Intermediate node selection for Scatter-Gather VM migration in cloud data center

Annadanam, Sudarshan Chakravarthy; Sudhakar, Chapram; Ramesh, T.

doi:10.1016/j.jestch.2020.01.008

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…The preliminary goal of this synopsis is to minimize the three-fold multipurpose problem, which is described statistically in Equation (1).…”

Section: Multipurpose Problem Description and Evaluation For Quantifi...mentioning

confidence: 99%

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Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model

Nauman,

Zhang,

et al. 2024

Applied Sciences

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Cloud computing has gained widespread recognition for facilitating myriad online services and applications. However, the current stages of commercial cloud computing employ a moderate design, wherein computational resources like storage and servers are housed in a few sizable worldwide data centers. System reliability, efficiency, and low latency are all goals of virtual machine (VM) placement. Load balancing has emerged as a crucial challenge for attaining energy efficiency in a fictitious grid computing architecture where a variety of users’ workloads are distributed across several virtual machines. We propose a more effective optimization technique known as the twin fold moth flame algorithm. This algorithm considers multiple constraints, including computation time, stability, and placement cost. The proposed model’s effectiveness will be evaluated based on relocation costs, reaction times, and stability assessments. The most significant gains of the presented work are 4.24%, 9.73%, 11.10%, 28.83%, 7.63%, and 10.62% for 20 count data of nodes for artificial bee colony–bat algorithm, ant colony optimization, crow search algorithm, krill herd, whale optimization genetic algorithm, and improved Lévy-based whale optimization algorithm, respectively.

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“…The preliminary goal of this synopsis is to minimize the three-fold multipurpose problem, which is described statistically in Equation (1).…”

Section: Multipurpose Problem Description and Evaluation For Quantifi...mentioning

confidence: 99%

“…Cloud-based solution technologies have seen extraordinary development over the past few years. Cloud computing has become a popular model for providing support for a diverse set of web-based applications and businesses [1][2][3]. For instance, the medical industry leverages cloud computing solutions to securely store and retrieve data.…”

Section: Introductionmentioning

confidence: 99%

Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model

Nauman,

Zhang,

et al. 2024

Applied Sciences

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“…Some cloud resources are required for the execution of those apps. (18) The location of VMs is critical to the effective use of data center resources. Each host in the data center can be in one of two states: either active or in sleep mode.…”

Section: System Modelmentioning

confidence: 99%

“…Real fireflies' social and biological characteristics served as influence of the Firefly Algorithm. (21) Genuine fireflies release rhythmic light that acts as a warning system to keep predators away and helps in luring potential mates. This flashing behavior is articulated by FA utilizing the problem's objective function as the basis for optimization.…”

Section: Resource Allocation Adaptive Firefly (Af) Algorithmmentioning

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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“…Live VM migration provides uninterrupted cloud services during VM consolidation [5,6] which also upholds fault tolerance, load balancing and minimizes Service Level Agreement (SLA) breaches [7,8]. However, it is essential to perceive CPU utilization, memory, disk and power consumption for preventing resource wastage [8]. Also it enables the end-users to experience the cloud resources without break.…”

Section: Introductionmentioning

confidence: 99%

Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

Subramanian¹,

Sriram²

2023

Intelligent Automation &Amp; Soft Computing

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Live Virtual Machine (VM) migration is one of the foremost techniques for progressing Cloud Data Centers' (CDC) proficiency as it leads to better resource usage. The workload of CDC is often dynamic in nature, it is better to envisage the upcoming workload for early detection of overload status, underload status and to trigger the migration at an appropriate point wherein enough number of resources are available. Though various statistical and machine learning approaches are widely applied for resource usage prediction, they often failed to handle the increase of non-linear CDC data. To overcome this issue, a novel Hypergrah based Convolutional Deep Bi-Directional-Long Short Term Memory (CDB-LSTM) model is proposed. The CDB-LSTM adopts Helly property of Hypergraph and Savitzky-Golay (SG) filter to select informative samples and exclude noisy inference & outliers. The proposed approach optimizes resource usage prediction and reduces the number of migrations with minimal computational complexity during live VM migration. Further, the proposed prediction approach implements the correlation co-efficient measure to select the appropriate destination server for VM migration. A Hypergraph based CDB-LSTM was validated using Google cluster dataset and compared with state-of-the-art approaches in terms of various evaluation metrics.

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Intermediate node selection for Scatter-Gather VM migration in cloud data center

Cited by 5 publications

References 8 publications

Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model

Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model

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

Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

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