However, the resource sharing model and virtualisation enable lower costs of data centre operation while serving more customers at the same time. Hence, there are two opposing sides, the customers who wish to obtain best possible QoS for their virtual resources, and the service providers who wish to achieve lowest possible operational costs. Considering that customers pay for the services offered by the providers, they must be provided with assurance on the QoS, similarly to the case of buying a physical product, which includes a warranty. On-demand computing and storage providers usually offer service guarantees under certain terms, commonly referred to as SLAs. SLAs are agreements between service providers and customers, specifying Service-Level Objectives (SLOs) as rates and conditions about the operability and performance of specific services, usually within certain time intervals, together with SLA violations and SLA exclusions. SLA violations define under what terms an SLA does not meet the agreed objectives and what legitimate actions the customer can take when a violation occurs. In case of SLA violations, the providers usually compensate the affected customers by offering discounted prices in the next billing period. SLA exclusions specify the terms and conditions under which the the providers are exempted from the compensations caused by the violations. Until now, SLA specifications have been investigated in relation to offered computing infrastructures as a service. To the best of our knowledge, this is the first work that introduces an SLA specification and SLA management approach for Fog Storage Services, which includes, but is not limited to, structured databases and file system operations. In the remainder of this section we summarize the research regarding SLAs, especially targeting SLAs and their specification in Fog Computing. For our work, approaches, solutions and techniques for data management operations close to the Edge of the network are of particular interest. The State-of-the-Art review results are presented in Table 1. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Work** & **QoS Attributes** & **SLA Impl.** & **SLA Spec.** & **Type of Nodes** & **Utility** \\ \hline [PERSON] and [PERSON] (2019) & Reliability Availability User satisfaction & Yes & No & Cloud & Data storage and processing \\ \hline [PERSON] et al. (2015) & Cost Reputation Availability Response & Yes & No & Cloud & Data storage and processing \\ \hline [PERSON] et al. (2016) & Cost Reliability Availability Response & Yes & Yes & Cloud & Data storage and processing \\ \hline [PERSON] et al. (2015) & QoS Cost Time complexity Network throughput & Yes & No & Cloud & Data Processing \\ \hline [PERSON] et al. (2012) & RTFTtch-Duration Average PacketLatency ClientConnectionCount & Yes & No & Cloud & Data storage and processing \\ \hline [PERSON] et al. (2014) & Cost Number of failed requests & Yes & Yes & Cloud & Data storage and processing \\ \hline [PERSON] et al. (2020) [13] & Cost I/O performance & Yes & Yes & Cloud & Data storage \\ \hline [PERSON] et al. (2020) [14] & Storage Throughput & Yes & No & Cloud & Data storage \\ \hline [PERSON] et al. (2016) & Cost Energy-efficiency & Yes & No & Cloud & Data processing \\ \hline [PERSON] et al. (2014) & Cost Energy-efficiency & Yes & No & Cloud & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: State-of-the-Art analysis summary for Fog storage. Works are compared with respect to the QoS attributes, the SLA implementation, the SLA specification, the type of nodes and the utility. There are many relevant challenges related to Fog computing [19; 20]; however, the focus of our research is narrowed down to assuring high QoS for highly dynamic storage operations, which may be used in the emerging IoT and smart city scenarios (e.g., smart applications executing low-latency operations). Such applications require high QoS for successful real-time processing of Big Data.