scholarly journals DEVELOPMENT OF A VIRTUAL COMPUTING SYSTEM WITH SUPPORT OF LOAD BALANCING TO INCREASE CALCULATION EFFICIENCY

2020 ◽  
Vol 2 (№6 2020) ◽  
pp. 214-219
Author(s):  
A.V. Bogdanov ◽  
Kyaw Tkhurein ◽  
Zaya Kyaw ◽  
Sone Pyae
Keyword(s):  
Load Balancing ◽  
Computing System ◽  
Virtual Computing

A Dynamic Load Balancing Strategy with Adaptive Thresholds (DLBAT) for Parallel Computing System

2014 ◽  
Vol 5 (1) ◽  
pp. 54-69 ◽  
Author(s):  
Taj Alam ◽  
Zahid Raza
Keyword(s):  
Load Balancing ◽  
Dynamic Load ◽  
Computing System ◽  
Primary Objective ◽  
Dynamic Load Balancing ◽  
Np Hard ◽  
Threshold Values ◽  
Adaptive Thresholds ◽  
Hard Problems ◽  
The Mean

The primary objective of scheduling is to minimize the job execution time and maximize the resource utilization. Scheduling of ‘m' jobs to ‘n' resources with the objective to optimize the QoS parameters has been proven to be NP-hard problem. Two broad approaches that are defined for dealing with NP-hard problems are approximate and heuristic approach. In this paper, a centralized dynamic load balancing strategy using adaptive thresholds has been proposed for a multiprocessors system. The scheduler continuously monitors the load on the system and takes corrective measures as the load changes. The threshold values considered are adaptive in nature and are readjusted to suite the changing load on the system according to the mean of the available load. Effectively, the load is leveraged towards the mean, transferring only the appropriate number of jobs from heavily loaded nodes to lightly loaded nodes. In addition, the threshold values are designed in such a way that the scheduler avoids excessive load balancing. Therefore, the scheduler always ensures a uniform distribution of the load on the processing elements with dynamic load environment. Simulation study reveals the effectiveness of the model under various conditions.

The Virtual Computing Lab (VCL)

2014 ◽  
Vol 5 (2) ◽  
pp. 51-63 ◽  
Author(s):  
Andy Rindos ◽  
Mladen Vouk ◽  
Yaser Jararweh
Keyword(s):  
Cloud Computing ◽  
Computing System ◽  
Academic Community ◽  
State University ◽  
Related Services ◽  
Virtual Computing ◽  
Cloud Computing System ◽  
Self Service ◽  
Research And Education ◽  
Number Of Customers

In this paper, we describe the Virtual Computing Lab (VCL) with its main features and services. Also, we introduce the recent advances of the VCL system and its usage in research and education. The VCL is a cloud computing system that has been optimized for the educational services and research needs of the academic community. VCL is an open source cloud orchestration stack with a self-service portal that currently supports a large number of customers and commercial cloud, or cloud-related services and solutions. It was developed by NCSU with support from IBM Corporation. VCLs promise to support researchers and students in all academic levels to fulfill all their computing needs. In addition to supporting students and faculty members at NC State University and other UNC System universities, the NC VCL now also supports students at several NC community colleges. Also, we introduced cloud computing and service science related activities and achievements at Jordan University of Science and Technology.

scholarly journals An Efficient Novel Load Balancing Algorithm to Improve the Performance of the System in Cloud Environment

2019 ◽  
Vol 8 (S3) ◽  
pp. 105-108
Author(s):  
P. Neelima ◽  
A. Rama Mohan Reddy
Keyword(s):  
Load Balancing ◽  
Fitness Function ◽  
Dynamic Environment ◽  
Computing System ◽  
Dragonfly Algorithm ◽  
Main Challenge ◽  
Cloud Computing System ◽  
Load Balancing Algorithm ◽  
Load Balancer ◽  
Is Design

Distribution of workload in a balanced manner is a main challenge in cloud computing system. It distributes workload among multiple nodes, hence resources are properly utilized. This is an optimization problem and a good load balancer should be involved for this strategy to the types of tasks and dynamic environment. To overcome load balancing problem here a Novel Load balancing Algorithm is develop i.e. Dragonfly Algorithm is design and developed, to execute the entire task with shortest completion time and load balanced. Our algorithm will be presented with efficient solution representation, derivation of efficient fitness function (or multi-objective function) along with the usual Dragonfly operators. The performance of the algorithm will be analyzed based on the different evaluation measures. The algorithms like particle swarm optimization (PSO) and Genetic algorithm (GA) will be taken for the comparative analysis.

Ant colony optimization algorithm for load balancing in grid computing

2012 ◽  
Author(s):  
Ku Ruhana Ku-Mahamud ◽  
Aniza Mohamed Din
Keyword(s):  
Resource Management ◽  
Load Balancing ◽  
Grid Computing ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Computing System ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Grid Resource ◽  
Grid Resource Management

Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources. This research proposes an enhancement of the ant colony optimization algorithm that caters for dynamic scheduling and load balancing in the grid computing system. The proposed algorithm is known as the enhance ant colony optimization (EACO). The algorithm consists of three new mechanisms that organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism. The resource allocation problem is modelled as a graph that can be used by the ant to deliver its pheromone.This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid resource management element. The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job. EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form. The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job.Resources with high pheromone value are selected to process the submitted jobs. Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization. Experimental results show that EACO produced better grid resource management solution.

scholarly journals Particle Swarm Optimization for Load Balancing in Distributed Computing Systems – A Survey

2021 ◽  
Vol 12 (1S) ◽  
pp. 257-265
Author(s):  
Vidya S. Handur, Et. al.
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Computing ◽  
Load Balancing ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Computing System ◽  
Optimization Approach ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
Swarm Optimization

Development of technology like Cloud Computing and its widespread usage has given rise to exponential increase in the volume of traffic. With this increase in huge traffic the resources in the network would either be insufficient to handle the traffic or the situation may cause some of the resources to be over utilized or underutilized. This condition leads to reduced performance of the system. To improve the performance of the system the traffic requires to be regulated such that all the resources are utilized conferring to their capacity which is known as load balancing. Load balancing has been one of the concerns in the distributed computing systems where the computing nodes do not have a global view of the network. There have been constant efforts to provide an efficient solution for load balancing through the approaches like game theory, fuzzy logic, heuristics and metaheuristics. Even though various solutions exist for balancing the load, the issue is challenging as there does not exist one best fit solution. The paper aims at the study of how Particle Swarm Optimization approach is used to achieve an optimal solution for load balancing in distributed computing system.

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