Greedy Multi-Cloud Selection Approach to Deploy an Application Based on Microservices

Carvalho, Juliana Oliveira de; Vieira, Dário; Trinta, Fernando

doi:10.1109/empdp.2019.8671608

Cited by 8 publications

(8 citation statements)

References 15 publications

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“…Carvalho et al 42 propose multiple provider selection approaches for cloud service selection in microservice-based applications. This approach can select several services from a single provider for a microservice and separate providers for each microservice, unlike other techniques.…”

Section: Related Workmentioning

confidence: 99%

Micro‐IDE: A tool platform for generating efficient deployment alternatives based on microservices

Aksakallı

Çelik²,

Can

et al. 2022

Softw Pract Exp

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Microservice architecture (MSA) is a paradigm to design and develop scalable distributed applications using loosely coupled, highly cohesive components that can be deployed independently. The applications that realize the MSA may contain thousands of services that together form the overall system. Microservices interact with each other by producing and consuming data. Deploying frequently communicating services to the same physical resource would reduce network utilization, which is vital for reducing costs and improving scalability.Since the physical resources have limited capacity, it is not always possible to deploy communicating services to the same resource. Therefore, automated efficient deployment alternatives need to be generated for MSA in the design phase.To address this problem, we proposed an algorithmic approach to generate efficient microservice deployment configurations to available cloud resources in our previous study. In this study, a tool (Micro-IDE) has been proposed to realize and evaluate this approach. The Micro-IDE tool has been validated using a case study inspired by the Spotify application.

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Section: Related Workmentioning

confidence: 99%

Micro‐IDE: A tool platform for generating efficient deployment alternatives based on microservices

Aksakallı

Çelik²,

Can

et al. 2022

Softw Pract Exp

View full text Add to dashboard Cite

show abstract

“…• Application servers: many cloud service designs distribute functionalities among microservices. Microservices spread the application's logic into simple, indivisible services [27], [28]. Microservices allow easy scalability and simple integration for easy updates of only the necessary components.…”

Section: B Monitoring Framework Architecturementioning

confidence: 99%

Provisioning Computational Resources for Cloud-Based e-Learning Platforms Using Deep Learning Techniques

et al. 2021

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The use of e-learning technologies is growing even faster due to the existing conditions where virtual setups temporarily replace traditional classroom environments. Service infrastructure support for e-learning has moved to the cloud. For this reason, the efficient provisioning of resources for such platforms, which is achieved through prediction, is very relevant. The existing techniques for predicting the use of resources in the cloud are not designed with e-learning's specific requirements. This paper presents a neural-network-based model for predicting the usage of computational resources for e-learning platforms. This model consists of a series of interconnected neural networks used to predict values for variables of interest, such as Random Access Memory (RAM) usage and Central Processing Unit (CPU) usage. Using data collected from a high school real scenario, we analyzed and used it to train and validate our neuralnetwork-based model. This scenario consisted of a Moodle server deployed in a Google Virtual Machine with a configured course and its contents. Each student performed a series of activities while connected to it. Our proposed model achieves high accuracy. The obtained results are promising, paving the way towards the construction of software tools for provisioning computational resources on demand for e-learning platforms.

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“…In this paper, we propose a new provider selection model and three different approaches for this model. Unlike our previous works [15][16][17], we now consider that the communication time between microservices deployed in different providers must be handled. Next, we detail our main contributions.…”

Section: Introductionmentioning

confidence: 99%

LM2K Model for Hosting an Application Based on Microservices in Multi-Cloud

Carvalho

Vieira

Rodrigues

et al. 2023

Sensors

Self Cite

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Cloud computing has become a popular delivery model service, offering several advantages. However, there are still challenges that need to be addressed when applying the cloud model to specific scenarios. Two of such challenges involve deploying and executing applications across multiple providers, each comprising several services with similar functionalities and different capabilities. Therefore, dealing with application distributions across various providers can be a complex task for a software architect due to the differing characteristics of the application components. Some works have proposed solutions to address the challenges discussed here, but most of them focus on service providers. To facilitate the decision-making process of software architects, we previously presented PacificClouds, an architecture for managing the deployment and execution of applications based on microservices and distributed in a multi-cloud environment. Therefore, in this work, we focus on the challenges of selecting multiple clouds for PacificClouds and choosing providers that best meet the microservices and software architect requirements. We propose a selection model and three approaches to address various scenarios. We evaluate the performance of the approaches and conduct a comparative analysis of them. The results demonstrate their feasibility regarding performance.

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Greedy Multi-Cloud Selection Approach to Deploy an Application Based on Microservices

Cited by 8 publications

References 15 publications

Micro‐IDE: A tool platform for generating efficient deployment alternatives based on microservices

Micro‐IDE: A tool platform for generating efficient deployment alternatives based on microservices

Provisioning Computational Resources for Cloud-Based e-Learning Platforms Using Deep Learning Techniques

LM2K Model for Hosting an Application Based on Microservices in Multi-Cloud

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