Optimizing makespan and resource utilization in cloud computing environment via evolutionary scheduling approach DOI Creative Commons
Faten Khalid Karim, Sara Ghorashi, Salem Alkhalaf

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(11), P. e0311814 - e0311814

Published: Nov. 22, 2024

As a new computing resources distribution platform, cloud technology greatly influenced society with the conception of on-demand resource usage through virtualization technology. Virtualization allows physical in way that will enable multiple end-users to have similar hardware infrastructure. In cloud, many challenges exist on provider side due expectations clients. Resource scheduling (RS) is most significant nondeterministic polynomial time (NP) hard problem owing its crucial impact performance. Previous research found metaheuristics can dramatically increase CC performance if deployed as algorithms. Therefore, this study develops an evolutionary algorithm-based approach for makespan optimization and utilization (EASA-MORU) technique environment. The EASA-MORU aims maximize effectively use technique, dung beetle (DBO) used purposes. Moreover, balances load properly distributes based demands evaluation method tested using series measures. A wide range comprehensive comparison studies emphasized performs better than other methods different

Language: Английский

Substitution Box Construction Using Transfer‐Function Assisted Metaheuristic and Booster Algorithm: A Hybrid Approach DOI
Mohammad Shadab, Md Saquib Jawed, Mohammad Sajid

et al.

Security and Privacy, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 26, 2024

ABSTRACT S‐box strengthens the encryption and decryption process by introducing nonlinearity protecting encrypted data against various differential linear cryptanalytic attacks. Generating a highly nonlinear with maximal is computationally impractical due to expansive search space, classifying it as an NP‐hard problem. This paper proposes Hawkboost algorithm, novel hybrid method merging Harris Hawks optimization algorithm (HHO) Booster for generating S‐boxes low computational efforts. The HHO utilized navigate in large permutation space find acceptable cryptographic properties. assisted Transfer function Random Key (RK) speed up design process. Additionally, enhances applying random local operators like swap inversion, effectively reshaping elements of S‐box. combination methodologies facilitates efficient generation that exhibit excellent properties while addressing key challenges optimization. performance proposed has been analyzed comparing state‐of‐the‐art based on numerous characteristics including average nonlinearity, strict Avalanche criterion (SAC), SAC offset, bit independence (BIC), approximation probability (LP), (DP), fixed points, opposite cycle counts. results from experiments analysis multiple metrics show satisfies all requirements safe reliable without sacrificing any crucial security features.

Language: Английский

Citations

3
Multi-objective energy aware task scheduling using Orthogonal Learning Particle Swarm Optimization on cloud environment DOI

Bantupalli Nagalakshmi,

S. Sumathy

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 13, 2024

Language: Английский

Citations

3
QoS aware task scheduling and congestion avoidance in fog enabled car parking systems DOI

M. K. Dhananjaya,

Kalpana Sharma,

Amit Kumar Chaturvedi

et al.

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: 16(8), P. 4787 - 4795

Published: Aug. 10, 2024

Language: Английский

Citations

1
GIJA:Enhanced geyser‐inspired Jaya algorithm for task scheduling optimization in cloud computing DOI
Laith Abualigah, Ahmad MohdAziz Hussein,

Mohammad H. Almomani

et al.

Transactions on Emerging Telecommunications Technologies, Journal Year: 2024, Volume and Issue: 35(7)

Published: July 1, 2024

Abstract Task scheduling optimization plays a pivotal role in enhancing the efficiency and performance of cloud computing systems. In this article, we introduce GIJA (Geyser‐inspired Jaya Algorithm), novel approach tailored for task environments. integrates principles Geyser‐inspired algorithm with algorithm, augmented by Levy Flight mechanism, to address complexities optimization. The motivation research stems from increasing demand efficient resource utilization management computing, driven proliferation Internet Things (IoT) devices growing reliance on cloud‐based services. Traditional algorithms often face challenges handling dynamic workloads, heterogeneous resources, varying objectives, necessitating innovative techniques. leverages eruptive dynamics geysers, inspired nature's channeling guide decisions. By combining simplicity effectiveness offers robust framework capable adapting diverse Additionally, integration mechanism introduces stochasticity into process, enabling exploration solution spaces accelerating convergence. To evaluate efficacy GIJA, extensive experiments are conducted using synthetic real‐world datasets representative workloads. Comparative analyses against existing algorithms, including AOA, RSA, DMOA, PDOA, LPO, SCO, GIA, GIAA, demonstrate superior terms quality, convergence rate, diversity, robustness. findings provide promising quality addressing environments (95%), implications system performance, scalability, utilization.

Language: Английский

Citations

1
OSSA Scheduler: Opposition-Based Learning Salp Swarm Algorithm for Task Scheduling in Cloud Computing DOI
Mohammad Qasim, Mohammad Sajid, Maria Lapina

et al.

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 237 - 248

Published: Jan. 1, 2024

Language: Английский

Citations

1
Task Scheduling Strategy Using Chaotic Whale Optimization Algorithm in Cloud Computing DOI
Mohammad Qasim, Mohammad Sajid, Ranjit Rajak

et al.

Advances in computer and electrical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 31 - 52

Published: Dec. 6, 2024

Cloud computing is becoming popular because it can provide cloud consumers with IT services scaled up globally over the internet. These include platforms, applications, and infrastructure. Moreover, be provided on demand offered in different pricing packages. To schedule task optimally a environment considered an NP-hard problem, which has become complex introduction of variables such as resource dynamicity on-demand consumer applications. The proposed research introduces Whale Optimization Algorithm (WOA) incorporating transfer function (TF) tent chaotic map to tackle scheduling challenges computing. performance chaotic-based whale optimization algorithm (CWOA) compared that well-known metaheuristics methods. results show CWOA may significantly reduce makespan problem standard Grey Wolf Optimizer (GWO) BAT algorithms. Furthermore, converges quickly search space grows more prominent, making suitable for large-scale issues.

Language: Английский

Citations

0
Generating Highly Nonlinear S-Boxes Using a Hybrid Approach With Particle Swarm Optimization DOI
Mohammad Shadab, Md Saquib Jawed, Mohammad Sajid

et al.

Advances in computer and electrical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 30

Published: Dec. 6, 2024

A substitution box (S-box) is a fundamental component in cryptographic algorithms that enhance data security by providing complex mapping between input and output values. S-box strengthens the encryption decryption process introducing nonlinearity protecting encrypted against various differential linear cryptanalytic attacks. The problem of generating an with optimal properties challenging falls under category NP-Hard problems. This study proposes hybrid approach combining Particle Swarm optimization algorithm (PSO) Booster to construct highly nonlinear low computational efforts. PSO algorithm, assisted Transfer function Random Key (RK), utilized navigate large permutation search space find acceptable properties. works based on random applications local operators for shuffling elements each other transforming elements' arrangement, resulting modified increased nonlinearity.

Language: Английский

Citations

0
Optimizing makespan and resource utilization in cloud computing environment via evolutionary scheduling approach DOI Creative Commons
Faten Khalid Karim, Sara Ghorashi, Salem Alkhalaf

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(11), P. e0311814 - e0311814

Published: Nov. 22, 2024

As a new computing resources distribution platform, cloud technology greatly influenced society with the conception of on-demand resource usage through virtualization technology. Virtualization allows physical in way that will enable multiple end-users to have similar hardware infrastructure. In cloud, many challenges exist on provider side due expectations clients. Resource scheduling (RS) is most significant nondeterministic polynomial time (NP) hard problem owing its crucial impact performance. Previous research found metaheuristics can dramatically increase CC performance if deployed as algorithms. Therefore, this study develops an evolutionary algorithm-based approach for makespan optimization and utilization (EASA-MORU) technique environment. The EASA-MORU aims maximize effectively use technique, dung beetle (DBO) used purposes. Moreover, balances load properly distributes based demands evaluation method tested using series measures. A wide range comprehensive comparison studies emphasized performs better than other methods different

Language: Английский

Citations

0