Chaotic Simulator for Bilevel Optimization of Virtual Machine Placements in Cloud Computing

Ganesan, Timothy; Vasant, Pandian; Litvinchev, Igor

doi:10.1007/s40305-020-00326-5

Cited by 7 publications

(6 citation statements)

References 45 publications

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“…𝛽(𝑟 𝑖𝑗 ) = 𝛽 0 * 𝑒 −𝛾 𝑟 𝑖𝑗 2 (8) Where, 𝐼 0 represents maximum luminance; 𝛾 represents the coefficient of absorption that the light strength; 𝑟 𝑖,𝑗 represents spatial interval; 𝛽 0 represents the maximum degree of attraction.…”

Section: Firefly Algorithm (Fa)mentioning

confidence: 99%

“…In recent years, to counter the security threat in cloud computing environments, major cloud providers have adopted various conventional security protection mechanisms based on the characteristics of their cloud platforms The technology of virtualization permits cloud providers to run number of VMs on a physical machine [7]. An efficient VM placement allows the cloud providers to the cloud users to increase the benefit over reducing the energy consumption, physical machine resource wastage, and increasing the QoS [8]. The scheduling process of the VM is supported by to demand of the physical machines in a specific data center, and the necessity to be fulfilled with the demanded resources [9].…”

Section: Introductionmentioning

confidence: 99%

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Movement Mode Harmony Search Based Multi-objective Firefly Algorithm Feature Selection for Detecting the Security Threats in Virtual Machine

2024

IJIES

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The energy-efficient and secure allocation of virtual machines (VMs) plays an important role at the data center. As cloud computing continues to expand rapidly and the number of cloud users increases day by day, the issue of high energy consumption in complex cloud data centers has become a significant concern. To address this challenge, the consolidation of virtual machines (VMs) emerges as a crucial strategy for optimizing cloud resources efficiently. In this study, a novel security evaluation method is proposed to assist the model available for the virtualized system. A multi-objective model-based firefly algorithm (FA) and harmony search (HS) algorithm are used for the system configuration in VM migration is proposed to measure the security threats such as denial of service (DoS), distributed denial of service (DDoS) and Man-in-the-middle attack. The proposed method also decreases the power consumption, network usage and resource wastage in virtual machines. The proposed algorithm achieves better results compared to other existing methods by utilizing the number of virtual machine blocks with cost migration. The experimental results shows that the proposed FA+HS delivers the performance metrics such as makespan, execution cost and resource utilization and achieved at the 1000VMS of 950, 0.001 and 62 respectively, which ensures the better results compared with the existing methods such as whale optimization genetic algorithm (WOGA), multi-objective whale optimization algorithm-based differential evolution (M-WODE) and joint task scheduling and virtual machine placement (JTSVMP).

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Section: Firefly Algorithm (Fa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Movement Mode Harmony Search Based Multi-objective Firefly Algorithm Feature Selection for Detecting the Security Threats in Virtual Machine

2024

IJIES

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“…In this work, the dual annealing technique is coupled with coupled map lattices (CML) to enhance its optimization capability by simulating chaotic non-linear dynamics. CMLs have applications in diverse fields of research and implementation: system identification, process modeling, design optimization, internet of things (IoT) and [24], [33], [34], [35]. For a specific discreet-time dynamical map, CMLs consist of discreet interacting elements.…”

Section: Chaotic Dual Annealing Optimizationmentioning

confidence: 99%

“…The bilevel optimization problem presented in Equation ( 3) is solved within a Stackelberg game-theoretic framework [34]. The fidelity objective function, F is the upper level/leader while the probability of success, 𝑃(𝛿) lower level/follower.…”

Section: Endmentioning

confidence: 99%

Entanglement Distillation Optimization using Fuzzy Relations for Quantum State Tomography

Ganesan¹,

Elamvazuthi²

2023

Preprint

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Practical entanglement distillation is a critical component in quantum information theory. Entanglement distillation is often utilized for designing quantum computer networks and quantum repeaters. The practical entanglement distillation problem is formulated as a bilevel optimization problem. A fuzzy formulation is introduced to estimate the quantum state (density matrix) from pseudo-likelihood functions (i.e., quantum state tomography). A scale-independent relationship between fuzzy relations in terms of the pseudo-likelihood functions is obtained. The entanglement distillation optimization problem was solved using the combined coupled map lattice and dual annealing approach. Comparative analysis of the results is then conducted against a standard dual annealing algorithmic implementation.

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“…In this work, the dual annealing technique is coupled with coupled map lattices (CML) to enhance its optimization capability by simulating chaotic non-linear dynamics. CMLs have applications in diverse fields of research and implementation: system identification, process modeling, design optimization, internet of things (IoT), and cryptography [24,[33][34][35]. For a specific discreet-time dynamical map, CMLs consist of discreet interacting elements.…”

Section: Chaotic Dual Annealing Optimizationmentioning

confidence: 99%

Entanglement Distillation Optimization Using Fuzzy Relations for Quantum State Tomography

Ganesan

Elamvazuthi

2023

Algorithms

Self Cite

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Practical entanglement distillation is a critical component in quantum information theory. Entanglement distillation is often utilized for designing quantum computer networks and quantum repeaters. The practical entanglement distillation problem is formulated as a bilevel optimization problem. A fuzzy formulation is introduced to estimate the quantum state (density matrix) from pseudo-likelihood functions (i.e., quantum state tomography). A scale-independent relationship between fuzzy relations in terms of the pseudo-likelihood functions is obtained. The entanglement distillation optimization problem is solved using the combined coupled map lattice and dual annealing approach. Comparative analysis of the results is then conducted against a standard dual annealing algorithmic implementation.

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Chaotic Simulator for Bilevel Optimization of Virtual Machine Placements in Cloud Computing

Cited by 7 publications

References 45 publications

Movement Mode Harmony Search Based Multi-objective Firefly Algorithm Feature Selection for Detecting the Security Threats in Virtual Machine

Movement Mode Harmony Search Based Multi-objective Firefly Algorithm Feature Selection for Detecting the Security Threats in Virtual Machine

Entanglement Distillation Optimization using Fuzzy Relations for Quantum State Tomography

Entanglement Distillation Optimization Using Fuzzy Relations for Quantum State Tomography

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