MiCADO – Towards a microservice-based cloud application-level dynamic orchestrator

Visti, Hannu; Kiss, Tamás; Terstyánszky, Gábor; Gesmier, Gregoire; Winter, Stephen

doi:10.7287/peerj.preprints.2536v1

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…A framework named MiCADO was proposed by Visti et al [24], which provided application-level dynamic cloud orchestration, thus resulting in predicting the application capacity demand behavior. The microservices orchestration layer was divided into four sublayers; refer to Figure 3.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The framework was presented as the first proof-of-concept and its applicability was checked using open-source tools such as Docker1, Consul2, etc. In 2019, the MiCADO by Visti et al [24] was implemented by Kiss et al [25], which provided support for the automated scalability of cloud applications. The implementation for evaluation of MiCADO was based on the CloudSigma public cloud.…”

Section: Literature Reviewmentioning

confidence: 99%

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Containerized Microservices Orchestration and Provisioning in Cloud Computing: A Conceptual Framework and Future Perspectives

et al. 2022

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Cloud computing is a rapidly growing paradigm which has evolved from having a monolithic to microservices architecture. The importance of cloud data centers has expanded dramatically in the previous decade, and they are now regarded as the backbone of the modern economy. Cloud-based microservices architecture is incorporated by firms such as Netflix, Twitter, eBay, Amazon, Hailo, Groupon, and Zalando. Such cloud computing arrangements deal with the parallel deployment of data-intensive workloads in real time. Moreover, commonly utilized cloud services such as the web and email require continuous operation without interruption. For that purpose, cloud service providers must optimize resource management, efficient energy usage, and carbon footprint reduction. This study presents a conceptual framework to manage the high amount of microservice execution while reducing response time, energy consumption, and execution costs. The proposed framework suggests four key agent services: (1) intelligent partitioning: responsible for microservice classification; (2) dynamic allocation: used for pre-execution distribution of microservices among containers and then makes decisions for dynamic allocation of microservices at runtime; (3) resource optimization: in charge of shifting workloads and ensuring optimal resource use; (4) mutation actions: these are based on procedures that will mutate the microservices based on cloud data center workloads. The suggested framework was partially evaluated using a custom-built simulation environment, which demonstrated its efficiency and potential for implementation in a cloud computing context. The findings show that the engrossment of suggested services can lead to a reduced number of network calls, lower energy consumption, and relatively reduced carbon dioxide emissions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Containerized Microservices Orchestration and Provisioning in Cloud Computing: A Conceptual Framework and Future Perspectives

et al. 2022

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“…Microservice architectures [66], [115] represents a so-lution for developers to convert large services into small, independent heterogeneous and isolated services that are managed by using exchange data and messages [116]. Nevertheless, the creation of comprehensive solutions with implicit resource management and flexible portability is still an issue for microservice architectures [117]. In turn, the model proposed in this paper introduces software pieces called blocks (implemented in the form of microservices and nanoservices), which include methods for managing the exchange of data and messages in implicit, secure, reliable, and cost-efficiency manners.…”

Section: Related Workmentioning

confidence: 99%

On the Continuous Processing of Health Data in Edge-Fog-Cloud Computing by Using Micro/Nanoservice Composition

et al. 2020

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The edge, the fog, the cloud, and even the end-user's devices play a key role in the management of the health sensitive content/data lifecycle. However, the creation and management of solutions including multiple applications executed by multiple users in multiple environments (edge, the fog, and the cloud) to process multiple health repositories that, at the same time, fulfilling non-functional requirements (NFRs) represents a complex challenge for health care organizations. This paper presents the design, development, and implementation of an architectural model to create, on-demand, edge-fog-cloud processing structures to continuously handle big health data and, at the same time, to execute services for fulfilling NFRs. In this model, constructive and modular blocks, implemented as microservices and nanoservices, are recursively interconnected to create edge-fog-cloud processing structures as infrastructureagnostic services. Continuity schemes create dataflows through the blocks of edge-fog-cloud structures and enforce, in an implicit manner, the fulfillment of NFRs for data arriving and departing to/from each block of each edge-fog-cloud structure. To show the feasibility of this model, a prototype was built using this model, which was evaluated in a case study based on the processing of health data for supporting critical decision-making procedures in remote patient monitoring. This study considered scenarios where end-users and medical staff received insights discovered when processing electrocardiograms (ECGs) produced by sensors in wireless IoT devices as well as where physicians received patient records (spirometry studies, ECGs and tomography images) and warnings raised when online analyzing and identifying anomalies in the analyzed ECG data. A scenario where organizations manage multiple simultaneous each edge-fog-cloud structure for processing of health data and contents delivered to internal and external staff was also studied. The evaluation of these scenarios showed the feasibility of applying this model to the building of solutions interconnecting multiple services/applications managing big health data through different environments. INDEX TERMS Big health data, edge-fog-cloud, health-IoT processing, Internet of Things, microservice architecture.

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“…Whereas the cloud platforms have evolved to a great scale and provide numerous services to achieve the scalable designs. But with the multitude of public cloud platform options available, for example: AWS, Azure, GCP, IBM Bluemix to name a few, and the plethora of infrastructure services they offer, it becomes a daunting task to pick up the right choice and design an optimal deployment architecture for MSA [18].…”

Section: Choice Of the Right Infrastructure Platformrelated Workmentioning

confidence: 99%

A Systematic Study of Micro Service Architecture Evolution and their Deployment Patterns

Rudrabhatla¹

2018

IJCA

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With the advent of Local Area Networks (LAN), the client server architecture gained traction. The ever-growing need for the distributed systems have paved the path for client server architecture to transform in to Service Oriented Architecture (SOA). Due to the reusable and loosely coupled nature of the services, SOA became a successful representation of client server architecture. However, over time SOA fell short of expectations, as it was fully reliant on monolithic system design. Achieving horizontal scalability, faster response times, high availability, infrastructure agility, service and resource isolation was a challenge in SOA frameworks. Micro service architecture (MSA) soon came to the rescue. It offered various solutions to overcome most of the shortfalls of the traditional monolithic SOA architecture. But at the same time, MSA comes with its own set of challenges due to the complex distributed design. Among various design complexities involved in MSA, creating, managing and deploying microservices in a clustered production grade environment is a major challenge. A micro service can be deployed to run on a virtual machine (VM) or on a container which itself runs on a VM. The VM can be in the data center or in the public cloud. The containers can be self-managed or orchestrated. The orchestration can be done by the cloud provider or a third-party software. This research paper illustrates (1) the journey of architectural design patterns from SOA to MSA, by citing the related work and the reasons for evolution. (2) various deployment models available for MSA (3) comparison of the deployment models and a quantitative analysis of the use cases.

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MiCADO – Towards a microservice-based cloud application-level dynamic orchestrator

Cited by 4 publications

References 6 publications

Containerized Microservices Orchestration and Provisioning in Cloud Computing: A Conceptual Framework and Future Perspectives

Containerized Microservices Orchestration and Provisioning in Cloud Computing: A Conceptual Framework and Future Perspectives

On the Continuous Processing of Health Data in Edge-Fog-Cloud Computing by Using Micro/Nanoservice Composition

A Systematic Study of Micro Service Architecture Evolution and their Deployment Patterns

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