Karl Michael Göschka

Functions as a Service (FaaS) represents a recent advancement in the cloud computing paradigm, offering a fine-grained deployment model that enables the execution of individual functions on cloud infrastructure. This paradigm shift promises enhanced scalability and potential cost efficiencies by leveraging the sophisticated, geographically distributed infrastructure of cloud service providers, thereby optimizing performance through minimized latency. This paper systematically explores the critical considerations for adopting FaaS, focusing on challenges such as provider selection, performance optimization, inter-function communication, and dependency management. Through a qualitative research methodology, including a comprehensive literature review and experiential insights from serverless computing, this study provides both theoretical insights and practical guidelines. The findings aim to contextualize current academic discussions and niche approaches within the broader scope of FaaS, contributing to a deeper understanding of its implications. Specific recommendations include refactoring existing applications to resolve performance bottlenecks, strategies to avoid vendor lock-in, and minimizing latency in FaaS architectures.

Function as a Service is an established option to deploy event-driven code fragments. Its scalability and cost efficiency through pay-per-use pricing models make it particularly interesting for many use cases. Its isolated nature and statelessness demand rethinking regular software engineering patterns. Communication between several functions is different than it would be in a monolith application. Most functions do not require inter-function exchanges; yet when necessary, options are limited. Leveraging additional services of a cloud service provider increases vendor lock-in. This paper contributes with an approach to enable direct communication between serverless functions, utilizing TCP hole punching. The three largest cloud service providers were tested on applicability and optimal settings. It turned out that TCP hole punching only works with Amazon Web Services, but not in FaaS offerings on Microsoft Azure or Google Cloud. When comparing TCP hole punching with AWS storage service S3, TCP hole punching was significantly faster for data transfers larger than 50 MB. Using the rendezvous server as a relay showed similar performance but is problematic scaling-wise.

The use of fault detection and tolerance measures in wireless sensor networks is inevitable to ensure the reliability of the data sources. In this context, immune-inspired concepts offer suitable characteristics for developing lightweight fault detection systems, and previous works have shown promising results. In this article, we provide a literature review of immune-inspired fault detection approaches in sensor networks proposed in the last two decades. We discuss the unique properties of the human immune system and how the found approaches exploit them. With the information from the literature review extended with the findings of our previous works, we discuss the limitations of current approaches and consequent future research directions. We have found that immune-inspired techniques are well suited for lightweight fault detection, but there are still open questions concerning the effective and efficient use of those in sensor networks.

In wireless sensor networks, the quality of the provided data is influenced by the properties of the sensor nodes. Often deployed in large numbers, they usually consist of low-cost components where failures are the norm, even more so in harsh outdoor environments. Current fault detection techniques, however, consider the sensor data alone and neglect vital information from the nodes’ hard- and software. As a consequence, they can not distinguish between rare data anomalies caused by proper events in the sensed data on one side and fault-induced data distortion on the other side. In this paper, we contribute with a novel, open-source sensor node platform for monitoring applications such as environmental monitoring. For long battery life, it comprises mainly low-power components. In contrast to other sensor nodes, our platform provides self-diagnostic measures to enable active node-level reliability. The entire sensor node platform including the hardware and software components has been implemented and is publicly available and free to use for everyone. Based on an extensive and long-running practical experiment setup, we show that the detectability of node faults is improved and the distinction between rare but proper events and fault-induced data distortion is indeed possible. We also show that these measures have a negligible overhead on the node’s energy efficiency and hardware costs. This improves the overall reliability of wireless sensor networks with both, long battery life and high-quality data.