improving response time increases the satisfaction of cloud users and increases the productivity of computing resources. Originality/Value: This research can be effective in optimizing the existing algorithms and will take a step towards further research in this regard.

Enhanced active VM load balancing algorithm using fuzzy logic and K-means clustering

2021 ◽  
Vol 17 (1) ◽  
pp. 59-82
Author(s):  
Mostefa Hamdani ◽  
Youcef Aklouf
Keyword(s):  
Cloud Computing ◽  
Fuzzy Logic ◽  
Load Balancing ◽  
Response Time ◽  
Data Center ◽  
Service Rate ◽  
Dynamic Allocation ◽  
Use Of Resources ◽  
Cloud Computing System ◽  
Load Balancing Algorithm

With the rapid development of data and IT technology, cloud computing is gaining more and more attention, and many users are attracted to this paradigm because of the reduction in cost and the dynamic allocation of resources. Load balancing is one of the main challenges in cloud computing system. It redistributes workloads across computing nodes within cloud to minimize computation time, and to improve the use of resources. This paper proposes an enhanced ‘Active VM load balancing algorithm’ based on fuzzy logic and k-means clustering to reduce the data center transfer cost, the total virtual machine cost, the data center processing time and the response time. The proposed method is realized using Java and CloudAnalyst Simulator. Besides, we have compared the proposed algorithm with other task scheduling approaches such as Round Robin algorithm, Throttled algorithm, Equally Spread Current Execution Load algorithm, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). As a result, the proposed algorithm performs better in terms of service rate and response time.

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.

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