In this blog we are presenting a sneak peak into ICOS exploitation activities!

The ICOS project is now in its final phase, moving decisively towards validation and the release of the final ICOS product.

The ICOS System aims at being the next generation meta-operating system for the Cloud-Edge-IoT Continuum, offering a complete platform for orchestrating the user applications’ lifecycle in a secure, smart and efficient way. At the core of the ICOS’ orchestration capabilities there are i) a powerful observability framework that efficiently reports the status and events for the infrastructure and the applications (e.g. topology, resource characteristics and usage, security assessments, power consumption), ii) a policy-based engine that allow to compare the current status of the system with the desidered policies for a given component of the infrastructure and/or applications and iii) a matchmaking mechanisms to find an orchestration solution that satisfy all the requirements.

As edge computing matures, onboarding devices and services into a unified ecosystem becomes a critical task, especially when that ecosystem spans from sensors in the field to high-performance computing in the cloud. The ICOS project (IoT2Cloud Operating System) is tackling this challenge by offering a streamlined and intelligent approach to onboarding that supports flexibility, automation, and interoperability.

The ICOS System aims at being the next generation meta-operating system for the Cloud-Edge-IoT Continuum, offering a complete platform for orchestrating the user applications’ lifecycle in a secure, smart and efficient way. At the core of the ICOS’ orchestration capabilities there are i) a powerful observability framework that efficiently reports the status and events for the infrastructure and the applications (e.g. topology, resource characteristics and usage, security assessments, power consumption), ii) a policy-based engine that allow to compare the current status of the system with the desidered policies for a given component of the infrastructure and/or applications and iii) a matchmaking mechanisms to find an orchestration solution that satisfy all the requirements.

The ICOS System aims at being the next generation meta-operating system for the Cloud-Edge-IoT Continuum, offering a complete platform for orchestrating the user applications’ lifecycle in a secure, smart and efficient way. At the core of the ICOS’ orchestration capabilities there are i) a powerful observability framework that efficiently reports the status and events for the infrastructure and the applications (e.g. topology, resource characteristics and usage, security assessments, power consumption), ii) a policy-based engine that allow to compare the current status of the system with the desidered policies for a given component of the infrastructure and/or applications and iii) a matchmaking mechanisms to find an orchestration solution that satisfy all the requirements.

As edge computing matures, onboarding devices and services into a unified ecosystem becomes a critical task, especially when that ecosystem spans from sensors in the field to high-performance computing in the cloud. The ICOS project (IoT2Cloud Operating System) is tackling this challenge by offering a streamlined and intelligent approach to onboarding that supports flexibility, automation, and interoperability.

The ICOS System aims at being the next generation meta-operating system for the Cloud-Edge-IoT Continuum, offering a complete platform for orchestrating the user applications’ lifecycle in a secure, smart and efficient way. At the core of the ICOS’ orchestration capabilities there are i) a powerful observability framework that efficiently reports the status and events for the infrastructure and the applications (e.g. topology, resource characteristics and usage, security assessments, power consumption), ii) a policy-based engine that allow to compare the current status of the system with the desidered policies for a given component of the infrastructure and/or applications and iii) a matchmaking mechanisms to find an orchestration solution that satisfy all the requirements.

As edge computing matures, onboarding devices and services into a unified ecosystem becomes a critical task, especially when that ecosystem spans from sensors in the field to high-performance computing in the cloud. The ICOS project (IoT2Cloud Operating System) is tackling this challenge by offering a streamlined and intelligent approach to onboarding that supports flexibility, automation, and interoperability.

Deploying microservice applications across multiple clusters offers many benefits. These include, for example, improved fault tolerance, increased scalability, performance and regulatory compliance. In addition, use of multiple locations may be required to access specialized hardware, services or data sources available only in those locations. Kubernetes SIG multicluster lists additional reasons for using multiple clusters.

An AI as a Service (AIaaS) framework is a comprehensive suite of cloud-based services, tools, and infrastructure that enables individuals and organizations to leverage artificial intelligence capabilities without the need to invest in and manage the underlying hardware, software, and personnel [1][3].

Deploying microservice applications across multiple clusters offers many benefits. These include, for example, improved fault tolerance, increased scalability, performance and regulatory compliance. In addition, use of multiple locations may be required to access specialized hardware, services or data sources available only in those locations. Kubernetes SIG multicluster lists additional reasons for using multiple clusters.

An AI as a Service (AIaaS) framework is a comprehensive suite of cloud-based services, tools, and infrastructure that enables individuals and organizations to leverage artificial intelligence capabilities without the need to invest in and manage the underlying hardware, software, and personnel [1][3].

Deploying microservice applications across multiple clusters offers many benefits. These include, for example, improved fault tolerance, increased scalability, performance and regulatory compliance. In addition, use of multiple locations may be required to access specialized hardware, services or data sources available only in those locations. Kubernetes SIG multicluster lists additional reasons for using multiple clusters.

An AI as a Service (AIaaS) framework is a comprehensive suite of cloud-based services, tools, and infrastructure that enables individuals and organizations to leverage artificial intelligence capabilities without the need to invest in and manage the underlying hardware, software, and personnel [1][3].

Deploying microservice applications across multiple clusters offers many benefits. These include, for example, improved fault tolerance, increased scalability, performance and regulatory compliance. In addition, use of multiple locations may be required to access specialized hardware, services or data sources available only in those locations. Kubernetes SIG multicluster lists additional reasons for using multiple clusters.

Artificial Intelligence (AI) is no longer the exclusive domain of powerful supercomputers and humongous datacenters. From smart thermostats to wearable health trackers, AI is quietly powering more and more of our everyday devices. But as intelligent technology spreads, so does the challenge of making it efficient, accessible, and sustainable — especially in devices that aren’t exactly powerhouses.

Deploying microservice applications across multiple clusters offers many benefits. These include, for example, improved fault tolerance, increased scalability, performance and regulatory compliance. In addition, use of multiple locations may be required to access specialized hardware, services or data sources available only in those locations. Kubernetes SIG multicluster lists additional reasons for using multiple clusters.

Artificial Intelligence (AI) is no longer the exclusive domain of powerful supercomputers and humongous datacenters. From smart thermostats to wearable health trackers, AI is quietly powering more and more of our everyday devices. But as intelligent technology spreads, so does the challenge of making it efficient, accessible, and sustainable — especially in devices that aren’t exactly powerhouses.

Deploying microservice applications across multiple clusters offers many benefits. These include, for example, improved fault tolerance, increased scalability, performance and regulatory compliance. In addition, use of multiple locations may be required to access specialized hardware, services or data sources available only in those locations. Kubernetes SIG multicluster lists additional reasons for using multiple clusters.

Artificial Intelligence (AI) is no longer the exclusive domain of powerful supercomputers and humongous datacenters. From smart thermostats to wearable health trackers, AI is quietly powering more and more of our everyday devices. But as intelligent technology spreads, so does the challenge of making it efficient, accessible, and sustainable — especially in devices that aren’t exactly powerhouses.