Subject » BMEVITMMA14
Cloud Services integration for Intelligent Devices
Felhőszolgáltatások intelligens eszközök támogatására
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Felhőszolgáltatások intelligens eszközök támogatására
Cloud Services integration for Intelligent Devices
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subject code | BMEVITMMA14 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | vizsga | ||||||||||||
| Credits | 5 | ||||||||||||
| Subject coordinator |
Dr. Simon Csaba
position: egyetemi docens
contact:
simon.csaba@vik.bme.hu
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| Responsible department |
Távközlési és Mesterséges Intelligencia Tanszék
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| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | https://www.tmit.bme.hu/vitmma14 | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Programme
1. Introduction
Motivation: digitalization of industries. Usage environment: Internet of Things (IoT) devices, possibilities of networked devices, communication needs. Smart devices: the task and role of intelligence in IoT systems. Trends.
2. Fundamentals of cloud systems
Background of the development of cloud-based systems, motivation. Definition of cloud systems, categories, use cases, security, monitoring.
3. Cloud services
Cloud service models: IaaS, PaaS, SaaS. The microservices model. Virtual machine and container-based systems. Orchestration functions, development of complex services, service chains.
4. Cloud service providers
Cloud systems and data centers. Presentation and illustration of the architecture of a cloud system through an OpenStack cloud. Large public service providers (hyperscalers: AWS, Google Cloud and Azure) and their functions.
5. Container-based systems
Container-based virtualization (Docker, Podman, Containerd). Container management frameworks (Kubernetes). The relationship between Kubernetes and cloud systems (Amazon EKS).
6. IoT platforms in the cloud
The role of cloud-based IoT platforms, their characteristic functions. Case study: Introduction and comparison of Amazon IoT Device Management and Amazon IoT Core services.
7. Smart devices in the Edge environment
The edge computing model (Edge Computing). Edge computing for IoT services.
8. Mobile network conditions for controlling smart devices
Quality requirements for remote control of smart devices. Properties of 5G networks, conditions provided for machine communication.
9. IoT support in 5G networks
IoT support in 5G systems using virtualized functions and MEC. Aspects of deploying smart devices and industrial applications in 5G private networks.
10. Device control in an industrial environment
Case study: control tasks during IoT Industry 4.0 work. Task performance conditions, communication and computing requirements.
11. Cloud-based device control
Presentation of communication conditions for remote device control. Analysis of QoS requirements.
12. Orchestration tasks
Presentation of resource and service orchestration tasks. Ensuring resource requirements for remote device control in a dynamic environment. Maintaining quality of service, ensuring automatic adaptation in the cloud system.
13. Implementing real-time tasks in cloud systems
Implementation problems of real-time tasks in cloud systems. Possibility of implementing real-time communication in the cloud network environment. Integration of delay-sensitive networking (Time-Sensitive Networking - TSN) into cloud systems.
Detailed topics of the exercises/labs:
1. Getting to know the AWS system
2. Managing containers in AWS, Amazon Elastic Container Service (ECS). Creating and managing a container.
3. Kubernetes and AWS: Amazon Elastic Kubernetes Service (EKS). Creating and managing a pod.
4. Distributed event management in cloud systems: AWS IoT Core.
5. Presentation of the departmental 5G network. Implementing a container-based service in this network.
6. Industrial IoT case study. Outsourcing intelligence from the device.
7. Analyzing the requirements of cloud-based real-world control, setting up task scheduling.
Motivation: digitalization of industries. Usage environment: Internet of Things (IoT) devices, possibilities of networked devices, communication needs. Smart devices: the task and role of intelligence in IoT systems. Trends.
2. Fundamentals of cloud systems
Background of the development of cloud-based systems, motivation. Definition of cloud systems, categories, use cases, security, monitoring.
3. Cloud services
Cloud service models: IaaS, PaaS, SaaS. The microservices model. Virtual machine and container-based systems. Orchestration functions, development of complex services, service chains.
4. Cloud service providers
Cloud systems and data centers. Presentation and illustration of the architecture of a cloud system through an OpenStack cloud. Large public service providers (hyperscalers: AWS, Google Cloud and Azure) and their functions.
5. Container-based systems
Container-based virtualization (Docker, Podman, Containerd). Container management frameworks (Kubernetes). The relationship between Kubernetes and cloud systems (Amazon EKS).
6. IoT platforms in the cloud
The role of cloud-based IoT platforms, their characteristic functions. Case study: Introduction and comparison of Amazon IoT Device Management and Amazon IoT Core services.
7. Smart devices in the Edge environment
The edge computing model (Edge Computing). Edge computing for IoT services.
8. Mobile network conditions for controlling smart devices
Quality requirements for remote control of smart devices. Properties of 5G networks, conditions provided for machine communication.
9. IoT support in 5G networks
IoT support in 5G systems using virtualized functions and MEC. Aspects of deploying smart devices and industrial applications in 5G private networks.
10. Device control in an industrial environment
Case study: control tasks during IoT Industry 4.0 work. Task performance conditions, communication and computing requirements.
11. Cloud-based device control
Presentation of communication conditions for remote device control. Analysis of QoS requirements.
12. Orchestration tasks
Presentation of resource and service orchestration tasks. Ensuring resource requirements for remote device control in a dynamic environment. Maintaining quality of service, ensuring automatic adaptation in the cloud system.
13. Implementing real-time tasks in cloud systems
Implementation problems of real-time tasks in cloud systems. Possibility of implementing real-time communication in the cloud network environment. Integration of delay-sensitive networking (Time-Sensitive Networking - TSN) into cloud systems.
Detailed topics of the exercises/labs:
1. Getting to know the AWS system
2. Managing containers in AWS, Amazon Elastic Container Service (ECS). Creating and managing a container.
3. Kubernetes and AWS: Amazon Elastic Kubernetes Service (EKS). Creating and managing a pod.
4. Distributed event management in cloud systems: AWS IoT Core.
5. Presentation of the departmental 5G network. Implementing a container-based service in this network.
6. Industrial IoT case study. Outsourcing intelligence from the device.
7. Analyzing the requirements of cloud-based real-world control, setting up task scheduling.
In several industries, as part of the digitalization process, the industry-specific devices are connected to applications installed in the cloud. This leads to a blurring of the boundary between electrical engineering systems and information infrastructure, and a digitalized environment formed by smart devices integrated with the cloud system is realized. During the design, implementation and operation of such a system, it is equally important for electrical engineers to understand the characteristics of the defining element of the IT infrastructure, the cloud-based network. An additional task is to learn about the alternatives for integrating smart devices into the cloud system. The aim of the course is to transfer the above knowledge, illustrate it with the help of use cases, and practice it.
In the first part of the course, the properties of cloud systems are described, with particular attention to a cloud-based infrastructure and a cloud network. In the second part, the practical implementation of these principles is presented, the focus will be on learning about and using the elements of the system. In the third part, issues of integrating smart devices into the cloud system will be discussed through specific case studies using IoT applications used in departmental research and development projects.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
Lectures and practices (in small groups).
Tanulástámogató anyagok
Online források
Preparation is aided by the expanded presentation slides prepared for this purpose, as well as the literature listed below:; Dinesh G. Dutt, Cloud Native Data Center Networking: Architecture, Protocols, and Tools, O'Reilly Media; 1st edition (December 10, 2019)
Recommended preliminary knowledge for completing the subject
Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
nincs
General rules
Requirements:
During the teaching period
Succesful completion of one midterm at the middle of the teaching period.
During the exam period
Written exam, the result of the midterm weights with 25% (theory and simple exercises).
Additional possibilities:
It is possible to write the mid-term exam again on the week of repeat.
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
Not provided.
Recommended courses
Not provided.
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
—
Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.