Security-aware dynamic VM consolidation

Elshabka, Mohamed A.; Hassan, Hanan A.; Sheta, Walaa M.; Harb, Hany M.

doi:10.1016/j.eij.2020.10.002

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…Elshabka et al (17) suggested a SDVMC method. An innovative VM placement approach designated MRI with RITH is employed in the SDVMC segment, while the SMM employs a 3D security evaluation framework.…”

Section: Related Workmentioning

confidence: 99%

Adaptive firefly algorithm for resource allocation and modified advanced encryption standard algorithm for hypervisor attack detection on cloud computing

Banu Priya,

Maheswari

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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Introduction: The advantages and wide-ranging applications of cloud computing have made it a prominent attention for scholars these days. The dispersed structure of cloud and its whole dependence on the internet for service delivery extant security problems. Methods: Hypervisor attack detection is carried out in this study using Modified Advanced Encryption Standard (MAES) system which ensures security prominently. It finds and detects the attacks earlier for secured VM migration. The proposed system includes main phases including system framework, load balancing, resource allocation and hypervisor attack detection via MAES algorithm. Initially, Over the duration of cloud computing, consider the quantity of tasks, VM, and cloud users. In this research, the MMH system is employed for load balancing to balance the total burden across the cloud. Tasks are moved from overloaded to underloaded nodes to attain load balancing. Next, the allocation of resources is carried out utilizing Adaptive Firefly (AF) optimization system which is used to select best resources optimally. It generates the best fitness values to choose the best resources. Results: It is also focused to improve the cost metric, computational complexity, throughput and VM performance in cloud. Then, to detect hypervisor attacks, the MAES method is employed. It specializes on offering enhanced security for cloud data and is employed to identify hypervisor and VM attackers. Conclusion: The findings produced the conclusion that the suggested MAES method superior to the current approaches according to throughput, computation cost, Mean Square Error (MSE) rate, and energy use.

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Section: Related Workmentioning

confidence: 99%

Adaptive firefly algorithm for resource allocation and modified advanced encryption standard algorithm for hypervisor attack detection on cloud computing

Banu Priya,

Maheswari

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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“…Elshopka et al 11 had designed security-aware dynamic VM integration. The method was focused on security-based VM migration to enlarge the lifetime of the Datacenters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…By looking at the conditions prior and then afterward the emphasis, the ideal value is contrasted with the individual history ideal value and the global ideal value, separately, and the individual history ideal value and the global ideal value are updated. The velocity and position of PSO are defined in (11) and (12).…”

Section: Step 3: Updation Using Ibso Algorithmmentioning

confidence: 99%

“…Randomly initialize the solution (refer to Figure 3) Evaluate the fitness of each solution using Equation (10) Set cycle 1 Repeat While (t < t max ) for i = 1:N update the position X t+1 j using Equation (11) update the velocity V t+1 j using Equation (12) end for for i = 1:N update the right ( X t+1 rs ) and left ( X t+1 ls ) antenna position using Equation (18) update the incremental function 𝜉 t+1 js using Equation ( 16) update the beetle speed Q t+1 js using Equation ( 14) update the next position of the beetle X t+1 js using Equation ( 13…”

Section: Startmentioning

confidence: 99%

“…Numerous researchers had developed virtual machine placement and consolidation on the cloud. Among them, few of the works are analyzed here; Elshopka et al 11 had designed security‐aware dynamic VM integration. The method was focused on security‐based VM migration to enlarge the lifetime of the Datacenters.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Improved beetle swarm optimization algorithm for energy efficient virtual machine consolidation on cloud environment

Hariharan

Rathinavelayatham

Shanmugaiah

et al. 2022

Concurrency and Computation

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In cloud computing, virtual machine (VM) consolidation and migration are significant schemes to enhance the energy efficiency and costs of the cloud environment. The process of VM consolidation is to execute a more number of tasks with less power consumption. However, due to unreliable physical resources, energy consumption is increased. So, to solve this issue, improved beetle swarm optimization (IBSO) algorithm based on energy-aware VM consolidation is presented in this article. IBSO algorithm joins together beetle swarm optimization (BSO) and particle swarm optimization (PSO). The proposed algorithm is defined with the effective representation of solution and multi-objective functions such as power consumption, resource wastage, and SLA violation. Results of the work are depicted that the proposed approach outperformed different previous methods in terms of different evaluation measures.

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ABSO: an energy-efficient multi-objective VM consolidation using adaptive beetle swarm optimization on cloud environment

Hariharan

Siva

Kaliraj

et al. 2021

J Ambient Intell Human Comput

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Security-aware dynamic VM consolidation

Cited by 7 publications

References 6 publications

Adaptive firefly algorithm for resource allocation and modified advanced encryption standard algorithm for hypervisor attack detection on cloud computing

Adaptive firefly algorithm for resource allocation and modified advanced encryption standard algorithm for hypervisor attack detection on cloud computing

Improved beetle swarm optimization algorithm for energy efficient virtual machine consolidation on cloud environment

ABSO: an energy-efficient multi-objective VM consolidation using adaptive beetle swarm optimization on cloud environment

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