Fog computing provides computing, storage and communication resources at the edge of the network, near the physical world. Subsequently, end devices nearing the physical world can have interesting properties such as short delays, responsiveness, optimized communications and privacy. However, these end devices have low stability and are prone to failures. There is consequently a need for failure management protocols for IoT applications in the Fog. The design of such solutions is complex due to the specificities of the environment, i.e., (i) dynamic infrastructure where entities join and leave without synchronization, (ii) high heterogeneity in terms of functions, communication models, network, processing and storage capabilities, and, (iii) cyber-physical interactions which introduce non-deterministic and physical world's space and time dependent events. This paper presents a fault tolerance approach taking into account these three characteristics of the Fog-IoT environment. Fault tolerance is achieved by saving the state of the application in an uncoordinated way. When a failure is detected, notifications are propagated to limit the impact of failures and dynamically reconfigure the application. Data stored during the state saving process are used for recovery, taking into account consistency with respect to the physical world. The approach was validated through practical experiments on a smart home platform.

Fog computing extends the capacities of the cloud to the edge of the network, near the physical world, so that Internet of Things (IoT) applications can benefit from properties such as short delays, real-time and privacy. Devices in the Fog-IoT environment are usually unstable and prone to failures. In this context, the consequences of failures may impact the physical world and can, therefore, be critical. This paper reports a framework for end-to-end resilience of Fog-IoT applications. The framework was implemented and experimented on a smart home testbed.

Fog Computing is especially appealing to the Internet of Things (IoT) because it provides computing, storage, and communication resources at the edge of the network, near the physical world (PW). Thus, IoT located in the PW can have interesting properties such as low latencies, real-time operations, and data privacy. The Fog, however, is unstable because it is constituted of billions of devices in a dynamic environment. Moreover, the Fog is cyber-physical and devices are thus subjected to external PW conditions which increase the occurrence of failures. When failures occur in such an environment, the resulting consequences on the PW can be hazardous and costly. This paper presents F 3 ARIoT, a framework for autonomic resilience of IoT applications in the Fog. This framework recovers from failures as well as maintains consistency and safety with respect to the PW during the recovery procedure. F 3 ARIoT was implemented and evaluated on a smart home application. A performance evaluation showed that it has a negligible overhead and recovers from failures in a very short delay with respect to end-users.

Fog computing provides computing, storage and communication resources at the edge of the network, near the physical world. Devices deployed in the Fog have interesting properties such as short delays, responsiveness, optimised communications and privacy. However, these devices have low stability and are prone to failures. Thus, there is a need for management protocols to tolerate failures of IoT applications in the Fog. We propose a failure management protocol which recovers from failures of devices and software elements involved in an IoT application. Designing such highly distributed management protocols is a difficult and error-prone task. Therefore, the main contribution of this paper is the formal specification and verification of this failure management protocol. Formal specification is achieved using a process algebraic language. The corresponding formal model was used to carry out extensive analysis of the protocol to ensure that it preserves important architectural invariants and functional properties. The verification step was performed using model checking techniques. The analysis of the protocol was a success because it allowed us to detect and correct several issues in the protocol.

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