scholarly journals Cloud computing based load balancing algorithm for erlang concurrent traffic

2020 ◽  
Vol 17 (2) ◽  
pp. 1109
Author(s):  
Chanintorn Jittawiriyanukoon
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Performance Metrics ◽  
Memory Load ◽  
Performance Parameters ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Concurrent Tasks ◽  
User Access ◽  
Load Balancing Algorithm

<p>The distribution of scheduler from user inquiries in the clouds is complex. In keeping up with the cloud computing environment and the inquirers, the clouds meet with some problematic load balancing complications as an improving load balancing tool induces the rigorous efficiency of the cloud based website’s user access. Overloaded or underloaded conditions originate processing catastrophe regarding the prolonged execution time, bandwidth hog, malfunction, and etc. Besides, to manipulate Erlang concurrent tasks is another skyward situation. Hence, the load balancing is obliged to exhaust all mentioned conditions. The proposed load balancing algorithm for Erlang concurrent tasks (those are and could also be autonomous and unstable.) on VMware workstations is introduced.  There are several load patterns within the clouds corresponding to CPU’s load (utilization), memory load (queue size), link capacity load (bandwidth), and so on. The proposed load balancing is to spot underloaded and overloaded conditions then stabilizes the weight amidst computing nodes. There are countless load balancing approaches in the cloud environment to examine performance parameters. A short outline of corresponding performance metrics in the review and their findings are presented. To investigate the fit efficiency of the proposed algorithm, the simulation is applied then results based on the proposed method are compared to the existing ones. The outcomes settle the weight balancing, outperform others when executing Erlang traffic, and are catered in the context.</p>

Sharing VM Resources With Using Prediction of Future User Requests for an Efficient Load Balancing in Cloud Computing Environment

2021 ◽  
Vol 13 (2) ◽  
pp. 37-64
Author(s):  
Mousa Elrotub ◽  
Ahmed Bali ◽  
Abdelouahed Gherbi
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Performance Metrics ◽  
Virtual Machines ◽  
Machine Learning Techniques ◽  
Data Repository ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Learning Techniques

The problem of balancing user requests in cloud computing is becoming more serious due to the variation of workloads. Load balancing and allocation processes still need more optimizing methodologies and models to improve performance and increase the quality of service. This article describes a solution to balance user workload efficiently by proposing a model that allows each virtual machine (VM) to maximize the serving number of requests based on its capacity. The model measures VMs' capacity as a percentage and maps groups of user requests to appropriate active virtual machines. Finding the expected patterns from a big data repository, such as log data, and using some machine learning techniques can make the prediction more efficiently. The work is implemented and evaluated using some performance metrics, and the results are compared with other research. The evaluation shows the efficiency of the proposed approach in distributing user workload and improving results.

scholarly journals A binary Bird Swarm Optimization based load balancing algorithm for cloud computing environment

2021 ◽  
Vol 11 (1) ◽  
pp. 146-160
Author(s):  
Kaushik Mishra ◽  
Santosh Kumar Majhi
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Service Providers ◽  
Scheduling Algorithm ◽  
Simulation Environment ◽  
Computing Environment ◽  
Swarm Optimization ◽  
Cloud Computing Environment ◽  
Np Hard Problem ◽  
Load Balancing Algorithm

Abstract Task scheduling and load balancing are a concern for service providers in the cloud computing environment. The problem of scheduling tasks and balancing loads in a cloud is categorized under an NP-hard problem. Thus, it needs an efficient load scheduling algorithm that not only allocates the tasks onto appropriate VMs but also maintains the trade-off amidst VMs. It should keep an equilibrium among VMs in a way that reduces the makespan while maximizing the utilization of resources and throughput. In response to it, the authors propose a load balancing algorithm inspired by the mimicking behavior of a flock of birds, which is called the Bird Swarm Optimization Load Balancing (BSO-LB) algorithm that considers tasks as birds and VMs as destination food patches. In the considered cloud simulation environment, tasks are assumed to be independent and non-preemptive. To evaluate the efficacy of the proposed algorithm under real workloads, the authors consider a dataset (GoCJ) logged by Goggle in 2018 for the execution of cloudlets. The proposed algorithm aims to enhance the overall system performance by reducing response time and keeping the whole system balanced. The authors have integrated the binary variant of the BSO algorithm with the load balancing method. The proposed technique is analyzed and compared with other existing load balancing algorithms such as MAX-MIN, RASA, Improved PSO, and other scheduling algorithms as FCFS, SJF, and RR. The experimental results show that the proposed method outperforms when being compared with the different algorithms mentioned above. It is noteworthy that the proposed approach illustrates an improvement in resource utilization and reduces the makespan of tasks.

scholarly journals A Proposed Load Balancing Algorithm Over Cloud Computing (Balanced Throttled)

2021 ◽  
Vol 10 (2) ◽  
pp. 28-33
Author(s):  
Shereen Yousef Mohamed ◽  
◽  
Mohamed Hamed N. Taha ◽  
Hesham N. Elmahdy ◽  
Hany Harb ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Static Load ◽  
Dynamic Load Balancing ◽  
System State ◽  
Computing Environment ◽  
Virtual Machine Migration ◽  
Cloud Computing Environment ◽  
Load Balancing Algorithm

Cloud computing refers to the services and applications that are accessible throughout the world from data centers. All services and applications are available online. Virtual machine migration is an important part of virtualization which is considered as essential part in cloud computing environment. Virtual Machine Migration means transferring a running Virtual Machine with all its applications and the operating system state as it is to target destination machine where it continues to run as if nothing happened. It makes balancing between servers. This improves the performance by redistributing the workload among available servers. There are many algorithms of load balancing classified into two types: static load balancing algorithms and dynamic load balancing algorithms. This paper presents the algorithm (Balanced Throttled Load Balancing Algorithm- BTLB). It compares the results of the BTLB with round robin algorithm, AMLB algorithm and throttled load balancing algorithm. The results of these four algorithms would be presented in this paper. The proposed algorithm shows the improvement in response time (75 µs). Cloud analyst simulator is used to evaluate the results. BTLB was developed and tested using Java.

scholarly journals Issues and Challenges of Load Balancing Algorithm in Cloud Computing Environment

2017 ◽  
Vol 10 (25) ◽  
pp. 1-12 ◽  
Author(s):  
Mahfooz Alam ◽  
Zaki Ahmad Khan ◽  
◽  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Load Balancing Algorithm

scholarly journals A Study on Particle Swarm based Load Balancing Algorithms in Cloud Computing

IJOSTHE ◽  
2018 ◽  
Vol 5 (1) ◽  
pp. 5
Author(s):  
Rachna Yadav ◽  
Mayank Namdev
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Large Scale ◽  
Particle Swarm ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Significant Area ◽  
Load Balancing Algorithm ◽  
New Applications ◽  
Cloud User

Cloud computing is a new and innovative perspective for large scale parallel and distributed computing. The dependence of user or load on the cloud is growing enormously with the enlargement of new applications. Load balancing is a significant area of cloud computing environment which ensures that all connected devices or processors carry out same amount of work in equal time. With an aim to make cloud resources and services accessible to the cloud user easily and conveniently, different algorithms and models for load balancing in cloud computing is being developed. There are so many algorithms are available for proper load balancing but in this paper particle swarm based algorithm is focused that can balance the load in cloud computing so that resources are easily available for users. This paper aims to develop an efficient load balancing algorithm using particle swarm based to minimize performance parameters like make span, latency, total execution time.

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