scholarly journals Performance evaluation of proposed load balancing algorithm with unstable concurrent programs

2019 ◽  
Vol 14 (3) ◽  
pp. 1452
Author(s):  
Chanintorn Jittawiriyanukoon
Keyword(s):  
Performance Evaluation ◽  
Load Balancing ◽  
Queue Length ◽  
Performance Metrics ◽  
The Internet ◽  
Concurrent Programs ◽  
Computing Environment ◽  
Mean Queue Length ◽  
Load Balancing Algorithm ◽  
Task Partition

<span>IoT is the succeeding cohort of the digital computing environment. A swift progression in the IoT deployment and its applications are on the rise. Improving load balancing mechanisms induces healthier performance of the internet based computing as higher number of users can be comfortable. Implementing full services for tasks with unstable concurrency is an uphill process. One of the encounters allied with this administration is the task partition among the applications, regularly referred as concurrent programs. Through load balancing not only resources are equally utilized but also concurrent job’s response time can be promoted. Therefore, in this paper the widely used load balancing algorithms are investigated and yet the proposed algorithm is introduced. Simulation is employed in order to compare the performance metrics such as mean queue length, utilization and throughput between the recommended and existing algorithms. The proposed algorithm confirms the load balancing and outperforms when processing unstable concurrent programs.</span>

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>

scholarly journals Resource Allocation in Cloud Computing using Heuristic Load Balancing Algorithm

2020 ◽  
Author(s):  
Anup Shrestha ◽  
Suriayati Chuprat ◽  
Nandini Mukherjee
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
Completion Time ◽  
Virtual Machines ◽  
It Services ◽  
Computing Environment ◽  
Load Balancing Algorithm ◽  
Better Than

Cloud computing is becoming more popular, unlike conventional computing, due to its added advantages. This is because it offers utility-based services to its subscribers upon their demand. Furthermore, this computing environment provides IT services to its users where they pay for every use. However, the increasing number of tasks requires virtual machines for them to be accomplished quickly. Load balancing a critical concern in cloud computing due to the massive increase in users' numbers. This paper proposes the best heuristic load balancing algorithm that will schedule a strategy for resource allocation that will minimize make span (completion time) in any technology that involves use cloud computing. The proposed algorithm performs better than other load balancing algorithms.

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.

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