Comparative Analysis of Various Evolutionary Techniques of Load Balancing: A Review

Mishra, Manvi; Agarwal, Sumeet; Mishra, Payal; Singh, Shankar

doi:10.5120/10540-4675

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…Finite Element Method (FEM) is a numerical analysis technique used to solve complex engineering problems [12]. The methodology of FEM analysis can be summarized as follows:…”

Section: Methodsmentioning

confidence: 99%

Finite Element Analysis on the Arms of Multirotor UAV

Abuzar Ulla Khan,

Justus Shadrach J,

Karan H

et al. 2023

acsjse

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As the development and applications of UAV is been increasing significantly in various fields. The main focus is towards the design and structural analysis of the UAV. As the initial step of initialization of any UAV prior condition is to design. The structural analysis of a UAV frame is important since it has to withstand the forces due to aerodynamic and gravitational forces due to the mounted weights. As design optimisation plays an important role for an efficient structure, a finite element analysis provides an efficient methodology to meet the desired objectives related to structural elements. This project mainly focuses on the structural loads and vibrations acting on the arms of multirotor UAV. Finite element analysis is been carried out on the arms of multirotor UAV to understand the structural loads acting on the arms of the UAV. A small deformation occurred on the arms are safe and within the limit. Therefore, this study aimed to analyse the strength and durability of UAV arms during the in-flight conditions through the analysis.

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“…Finite Element Method (FEM) is a numerical analysis technique used to solve complex engineering problems [12]. The methodology of FEM analysis can be summarized as follows:…”

Section: Methodsmentioning

confidence: 99%

Finite Element Analysis on the Arms of Multirotor UAV

Abuzar Ulla Khan,

Justus Shadrach J,

Karan H

et al. 2023

acsjse

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“…Scheduling and load balancing are two of the most important aspects allowing to squeeze performance and optimizing the energy consumption of current parallel and distributed systems. The importance of this topic is reflected in a large number of publications and several surveys as [14][15][16][17][18].…”

Section: Related Workmentioning

confidence: 99%

Improving utilization of heterogeneous clusters

Stafford

Bosque

2020

J Supercomput

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Datacenters often agglutinate sets of nodes with different capabilities, leading to a sub-optimal resource utilization. One of the best ways of improving utilization is to balance the load by taking into account the heterogeneity of these clusters. This article presents a novel way of expressing computational capacity, more adequate for heterogeneous clusters, and also advocates for task migration in order to further improve the utilization. The experimental evaluation shows that both proposals are advantageous and allow improving the utilization of heterogeneous clusters and reducing the makespan to 16.7% and 17.1%, respectively.

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“…More recently, other nature-inspired optimization methods have been investigated for dynamic load balancing, including Particle Swarm Optimization (PSO). A good review of such methods can be found in [5]. At the best of our knowledge, no other authors have ever attempted to use EO to solve dynamic load balancing.…”

Section: Introductionmentioning

confidence: 97%

Extremal Optimization with Guided State Changes in Load Balancing of Distributed Programs

Falco¹,

Laskowski

Olejnik³

et al. 2014

2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

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International audienceThe paper concerns methods for using Extremal Optimization (EO) for processor load balancing during execution of distributed programs. A load balancing algorithm for clusters of multicore processors is presented and discussed. In this algorithm the EO approach is used to periodically detect the best tasks as candidates for migration and for a guided selection of the best processors to receive the migrated tasks. To decrease the complexity of selection for migration, we propose a guided EO algorithm which assumes a two step stochastic selection during the solution improvement based on two separate fitness functions. The functions are based on specific program models which estimate relations between the programs and the executive hardware. The proposed load balancing algorithm is assessed by experiments with simulated load balancing of distributed program graphs. The algorithm is compared against an EO - based algorithm with random placement of migrated tasks and a classic genetic algorithm

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Comparative Analysis of Various Evolutionary Techniques of Load Balancing: A Review

Cited by 8 publications

References 32 publications

Finite Element Analysis on the Arms of Multirotor UAV

Finite Element Analysis on the Arms of Multirotor UAV

Improving utilization of heterogeneous clusters

Extremal Optimization with Guided State Changes in Load Balancing of Distributed Programs

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