Microservices and serverless functions – lifecycle, performance, and resource utilisation of edge based real-time IoT analytics

Edge Computing harnesses resources close to the data sources to reduce end-to-end latency and allow real-time process automation for verticals such as Smart City, Healthcare and Industry 4.0. Edge resources are limited when compared to traditional Cloud data centres; hence the choice of proper resource management strategies in this context becomes paramount. Microservice and Function as a Service architectures support modular and agile patterns, compared to a monolithic design, through lightweight containerisation, continuous integration / deployment and scaling. The advantages brought about by these technologies may initially seem obvious, but we argue that their usage at the Edge deserves a more in-depth evaluation. By analysing both the software development and deployment lifecycle, along with performance and resource utilisation, this paper explores microservices and two alternative types of serverless functions to build edge real-time IoT analytics. In the experiments comparing these technologies, microservices generally exhibit slightly better end-to-end processing latency and resource utilisation than serverless functions. One of the serverless functions and the microservices excel at handling larger data streams with auto-scaling. Whilst serverless functions natively offer this feature, the choice of container orchestration framework may determine its availability for microservices. The other serverless function, while supporting a simpler lifecycle, is more suitable for low-invocation scenarios and faces challenges with parallel requests and inherent overhead, making it less suitable for real-time processing in demanding IoT settings

Modeling and emulation of an osmotic computing ecosystem using OsmoticToolkit

Digital services are increasingly becoming cyber-physical and osmotic, combining Cloud resources with Fog, Edge, and IoT devices. This trend can be observed in the e-health domain or in smart city applications where the location of software deployments and data processing matters. Before such applications go live, careful planning with real system emulation is necessary. We claim that the OsmoticToolkit, although in the early stages, is the first emulation environment designed to address this challenge. In this paper, we introduce the emulator’s functionalities and validate experimentally with an e-health scenario, using a reference deployment of a microservice-based hospital application. The experimental results carried out show its effectiveness providing valuable support for understanding the impact on resources, workloads, and Quality of Service requirements within Cloud-Edge/Fog-IoT scenarios while preserving the users’ Service Level Agreements (SLAs)

Rule-based resource matchmaking for composite application deployments across IoT-fog-cloud continuums

Where shall my new shiny application run? Hundreds of such questions are asked by software engineers who have many cloud services at their disposition, but increasingly also many other hosting options around managed edge devices and fog spectrums, including for functions and container hosting (FaaS/CaaS). Especially for composite applications prevalent in this field, the combinatorial deployment space is exploding. We claim that a systematic and automated approach is unavoidable in order to scale functional decomposition applications further so that each hosting facility is fully exploited. To support engineers while they transition from cloud-native to continuum-native, we provide a rule-based matchmaker called RBMM that combines several decision factors typically present in software description formats and applies rules to them. Using the MaestroNG orchestrator and OsmoticToolkit, we also contribute an integration of the matchmaker into an actual deployment environment

A Map-Reduce Approach for the Dijkstra Algorithm in SDN Over Osmotic Computing Systems

Osmotic Computing represents a glue solution able to manage the deployment and orchestration of interconnected microelements across heterogeneous physical and virtual infrastructures (e.g., IoT, Edge and Cloud nodes) according to the behavior of hardware and software components during the time. The adoption of Osmotic Computing is challenging, but addressing networking issues is a key research topic due to the emergence of new problems in terms of QoS requirements. In this paper, we analyze how to exploit well-known networking solutions, such as the Dijkstra’s algorithm, and Big Data oriented technologies, such as the Hadoop and MapReduce, to provide efficient newtorking functionalities in Osmotic Computing. In particular, our objective is to minimize the routing path computation time in the software defined network (SDN) at the basis of microelement networking, as well as to ensure a global view and a high level of dynamism of our network topology. To accomplish this task, we process routing tables through a MapReduce based implementation of the Dijkstra’s algorithm whenever a topology change occurs, and we export routing results into the SDN. Our experimental results show that our networking strategy drastically reduces the best path computation time whenever the network of microelements is very large