Quality of service aware improved coati optimization algorithm for efficient task scheduling in cloud computing environment

Tamilarasu, P.; Singaravel, G.

doi:10.1016/j.jer.2023.09.024

Cited by 7 publications

(1 citation statement)

References 26 publications

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“…These techniques are developed by inspiring natural hunting and escaping phenomenon of the coatis, foxes and pelicans, respectively. There are limited studies including the usage of COA [24,25], FOX [26,27] and POA [28,29] on the optimization of problems in the literature. However, there have not been encountered with any study which contains the implementation of COA, FOX and POA techniques on complex and nonlinear engineering design problems such as structural design cost optimization, so far.…”

Section: Introductionmentioning

confidence: 99%

Design cost minimization of a reinforced concrete column section using overnew swarm-based optimization algorithms

Tunca,

Carbas

2024

Neural Comput & Applic

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It is very tiresome for a practiser to detect the best feasible sizing design of structural members including reinforced concrete columns that is a highly nonlinear and complicated structural engineering optimization problem. This is due to such a design is practically conducted via conventional trial-and-error computing methods in which resistance to external loads, cost efficiency, and aesthetic factors, etc. have to be considered. This study focuses on minimizing the design cost of primarily proposed reinforced concrete column design problem via three overnew swarm-based optimizers such as Coati Optimization Algorithm, Fox Optimizer and Pelican Optimization Algorithm (POA) that are firstly utilized for this purpose. In this regard, the type of steel rebar distribution, the characteristic strength of the concrete, the height and width of the column section, and the number and diameter of the rebars are treated as discrete design variables of the newly proposed complex reinforced concrete column design cost optimization problem. In solution, the design requirements specified in practice code provisions should also be met. Here, Turkish Building Earthquake Code 2018 specifications are considered as practice structural design constraints. Consequently, the algorithmic performances of three overnew swarm-based metaheuristic optimization algorithms are compared and evaluated in detail. Amongst them, the POA shows most fruitful algorithmic design solution performance.

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Section: Introductionmentioning

confidence: 99%

Design cost minimization of a reinforced concrete column section using overnew swarm-based optimization algorithms

Tunca,

Carbas

2024

Neural Comput & Applic

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An Effective Virtual Machine Allocation in Federated Cloud by PARAMR-DNN Technique

Kshatriya,

Lepakshi

2024

Communications in Computer and Information Science

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LATA: learning automata-based task assignment on heterogeneous cloud computing platform

Gheisari,

ShokrZadeh

2024

J Supercomput

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A cloud computing environment is a distributed system where idle resources are accessible across a wide area network, such as the Internet. Due to the diverse specifications of these resources, computational clouds exhibit high heterogeneity. Task scheduling, the process of dispatching cloud applications onto processing nodes, becomes a critical challenge in such environments. Ensuring high utilization in this heterogeneous environment entails identifying suitable machines or virtual machines capable of efficiently executing jobs, constituting a multi-objective optimization problem. This paper proposes a dynamic Learning Automata-based Task Assignment algorithm, named LATA, to address this challenge. In the algorithm, each application is represented as a Directed Acyclic Graph, with tasks as nodes and data dependencies as edges. Initially, tasks are grouped based on their data dependencies to consolidate independent tasks into one group. Subsequently, a variable-structure learning automaton is assigned to each group of tasks to identify appropriate task-machine combinations. The primary objectives of LATA include minimizing makespan and energy consumption by facilitating efficient task placement to achieve load balance and maximize resource utilization. Additionally, an enhancement is proposed, involving the use of a different grouping policy prior to task assignment to further improve performance. Computer simulation results demonstrate the superior performance of the proposed algorithms in highly heterogeneous environments compared to state-of-the-art algorithms. Notably, total execution time and energy consumption decrease by up to 50% and 37%, respectively.

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Quality of service aware improved coati optimization algorithm for efficient task scheduling in cloud computing environment

Cited by 7 publications

References 26 publications

Design cost minimization of a reinforced concrete column section using overnew swarm-based optimization algorithms

Design cost minimization of a reinforced concrete column section using overnew swarm-based optimization algorithms

An Effective Virtual Machine Allocation in Federated Cloud by PARAMR-DNN Technique

LATA: learning automata-based task assignment on heterogeneous cloud computing platform

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