[12] proposes the Cloudcompaas platform that allows representation, scheduling and management of Cloud resources. The proposed platform features an extension of the SLA specification, called WS-Agreement, which enables Cloud providers with a generic SLA model to deal with higher-level QoS metrics, closer to end-user perception. [PERSON] et al. [13] described an SLA-driven deduplication framework for large-scale cloud storage systems. The proposed solution is composed of an SLA notation and deduplication mechanism that facilitates discovery and removal of abundant data in the Cloud, through a performance-cost trade-off to maintain high deduplication quality and meets the necessary SLA. [PERSON] et al. [14] presented a resource scheduling algorithm for efficient exploitation of cloud storage. The described solution utilizes throughput and storage space as constraints in the decision making for the scheduling process. Its main objective is to reduce the SLA violation rate for cloud storage services. [PERSON] et al. [15] studied the trade-off between energy efficiency and performance. They stated that a data centre hosting the Cloud environment is likely to achieve greater energy efficiency (at a reduced cost) compared to a local deployment. With increasing energy prices, it is estimated that a large percentage of operational costs within a Cloud environment can be attributed to energy. This work provides some insight on the relationship between power consumption and QoS related metrics, describing how a combined consideration of these two metrics could be supported for a particular workload. [PERSON] et al. [16] presented an energy-aware multi-start local search algorithm that optimises energy consumption of an OpenNebula-based Cloud. This study proposes the implementation of Pareto Multi-Objective version to deal with both the energy consumption and the SLA. The objective is to find a Pareto trade-off between reducing the energy consumption of the Cloud while preserving the performance of VMs. [PERSON] et al. [17] proposed a FogStore system, which allows distributing and storing the data of stateful applications in the Fog. Its baseline algorithm utilises Fog-aware requirement (i.e., network latency) and data-context requirements (i.e., geolocation and scenario type) to place the data as close as possible to their clients, and to also place the replicas in close proximity to each other. Similarly, [PERSON] et al. [18] presented vStore, which is a framework that facilitates context-aware placement of mobile user data in the Cloud. It uses a rules engine to place and locate data based on its context metadata. This paper complements the above efforts by delivering a new software engineering technique for dynamic orchestration of containerised databases and file systems across the overall Things-to-Cloud computing continuum. It is supported by DECENTER's novel Kubernetes-based orchestration technology and its Ethereum-based resource brokerage platform. Our solution of Fog Storage Services consists of a novel SLA specification and Pareto front decision-making method that facilitate orchestration of database and file system containers across the Things-to-Cloud computing continuum. The contributions of this work can be summarised as follows:  
* A new Fog Storage Services (an infrastructure layer) for database and file systems storage operations;
* A new SLA specification that is used in the orchestration of Fog storage containers;
* A new Pareto-based decision-making method for the placement of Fog storage containers (e.g., containerised databases and file systems) which is used to provide specific QoS guarantees. This paper is organised as follows. In Section 1, we presented the background for our research and in the remainder, we review related research in the area of SLAs in Fog computing. Section 2 presents the research methodology. Section 3 explains the infrastructure for our proposed data storage services.