Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. Although the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, such as to increase performance and to make a running system more robust. We instantiate and empirically examine the preceding idea for software-defined networking—a key enabling technology for modern data centers and Internet of Things applications. Using genetic programming (GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open source synthetic and industrial data, indicates that compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, it reduces the frequency of adaptation interventions over time. In addition, we show that for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss.

The Internet of Things (IoT) connects a plethora of smart devices globally across various applications like smart cities, autonomous vehicles, and health monitoring. Simulation plays a key role in the testing of IoT systems, noting that field testing of a complete IoT product may be infeasible or prohibitively expensive. This paper addresses a specific yet important need in simulation-based testing for IoT: Stress testing of cloud systems that are increasingly employed in IoT applications. Existing stress testing solutions for IoT demand significant computational resources, making them ill-suited and costly. We propose a lean simulation framework designed for IoT cloud stress testing. The framework enables efficient simulation of a large array of IoT and edge devices that communicate with the cloud. To facilitate simulation construction for practitioners, we develop adomain-specific language (DSL), named IoTECS, for generating simulators from model-based specifications. We provide the syntax and semantics of IoTECS and implement IoTECS using Xtext and Xtend. We assess simulators generated from IoTECS specifications for stress testing two real-world systems: a cloud-based IoT monitoring system developed by our industry partner and an IoT-connected vehicle system. Our empirical results indicate that simulators created using IoTECS: (1) achieve best performance when configured with Docker containerization; (2) effectively assess the service capacity of our case-study systems, and (3) outperform industrial stress-testing baseline tools, JMeter and Locust, by a factor of 3.5 in terms of the number of IoT and edge devices they can simulate using identical hardware resources. To gain initial insights about the usefulness of IoTECS in practice, we interviewed two engineers from our industry partner who have firsthand experience with IoTECS. Feedback from these interviews suggests that IoTECS is effective in stress testing IoT cloud systems, saving significant time and effort.

The Internet of things (IoT) is increasingly prevalent in domains such as emergency response, smart cities and autonomous vehicles. Simulation plays a key role in the testing of IoT systems, noting that field testing of a complete IoT product may be infeasible or prohibitively expensive. In this paper, we propose a domain-specific language (DSL) for generating edge-to-cloud simulators. An edge-to-cloud simulator executes the functionality of a large array of edge devices that communicate with cloud applications. Our DSL, named IoTECS, is the result of a collaborative project with an IoT analytics company, Cheetah Networks. The industrial use case that motivates IoTECS is ensuring the scalability of cloud applications by putting them under extreme loads from IoT devices connected to the edge. We implement IoTECS using Xtext and empirically evaluate its usefulness. We further reflect on the lessons learned.

Internet of Things (IoT) is a pivotal technology in application domains that require connectivity and interoperability between large numbers of devices. IoT systems predominantly use a software-defined network (SDN) architecture as their core communication backbone. This architecture offers several advantages, including the flexibility to make IoT networks self-adaptive through software programmability. In general, self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this paper, we argue that, rather than generating individual adaptation strategies, the goal should be to adapt the logic / code of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. We instantiate and empirically assess this idea in the context of IoT networks. Specifically, using genetic programming (GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of SDN-based IoT networks. Our evaluation, performed using open-source synthetic and industrial data, indicates that, compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, reduces the frequency of adaptation interventions over time. In addition, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss.