Energy-efficient polyglot persistence database live migration among heterogeneous clouds

Kaur, Kiranbir; Bharany, Salil; Badotra, Sumit; Aggarwal, Karan; Nayyar, Anand; Sharma, Sandeep

doi:10.1007/s11227-022-04662-6

Cited by 25 publications

(21 citation statements)

References 24 publications

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“…However, since these services are customized for specific storage requirements, it may be necessary to aggregate them from several heterogeneous clouds or migrate data from one cloud to another. Data migration can be performed offline when the database is independent of the application or, alternatively, the application must be taken offline during the migration process [7].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Investigating the effect of virtual machine migration accounting on reliability using a cluster model

et al. 2023

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The purpose of the article is to develop and verify with the help of mathematical modeling a software method of deploying a fault-tolerant computing cluster with a virtual machine, which consists of two physical servers (main and backup), on which a distributed data storage system with synchronous data replication from the source server to the backup server is deployed. For this purpose, the task is to conduct a computational experiment on a model of a fault-tolerant cluster, which neglects costs during recovery for the migration of virtual machines by means of the mathematical application Mathcad. Combining computing resources into clusters is a way to ensure high reliability, fault tolerance, and continuity of the computing process of computer systems. This is achieved through virtualization, which enables the movement of virtual resources, services, or applications between physical servers while maintaining the continuity of computing processes. The focus of this study is on a failover cluster, which is composed of two physical servers (primary and backup) connected through a switch, and each server has a local hard disk. A distributed storage system with synchronous data replication from the source server to the backup server is deployed on the local disks of the servers, and a virtual machine is running on the cluster. Markovian processes, flows of podias, and Kolmogorov's systems of differential equations are built into the mathematical tools of the model of a water cluster. To ensure the continuity of the computing process in case of a failure of the main server, a shadow copy of the virtual machine is launched on the backup server. The reliability of the failover cluster is measured by the coefficient of non-stationary readiness. A Markov model is proposed to assess the reliability of the failover cluster, taking into account the costs of migrating virtual machines and mechanisms that ensure the continuity of the computing process in the cluster in case of a failure of one physical server. The memory migration process maintains two copies of the virtual machine on different physical servers, enabling them to continue working on the other in the event of failure. A simplified model of the failover cluster neglects the cost of migrating virtual machines and provides an upper estimate of reliability. The study shows that the reliability of a failover cluster, as measured by the non-stationary availability factor, is significantly impacted by the virtual machine migration process. The findings of this study can be used to inform decisions about the technology chosen to ensure the failure stability and continuity of the computing process of computer systems with cluster architecture. The calculations allow us to draw a conclusion about the significant impact of virtual machine migration accounting on reliability. The calculations allow us to draw a conclusion about the significant impact of virtual machine migration accounting on reliability. The calculation was performed under the following failure rates of the server, disk, and switch: λ0 = 1,115×10-5 1/h, λ1 = 3,425×10-6 1/h, λ2 = 2,3×10-6 1/h recovery respectively: μ0 = 0,33 1/h, μ1 = 0,171/h, μ2 = 0,33 1/h. The intensity of synchronization of the distributed storage system: μ3 = 1 1/h, μ4 = 2 1/h. The difference of non-stationary cluster availability coefficients is d = К2(t) – К1(t) = 2.7×10-10

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Section: Theoretical Backgroundmentioning

confidence: 99%

Investigating the effect of virtual machine migration accounting on reliability using a cluster model

et al. 2023

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“…Taher Elgamal developed the ElGamal algorithm, which is a public-key method of encryption. It is based on the one-way feature, which ensures that encryption schemes are performed separately [20][21][22][23][24].…”

Section: The Elgamal Algorithmmentioning

confidence: 99%

Secure Sensitive Data Sharing Using RSA and ElGamal Cryptographic Algorithms with Hash Functions

Adeniyi¹,

Falola²,

Maashi

et al. 2022

Information

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With the explosion of connected devices linked to one another, the amount of transmitted data grows day by day, posing new problems in terms of information security, such as unauthorized access to users’ credentials and sensitive information. Therefore, this study employed RSA and ElGamal cryptographic algorithms with the application of SHA-256 for digital signature formulation to enhance security and validate the sharing of sensitive information. Security is increasingly becoming a complex task to achieve. The goal of this study is to be able to authenticate shared data with the application of the SHA-256 function to the cryptographic algorithms. The methodology employed involved the use of C# programming language for the implementation of the RSA and ElGamal cryptographic algorithms using the SHA-256 hash function for digital signature. The experimental result shows that the RSA algorithm performs better than the ElGamal during the encryption and signature verification processes, while ElGamal performs better than RSA during the decryption and signature generation process.

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“…To determine how evenly your virtual machines are used [36][37][38][39][40][41][42]. Therefore, a lower DI value demonstrates that the load is more evenly distributed.…”

Section: Definition 3 (Degree Of Imbalance" (Di))mentioning

confidence: 99%

“…The distinct activities and three virtual machines tasks are provided in Table 2. This work therefore contains two sub-problems, which are as follows [40][41][42][43][44][45][46][47][48]:…”

Section: Problem Descriptionmentioning

confidence: 99%

Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)

Alghamdi

2022

Sustainability

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As more people utilize the cloud, more employment opportunities become available. With constraints such as a limited make-span, a high utilization rate of available resources, minimal execution costs, and a rapid turnaround time for scheduling, this becomes an NP-hard optimization issue. The number of solutions/combinations increases exponentially with the magnitude of the challenge, such as the number of tasks and the number of computing resources, making the task scheduling problem NP-hard. As a result, achieving the optimum scheduling of user tasks is difficult. An intelligent resource allocation system can significantly cut down the costs and waste of resources. For instance, binary particle swarm optimization (BPSO) was created to combat ineffective heuristic approaches. However, the optimal solution will not be produced if these algorithms are not paired with additional heuristic or meta-heuristic algorithms. Due to the high temporal complexity of these algorithms, they are less useful in real-world settings. For the NP problem, the binary variation of PSO is presented for workload scheduling and balancing in cloud computing. Considering the updating and optimization constraints stated in this research, our objective function determines if heterogeneous virtual machines (VMs) Phave the most significant difference in completion time. In conjunction with load balancing, we developed a method for updating the placements of particles. According to the experiment results, the proposed method surpasses existing metaheuristic and heuristic algorithms regarding work scheduling and load balancing. This level of success has been attainable because of the application of Artificial Neural Networks (ANN). ANN has demonstrated promising outcomes in resource distribution. ANN is more accurate and faster than multilayer perceptron networks at predicting targets.

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Energy-efficient polyglot persistence database live migration among heterogeneous clouds

Cited by 25 publications

References 24 publications

Investigating the effect of virtual machine migration accounting on reliability using a cluster model

Investigating the effect of virtual machine migration accounting on reliability using a cluster model

Secure Sensitive Data Sharing Using RSA and ElGamal Cryptographic Algorithms with Hash Functions

Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)

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