Infrastructure as Code (IaC): Insights on Various Platforms

Kumar, Manish; Mishra, Shilpi; Lathar, Niraj Kumar; Singh, Pooran

doi:10.1007/978-981-19-5443-6_33

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…We managed all our AWS services through the Infrastructure as Code approach (Kumar et al, 2023) with Terraform v1.0.11. As a result, the whole configuration of our cloud environment is stored as Terraform code in the main NSP repository (nsp-code) under infra\core-infra sub-folder for the VPC configuration, and under infra\nsp-lb-service for all the other associated services.…”

Section: Simulation Settingmentioning

confidence: 99%

Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds

Chlasta

Paweł

Wójcik

et al. 2023

Front. Neuroinform.

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In this study, we explore the simulation setup in computational neuroscience. We use GENESIS, a general purpose simulation engine for sub-cellular components and biochemical reactions, realistic neuron models, large neural networks, and system-level models. GENESIS supports developing and running computer simulations but leaves a gap for setting up today's larger and more complex models. The field of realistic models of brain networks has overgrown the simplicity of earliest models. The challenges include managing the complexity of software dependencies and various models, setting up model parameter values, storing the input parameters alongside the results, and providing execution statistics. Moreover, in the high performance computing (HPC) context, public cloud resources are becoming an alternative to the expensive on-premises clusters. We present Neural Simulation Pipeline (NSP), which facilitates the large-scale computer simulations and their deployment to multiple computing infrastructures using the infrastructure as the code (IaC) containerization approach. The authors demonstrate the effectiveness of NSP in a pattern recognition task programmed with GENESIS, through a custom-built visual system, called RetNet(8 × 5,1) that uses biologically plausible Hodgkin–Huxley spiking neurons. We evaluate the pipeline by performing 54 simulations executed on-premise, at the Hasso Plattner Institute's (HPI) Future Service-Oriented Computing (SOC) Lab, and through the Amazon Web Services (AWS), the biggest public cloud service provider in the world. We report on the non-containerized and containerized execution with Docker, as well as present the cost per simulation in AWS. The results show that our neural simulation pipeline can reduce entry barriers to neural simulations, making them more practical and cost-effective.

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Section: Simulation Settingmentioning

confidence: 99%

Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds

Chlasta

Paweł

Wójcik

et al. 2023

Front. Neuroinform.

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“…DevOps practices such as Continuous Integration and Continuous Deployment (CI/CD) [4] play a pivotal role, in automating the integration, testing, and deployment of code changes to ensure software quality and rapid feature deployment with reduced risks. Furthermore, Infrastructure as Code (IaC) [5], an essential component of DevOps, enables the provisioning and management of infrastructure resources through code, promoting consistency and reproducibility. Scientifically, this approach aligns infrastructure management with version control and automation, fostering a more efficient, reliable, and scalable software development and deployment process within the dynamic realm of cloud-native services.…”

Section: Introductionmentioning

confidence: 99%

Security in Cloud-Native Services: A Survey

Theodoropoulos,

Rosa,

Benzaid

et al. 2023

JCP

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Cloud-native services face unique cybersecurity challenges due to their distributed infrastructure. They are susceptible to various threats like malware, DDoS attacks, and Man-in-the-Middle (MITM) attacks. Additionally, these services often process sensitive data that must be protected from unauthorized access. On top of that, the dynamic and scalable nature of cloud-native services makes it difficult to maintain consistent security, as deploying new instances and infrastructure introduces new vulnerabilities. To address these challenges, efficient security solutions are needed to mitigate potential threats while aligning with the characteristics of cloud-native services. Despite the abundance of works focusing on security aspects in the cloud, there has been a notable lack of research that is focused on the security of cloud-native services. To address this gap, this work is the first survey that is dedicated to exploring security in cloud-native services. This work aims to provide a comprehensive investigation of the aspects, features, and solutions that are associated with security in cloud-native services. It serves as a uniquely structured mapping study that maps the key aspects to the corresponding features, and these features to numerous contemporary solutions. Furthermore, it includes the identification of various candidate open-source technologies that are capable of supporting the realization of each explored solution. Finally, it showcases how these solutions can work together in order to establish each corresponding feature. The insights and findings of this work can be used by cybersecurity professionals, such as developers and researchers, to enhance the security of cloud-native services.

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“…When creating all the resources manually, the probability of making a mistake in several hundred manual operations is close to 100%. Considering these problems, infrastructure automation by using IaC practices is becoming not just a fashion trend, but a necessity [3].…”

mentioning

confidence: 99%

Research on the optimization model for building an efficient IT infrastructure using the AWS platform

Simkin,

Kopp,

Olkhovyi

2023

InterConf+

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The analysis of IT infrastructure processes has shown that their manual execution without automation can be time-consuming and labor-intensive, as well as increases the risk of errors. Designing an IT infrastructure using Amazon Web Services (AWS) allows to identify the required resources and components for building an efficient infrastructure. Therefore, this study aims to improve the processes of building and managing IT infrastructure by automating them using the AWS platform. Hence, in this paper we propose a mathematical model to determine the IT infrastructure desired configuration. The proposed model assumes solving a linear programming problem with integer constraints on the optimization variables. The Branch-and-Bound algorithm is used to study the proposed mathematical model, by considering different AWS server input data and defining optimal IT infrastructure configurations.

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Infrastructure as Code (IaC): Insights on Various Platforms

Cited by 3 publications

References 9 publications

Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds

Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds

Security in Cloud-Native Services: A Survey

Research on the optimization model for building an efficient IT infrastructure using the AWS platform

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