Simulation techniques have become a powerful tool for deciding the best starting conditions on pay-as-you-go scenarios. This is the case of public cloud infrastructures, where a given number and type of virtual machines (in short VMs) are instantiated during a specified time, being this reflected in the final budget. With this in mind, this paper introduces and validates iCanCloud, a novel simulator of cloud infrastruc-tures with remarkable features such as flexibility, scalability, performance and usability. Further-more, the iCanCloud simulator has been built on the following design principles: (1) it's targeted to conduct large experiments, as opposed to oth-ers simulators from literature; (2) it provides a flexible and fully customizable global hypervisor for integrating any cloud brokering policy; (3) it reproduces the instance types provided by a given cloud infrastructure; and finally, (4) it contains a user-friendly GUI for configuring and launching simulations, that goes from a single VM to large cloud computing systems composed of thousands of machines.Keywords Cloud computing · Cloud computing simulator · Cloud hypervisor · Validation · Scalability 1 to solve a given computational problem. If the same software and configurations are needed, the VMs may be started using the same image. This way, a machine offered by a computing cloud may become whatever the user needs, from a standalone computer to a cluster or Grid node.Nowadays, cloud computing systems are increasing their role due to the fast (r)evolution of computer networks and communication technologies. A very clear proof of this fact is that very important companies like Amazon, Google, Dell, IBM, and Microsoft are investing billions of dollars in order to provide their own cloud solutions [28].As soon as the scientific community had access to cloud production infrastructures, the first applications started to run on the cloud [26,34]. In many Research areas, the leap from traditional cluster and Grid computing to this new paradigm has been mandatory, being the main reason an evolution in the computational needs of the applications [10]. A remarkable fact from this evolution is that in a pre-cloud environment, hardware defines the level of parallelism of an application. In cloud computing, the level of parallelism is defined by the application itself, as there is no restriction in the number of machines, and CPU availability is 100% guaranteed by standard.There

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Abstract-The increasing heterogeneity of cloud resources, and the increasing diversity of services being deployed in cloud environments are leading to significant increases in the complexities of cloud resource management. This paper presents an architecture to manage heterogeneous resources and to improve service delivery in cloud environments. A loosely-coupled, hierarchical, selfadapting management model, deployed across multiple layers, is used for heterogeneous resource management. Moreover, a service-specific coalition formation mechanism is employed to identify appropriate resources to support the process parallelism associated with high performance services. Finally, a proofof-concept of the proposed hierarchical cloud architecture, as realized in CloudLightning project, is presented.

h i g h l i g h t s • An ontology for interoperability in heterogeneous cloud infrastructures isproposed. • Enable the adoption of heterogeneous physical resources in self managed clouds-Support for HPC-in-Cloud, hardware accelerators, resource abstraction methods. • A proposed architecture to explot the semantic and sintactic benefits. • Included into the CloudLightning project for large scale Cloud Computing environments.

Incorporating ethics and values within the life cycle of an AI asset means securing its development, deployment, use, and decommission under these perspectives. These approaches depend on the market domain where AI is operational – considering the interaction and the impact on humans if any process does not perform as expected – and the legal compliance, both required to ensure adequate fulfilment of ethics and values. Specifically, in the manufacturing sector, standards were developed since the 1990’s to guarantee, among others, the correct use of mechanical machinery, systems robustness, low product variability, workers safety, system security, and adequate implementation of system constraints. However, it is challenging to blend the existing practices with the needs associated with deployments of AI in a trustworthy manner. This document provides an extended framework for AI Management within the Manufacturing sector. The framework is based on different perspectives related to responsible AI that handle trustworthy issues as risk. The approach is based on the idea that ethical considerations can and should be handled as hazards. If these requirements or constraints are not adequately fulfilled and managed, it is expected severe negative impact on different sustainable pillars. We are proposing a well-structured approach based on risk management that would allow implementing ethical concerns in any life cycle stages of AI components in the manufacturing sector. The framework follows a pipeline structure, with the possibility of being extended and connected with other industrial Risk Management Processes, facilitating its implementation in the manufacturing domain. Furthermore, given the dynamic condition of the regulatory state of AI, the framework allows extension and considerations that could be developed in the future.