# End-to-End Service Availability in Heterogeneous Multi-Tier Cloud-Fog-Edge Networks
## 2 Related Works  
In recent years, the evolving landscape of geographically distributed networks, especially with the integration of cloud, fog, and edge computing, has received considerable attention in both academia and industry. The intersection of these architectures with Quality of Service (QoS), specifically availability, has also been a focal point of numerous studies. This section delves into the seminal and contemporary works that have laid the groundwork for the current understanding and guided the research presented in this article. Active research is underway exploring architectures, orchestration, optimization, availability modeling, fault tolerance, and security specifically for emerging multi-tier computing ecosystems. Next-generation communication networks will need to handle resource-intensive applications for numerous users. While cloud computing can assist with offloading these tasks, its centralized nature results in significant communication delays, making it unsuitable for emerging delay-sensitive applications. The edge and/or cloud can be combined to facilitate the task offloading problem. The authors of [1] presented a fundamental survey on integrating edge and cloud computing to navigate the challenges of task offloading, emphasizing diverse optimization strategies. The comprehensive review underscored the intrinsic importance of this topic, concluding with open challenges and avenues for future research. The main research directions and publications related to multi-level cloud-fog-edge computing architectures and assessing their reliability and availability include different topics:  
* Architectures and methodologies for integrating cloud, fog, and edge layers into unified platforms for seamless operation and resource orchestration, e.g., OpenFogConsortium reference architecture [2], multi-access edge computing standards of the European Standards Organization [3], fog computing and networking theory, practice, and applications [4]. * Management and virtualization techniques to dynamically allocate resources across discrete layers, e.g., software-defined networking approaches [5]. * Programming models and application development approaches tailored for distributed multi-tier environments, e.g., workflow partitioning techniques in a fog application placement mechanism under the requirement of QoS satisfaction degree [6; 7]. * Performance modeling and optimization, e.g., analytically modeling attributes like latency, reliability, and power consumption to optimize tiers [8; 9; 10]. * Assessing availability and reliability of multi-tier applications using probabilistic models to combine individual tier reliabilities [11; 12; 13; 14]. * Machine learning techniques to predict reliability from system data and model correlations between tiers [15; 16; 17]. * Hardware/software fault tolerance mechanisms tailored to heterogeneous distributed architectures to enhance reliability [18; 19; 20]. * Security, privacy, and trust considerations in interconnected environments spanning administrative domains with decentralized data [21; 22; 23]. There are many publications with real-world case studies and applications of multi-tiered computing systems. The authors of [24] proposed an architectural and implementational model for real-time hardware monitoring and management to enhance security in heterogeneous networks. The system used symmetrical design to allow data wrapping and transport across diverse operating systems. A multi-layered architecture was proposed in [25], using the Modbus TCP protocol to integrate heterogeneous hardware and software components for deploying experimental smart grids and microgrids with photovoltaic integration. An application of the photovoltaic-based microgrid demonstrated and validated the architecture.