Resilience of Stateful IoT Applications in a Dynamic Fog Environment

International audienceFog 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 reconfig-ure 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

Edge Computing for Extreme Reliability and Scalability

The massive number of Internet of Things (IoT) devices and their continuous data collection will lead to a rapid increase in the scale of collected data. Processing all these collected data at the central cloud server is inefficient, and even is unfeasible or unnecessary. Hence, the task of processing the data is pushed to the network edges introducing the concept of Edge Computing. Processing the information closer to the source of data (e.g., on gateways and on edge micro-servers) not only reduces the huge workload of central cloud, also decreases the latency for real-time applications by avoiding the unreliable and unpredictable network latency to communicate with the central cloud

Designing and Implementing Resilient IoT Applications in the Fog: A Smart Home Use Case

International audienceFog 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

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology : Future Perspectives

The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.Peer reviewe

A survey on mobility-induced service migration in the fog, edge, and related computing paradigms

The final publication is available at ACM via http://dx.doi.org/10.1145/3326540With the advent of fog and edge computing paradigms, computation capabilities have been moved toward the edge of the network to support the requirements of highly demanding services. To ensure that the quality of such services is still met in the event of users’ mobility, migrating services across different computing nodes becomes essential. Several studies have emerged recently to address service migration in different edge-centric research areas, including fog computing, multi-access edge computing (MEC), cloudlets, and vehicular clouds. Since existing surveys in this area focus on either VM migration in general or migration in a single research field (e.g., MEC), the objective of this survey is to bring together studies from different, yet related, edge-centric research fields while capturing the different facets they addressed. More specifically, we examine the diversity characterizing the landscape of migration scenarios at the edge, present an objective-driven taxonomy of the literature, and highlight contributions that rather focused on architectural design and implementation. Finally, we identify a list of gaps and research opportunities based on the observation of the current state of the literature. One such opportunity lies in joining efforts from both networking and computing research communities to facilitate future research in this area.Peer ReviewedPreprin

V-Edge: Virtual Edge Computing as an Enabler for Novel Microservices and Cooperative Computing

As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: Instead of sending all data and tasks from an end user's device to the cloud, possibly covering thousands of kilometers and introducing delays lower-bounded by propagation speed, edge servers deployed in close proximity to the user (e.g., at some base station) serve as proxy for the cloud. This is particularly interesting for upcoming machine-learning-based intelligent services, which require substantial computational and networking performance for continuous model training. However, this promising idea is hampered by the limited number of such edge servers. In this article, we discuss a way forward, namely the V-Edge concept. V-Edge helps bridge the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available. Thus, V-Edge acts as an enabler for novel microservices as well as cooperative computing solutions in next-generation networks. We introduce the general V-Edge architecture, and we characterize some of the key research challenges to overcome in order to enable wide-spread and intelligent edge services

Continuous QoS-compliant Orchestration in the Cloud-Edge Continuum

The problem of managing multi-service applications on top of Cloud-Edge networks in a QoS-aware manner has been thoroughly studied in recent years from a decision-making perspective. However, only a few studies addressed the problem of actively enforcing such decisions while orchestrating multi-service applications and considering infrastructure and application variations. In this article, we propose a next-gen orchestrator prototype based on Docker to achieve the continuous and QoS-compliant management of multiservice applications on top of geographically distributed Cloud-Edge resources, in continuity with CI/CD pipelines and infrastructure monitoring tools. Finally, we assess our proposal over a geographically distributed testbed across Italy.Comment: 25 pages, 8 figure