Portable Modeling for ICU IoT-based Application using TOSCA on the Edge and Cloud

Solayman, Haleema Essa; Qasha, Rawaa Putros

doi:10.1109/csase51777.2022.9759684

Cited by 6 publications

(10 citation statements)

References 12 publications

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“…Remote health surveillance using digital technology enables the monitoring of specific patient parameters, facilitating accurate health assessments from home. This technological advancement has not only reduced patient mobility during COVID-19 but has also ensured intelligent healthcare solutions for individuals of all age groups [12][13][14][15].…”

Section: Contributionsmentioning

confidence: 99%

IoT Based ICU Healthcare: Optimizing Patient Monitoring and Treatment with Advanced Algorithms

2024

JMC

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In the realm of IoT-based Intensive Care Unit (ICU) healthcare, the quest for precision and reliability in patient monitoring and treatment optimization is paramount. This study delves into the realm of advanced algorithms, particularly focusing on the Pelican Optimization Algorithm Long Short-Term Memory (POA-LSTM), known for its remarkable accuracy rates exceeding 95%. The POA-LSTM algorithm, fine-tuned through the Pelican Optimization Algorithm, emerges as a beacon of accuracy in ICU healthcare. By optimizing hyperparameters and leveraging the Pelican Optimization Algorithm's optimization prowess, POA-LSTM surpasses industry standards, offering unparalleled precision and recall rates. Its ability to make informed predictions and provide real-time insights significantly enhances the quality of patient care and clinical decision-making in ICU settings. Additionally, the study explores Context-Oriented Attention LSTM (COA-LSTM) and Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) algorithms, each contributing unique strengths to the landscape of IoT-based ICU healthcare. COA-LSTM's attention mechanism and PSO-LSTM's hyperparameter optimization further enrich the capabilities of predictive modeling and anomaly detection in critical care scenarios. Through the integration of these advanced algorithms, healthcare providers can harness the power of data-driven insights to revolutionize ICU healthcare, ensuring optimal patient outcomes and advancing the frontier of medical care in the digital age.

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Section: Contributionsmentioning

confidence: 99%

IoT Based ICU Healthcare: Optimizing Patient Monitoring and Treatment with Advanced Algorithms

2024

JMC

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“…The Cloudify platform also includes a reasoner capable of translating its own TOSCA entities into Docker deployment files. Solayman et al [18] presented a noteworthy instance of the cooperation between Docker and Cloudify's TOSCA, which can deploy IoT applications as containers across both edge and cloud environments.…”

Section: Description Of the Deployment Phasementioning

confidence: 99%

“…This TOSCA extension aims to introduce a more dynamic and expressive description of containers compared to the original TOSCA, incorporating QoS and QoE attributes, as well as relationships, dependencies, and time constraints. Similarly, Solayman et al [18] extend Cloudify's TOSCA to include syntax capable of describing not only container entities but also entities that describe the programming languages and libraries required by a container to run. This approach splits the container's software requirements into three different entities, making it useful for container orchestration in serverless platforms where vessel containers need to be configured at runtime.…”

Section: Description Of Application Componentsmentioning

confidence: 99%

“…The relationship prestocloud.relationships.executedBy.loadBalanced ensures proper matching between load-balanced fragments and their corresponding agents. In research by Solayman et al [18], host and ConnectsTo relationships are used which are provided by the basic syntax of TOSCA, as well as the contained_in relationship which was introduced with their presented extension. The contained_in relationship expresses that a programming language or a library should be installed in a target container.…”

Section: Relationships Dependencies and Environment Valuesmentioning

confidence: 99%

“…This output is in the form of a deployment plan that con-tains resource information such as CPU, storage, and memory, and is then parsed by the Armonic component to generate appropriately sized virtual machines, also known as flavors. Soleyman et al [18] describe flavors as instance types in the Cloudify node cloudify.nodes.aws.ec2.Instances, which also defines the resource capacity of the virtual machine.…”

Section: Description Of Hostsmentioning

confidence: 99%

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A Survey on Modeling Languages for Applications Hosted on Cloud-Edge Computing Environments

Korontanis,

Makris,

Tserpes

2024

Applied Sciences

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In the field of edge-cloud computing environments, there is a continuous quest for new and simplified methods to automate the deployment and runtime adaptation to application lifecycle changes. Towards that end, cloud providers promote their own service description languages to describe deployment and adaptation processes, whereas application developers opt for cloud-agnostic open standards capable of modeling applications. However, not all open standards are able to capture concepts that relate to the adaptation of the underlying computing environment to changes in the application lifecycle. In our quest for a formal approach to encapsulate these concepts, this study presents various Cloud Modeling Languages (CMLs). In this study, when referring to CMLs, we are discussing service description languages, domain-specific languages, and open standards. The output of this study is a review that performs a classification on CMLs based on their effectiveness in describing deployment and adaptation of applications in both cloud and edge environments. According to our findings, approximately 90.9% of the examined languages offer support for deployment descriptions overall. In contrast, only around 27.2% of examined languages allow developers the choice to specify whether their application components should be deployed on the edge or in a cloud environment. Regarding runtime adaptation descriptions, approximately 54.5% of the languages provide support in general.

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Fog computing out of the box: Dynamic deployment of fog service containers with TOSCA

Basak,

Srirama

2023

Int J Network Mgmt

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The conventional cloud‐centric Internet of Things (IoT) application fails to meet the latency requirement of time‐critical applications. The idea of edge and fog computing arrived to distribute workloads across the fog devices located in the local area. However, achieving seamless interoperability, platform independence, and automatic deployment of services becomes the major challenge over heterogeneous fog devices. This paper proposes an integrated and standards‐based fog computing federation framework, FogDEFT, that adapts OASIS–Topology and Orchestration Specification for Cloud Applications (TOSCA) for service deployment in fog. The framework standardizes the distributed application design with TOSCA Service Template to deploy Docker Containers in Swarm mode and manages interoperability over heterogeneous fog devices. The framework uses a lightweight TOSCA compliant orchestrator to dynamically deploy various fog applications (user‐developed services) on the fly.

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Portable Modeling for ICU IoT-based Application using TOSCA on the Edge and Cloud

Cited by 6 publications

References 12 publications

IoT Based ICU Healthcare: Optimizing Patient Monitoring and Treatment with Advanced Algorithms

IoT Based ICU Healthcare: Optimizing Patient Monitoring and Treatment with Advanced Algorithms

A Survey on Modeling Languages for Applications Hosted on Cloud-Edge Computing Environments

Fog computing out of the box: Dynamic deployment of fog service containers with TOSCA

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