Highly scalable medical imaging repository based on Kubernetes

Baptista, Tiberio; Silva, Luís A. Bastião; Costa, Carlos

doi:10.1109/bibm52615.2021.9669559

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…The demand for scaling systems to streamline the management of cloud computing resources has intensified, particularly for systems with broad applications and a large user base. It is not surprising that research on optimizing the scaling process of Kubernetes clusters is often conducted based on platforms supporting the healthcare sector [25][26][27]. In this domain, complex performance and security requirements pose unique challenges for system engineers [28].…”

Section: Title-abs-key ((K8s or Kubernetes) And Autoscaling And (Hpa ...mentioning

confidence: 99%

Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud Deployments

Augustyn,

Wyciślik,

Sojka

2024

Applied Sciences

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In the context of scaling a business-critical medical service that involves electronic medical record storage deployed in Kubernetes clusters, this research addresses the need to optimize the configuration parameters of horizontal pod autoscalers for maintaining the required performance and system load constraints. The maximum entropy principle was used for calculating a load profile to satisfy workload constraints. By observing the fluctuations in the existing workload and applying a kernel estimator to smooth its trends, we propose a methodology for calculating the threshold parameter of a maximum number of pods managed by individual autoscalers. The results obtained indicate significant computing resource savings compared to autoscalers operating without predefined constraints. The proposed optimization method enables significant savings in computational resource utilization during peak loads in systems managed by Kubernetes. For the investigated case study, applying the calculated vector of maximum pod count parameter values for individual autoscalers resulted in about a 15% reduction in the number of instantiated nodes. The findings of this study provide valuable insights for efficiently scaling services while meeting performance demands, thus minimizing resource consumption when deploying to computing clouds. The results enhance our comprehension of resource optimization strategies within cloud-based microservice architectures, transcending the confines of specific domains or geographical locations.

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Section: Title-abs-key ((K8s or Kubernetes) And Autoscaling And (Hpa ...mentioning

confidence: 99%

Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud Deployments

Augustyn,

Wyciślik,

Sojka

2024

Applied Sciences

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“…The model explores a two-dimensional torus topology based on 5*5 optical switches per node. In [29], the authors investigated a novel architecture that is compliant with cloud-based medical imaging requirements using Kubernetes. Efforts in [30] investigated a new optical DCN referred to as ROTOS.…”

Section: Related Research Effortsmentioning

confidence: 99%

Integrating Resilient Tier N+1 Networks with Distributed Non-Recursive Cloud Model for Cyber-Physical Applications

2022

KSII TIIS

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Cyber-physical systems (CPS) have been growing exponentially due to improved clouddatacenter infrastructure-as-a-service (CDIaaS). Incremental expandability (scalability), Quality of Service (QoS) performance, and reliability are currently the automation focus on healthy Tier 4 CDIaaS. However, stable QoS is yet to be fully addressed in Cyber-physical data centers (CP-DCS). Also, balanced agility and flexibility for the application workloads need urgent attention. There is a need for a resilient and fault-tolerance scheme in terms of CPS routing service including Pod cluster reliability analytics that meets QoS requirements. Motivated by these concerns, our contributions are fourfold. First, a Distributed Non-Recursive Cloud Model (DNRCM) is proposed to support cyber-physical workloads for remote lab activities. Second, an efficient QoS stability model with Routh-Hurwitz criteria is established. Third, an evaluation of the CDIaaS DCN topology is validated for handling largescale, traffic workloads. Network Function Virtualization (NFV) with Floodlight SDN controllers was adopted for the implementation of DNRCM with embedded rule-base in Open vSwitch engines. Fourth, QoS evaluation is carried out experimentally. Considering the nonrecursive queuing delays with SDN isolation (logical), a lower queuing delay (19.65%) is observed. Without logical isolation, the average queuing delay is 80.34%. Without logical resource isolation, the fault tolerance yields 33.55%, while with logical isolation, it yields 66.44%. In terms of throughput, DNRCM, recursive BCube, and DCell offered 38.30%, 36.37%, and 25.53% respectively. Similarly, the DNRCM had an improved incremental scalability profile of 40.00%, while BCube and Recursive DCell had 33.33%, and 26.67% respectively. In terms of service availability, the DNRCM offered 52.10% compared with recursive BCube and DCell which yielded 34.72% and 13.18% respectively. The average delays obtained for DNRCM, recursive BCube, and DCell are 32.81%, 33.44%, and 33.75% respectively. Finally, workload utilization for DNRCM, recursive BCube, and DCell yielded 50.28%, 27.93%, and 21.79% respectively.

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Dicoogle Open Source: The Establishment of a New Paradigm in Medical Imaging

Lebre

Pinho²,

Jesus

et al. 2022

J Med Syst

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The rapid and continuous growth of data volume and its heterogeneity has become one of the most noticeable trends in healthcare, namely in medical imaging. This evolution led to the deployment of specialized information systems supported by the DICOM standard that enables the interoperability of distinct components, including imaging modalities, repositories, and visualization workstations. However, the complexity of these ecosystems leads to challenging learning curves and makes it time-consuming to mock and apply new ideas. Dicoogle is an extensible medical imaging archive server that emerges as a tool to overcome those challenges. Its extensible architecture allows the fast development of new advanced features or extends existent ones. It is currently a fundamental enabling technology in collaborative and telehealthcare environments, including research projects, screening programs, and teleradiology services. The framework is supported by a Learning Pack that includes a description of the web programmatic interface, a software development kit, documentation, and implementation samples. This article gives an in-depth view of the Dicoogle ecosystem, state-of-the-art contributions, and community impact. It starts by presenting an overview of its architectural concept, highlights some of the most representative research backed up by Dicoogle, some remarks obtained from its use in teaching, and worldwide usage statistics of the software. Finally, the positioning of Dicoogle in the medical imaging software field is discussed through comparison with other well-known solutions.

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Highly scalable medical imaging repository based on Kubernetes

Cited by 5 publications

References 12 publications

Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud Deployments

Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud Deployments

Integrating Resilient Tier N+1 Networks with Distributed Non-Recursive Cloud Model for Cyber-Physical Applications

Dicoogle Open Source: The Establishment of a New Paradigm in Medical Imaging

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