Trust is a crucial aspect when cyber-physical systems have to rely on resources and services under ownership of various entities, such as in the case of Edge, Fog and Cloud computing. The DECENTER's Fog Computing Platform is developed to support Big Data pipelines, which start from the Internet of Things (IoT), such as cameras that provide video-streams for subsequent analysis. It is used to implement Artificial Intelligence (AI) algorithms across the Edge-Fog-Cloud computing continuum which provide benefits to applications, including high Quality of Service (QoS), improved privacy and security, lower operational costs and similar. In this article, we present a trust management architecture for DECENTER that relies on the use of blockchain-based Smart Contracts (SCs) and specifically designed trustless Smart Oracles. The architecture is implemented on Ethereum ledger (testnet) and three trust management scenarios are used for illustration. The scenarios (trust management for cameras, trusted data flow and QoS based computing node selection) are used to present the benefits of establishing trust relationships among entities, services and stakeholders of the platform.

Context: Existing software workbenches allow for the deployment of cloud applications across a variety of Infrastructure-as-a-Service (IaaS) providers. The expected workload, Quality of Service (QoS) and Non-Functional Requirements (NFRs) must be considered before an appropriate infrastructure is selected. However, this decision-making process is complex and timeconsuming. Moreover, the software engineer needs assurances that the selected infrastructure will lead to an adequate QoS of the application. Objective: The goal is to develop a new method for selection of an optimal cloud deployment option, that is, an infrastructure and configuration for deployment and to verify that all hard and as many soft QoS requirements as possible will be met at runtime. Method: A new Formal QoS Assurances Method (FoQoSAM), which relies on stochastic Markov models is introduced to facilitate an automated decision-making process. For a given workload, it uses QoS monitoring data and a user-related metric in order to automatically generate a probabilistic model. The probabilistic model takes the form of a finite automaton. It is further used to produce a rank list of cloud deployment options. As a result, any of the cloud deployment options can be verified by applying a probabilistic model checking approach. Results: Testing was performed by ranking deployment options for two

Databases as software components may be used to serve a variety of smart applications. Currently, the Internet of Things (IoT), Artificial Intelligence (AI) and Cloud technologies are used in the course of projects such as the Horizon 2020 EU-Korea DECENTER project in order to implement four smart applications in the domains of Smart Homes, Smart Cities, Smart Construction and Robot Logistics. In these smart applications the Big Data pipeline starts from various sensor and video streams to which AI and feature extraction methods are applied. The resulting information is stored in database containers, which have to be placed on Edge, Fog or Cloud infrastructures. The placement decision depends on complex application requirements, including Quality of Service (QoS) requirements. Information that must be considered when making placement decisions includes the expected workload, the list of candidate infrastructures, geolocation, connectivity and similar. Software engineers currently perform such decisions manually, which usually leads to QoS threshold violations. This paper aims to automate the process of making such decisions.Therefore, the goals of this paper are to: (1) develop a decision making method for database container placement; (2) formally verify each placement decision and provide probability assurances to the software engineer for high QoS; and (3) design and implement a new architecture that automates the whole process.A new optimisation method is introduced, which is based on the theory and practice of stochastic Markov Decision Processes (MDP). It uses as input monitoring data from the container runtime, the expected workload and user-related metrics in order to automatically construct a probabilistic finite automaton. The generated automaton is used for both automated decision making and placement success verification. The method is implemented in Java. It also uses the PRISM model-checking tool. Kubernetes is used in order to automate the whole process when orchestrating database containers across Edge, Fog and Cloud infrastructures.Experiments are performed for NoSQL Cassandra database containers for three representative workloads of 50000 (workload 1), 200000 (workload 2) and 500000 (workload 3) CRUD database operations. Five computing infrastructures serve as candidates for database container placement. The new MDP-based method is compared with the widely used Analytic Hierarchy Process (AHP) method. The obtained results are used to analyse container placement decisions. When using the new MDP based method there were no QoS violations in any of the placement cases, while when using the AHP based method the placement results in some QoS threshold violations in all workload cases. Due to its properties, the new MDP method is particularly suitable for implementation.The paper also describes a multi-tier distributed computing system that uses multi-level (infrastructure, container, application) monitoring metrics and Kubernetes in order to orchestrate database containers across Edge, Fog and Cloud ...

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