Other cloud computing solutions include applications and technologies that do not fit into traditional categories, such as IaaS (Infrastructure as a Service), PaaS (Platform as a Service), or SaaS (Software as a Service). They can include custom cloud deployment models, hybrid solutions combining local and cloud environments, new technologies for data management, and advanced services to support application development. They often include innovative approaches, such as edge computing, cloud-native development, and multi-cloud management.
Other Cloud Computing Solutions
Type of technology
Description of the technology
Basic elements
- Edge computing: Processing architecture closer to data sources, e.g. in IoT devices.
- Hybrid solutions: Connecting local resources with cloud infrastructure.
- Multi-cloud management: Tools to manage resources across multiple clouds simultaneously.
- Serverless services: Cloud services that make it possible to run the code without managing servers.
- Cloud databases: Advanced database systems to support scaling and data analysis in the cloud.
Industry usage
- Edge computing: Processing data from IoT devices at the network edge.
- Multi-cloud management: Resource optimisation in multi-cloud environments.
- Hybrid solutions: Combining local and cloud data to improve performance.
- Serverless computing: Running functions in the cloud without managing infrastructure.
- Cloud-based data warehouses: Storage and analysis of large data sets in distributed systems.
Importance for the economy
Other cloud solutions support organisations in adapting to rapidly changing market conditions by enabling more flexible management of IT resources. Companies can take advantage of a variety of deployment models, enhance security, and improve operational efficiency with modern cloud architectures. These solutions contribute to the innovation and competitiveness of companies in global markets.
Related technologies
Mechanism of action
- Other cloud solutions include deploying custom cloud architectures for greater flexibility and operational efficiency. For example, edge computing makes it possible to process data close to the source of generation, reducing latency and the load on central servers. Hybrid models enable companies to keep critical data locally while using the cloud for processing and analysis. These solutions are deployed with specific company requirements in mind, such as the need for low latency, increased control over data, and easier management of multi-cloud environments.
Advantages
- Implementation flexibility: Ability to tailor cloud architecture to meet specific business needs.
- Latency reduction: Faster data processing with edge computing.
- Greater control over data: Hybrid models enable local maintenance of critical data.
- Easier management: Multi-cloud resource management tools.
- Scalability: Ability to dynamically scale resources as needed.
Disadvantages
- Management complexity: Difficulties in integrating and managing multiple cloud environments.
- Risk of security breaches: Potential risks of managing multiple access points.
- Compatibility issues: Difficulties with integration between different cloud platforms.
- High costs: Significant costs of implementing and maintaining advanced cloud systems.
- Portability issues: Difficulties in migrating data between different clouds.
Implementation of the technology
Required resources
- Cloud management platforms: Tools to support the management of multi-cloud environments.
- Computing servers: High-performance servers to support data processing in various environments.
- Data management systems: Cloud-based data integration and management tools.
- Cybersecurity systems: Advanced security features to protect data in distributed environments.
- Network services: High-speed transmission networks for integration of local and cloud resources.
Required competences
- Cloud engineering: Ability to design and implement custom cloud architectures.
- Multi-cloud management: Knowledge of tools and technologies to support multi-cloud environments.
- Data management: Ability to integrate and process data in complex environments.
- IT security: Data protection in distributed and hybrid environments.
- Automation: Creation of scripts and tools to automate the management of cloud environments.
Environmental aspects
- Energy consumption: High energy demand of servers supporting multi-cloud environments.
- Emissions of pollutants: Emissions from data centre operation.
- Raw material consumption: High demand for electronic components.
- Recycling: Problems with recovering materials from equipment used for data processing.
- Waste generated: Electronic waste from IT equipment upgrades ad replacements.
Legal conditions
- Data protection: Regulations for cloud storage and processing, such as GDPR and CCPA.
- IT security standards: Standards for data protection in cloud environments.
- Interoperability regulations: Regulations for connecting data between clouds.
- Intellectual property: Rights regarding software and solutions used in the cloud.
- Industry standards: Standards for quality and security of managing cloud environments.