This paper introduces ZEROTuNE, a novel cost model for parallel and distributed stream processing that can be used to effectively set initial parallelism degrees of streaming queries. Unlike existing models, which rely majorly on online learning statistics that are non-transferable, context-specific, and require extensive training, ZEROTuNE proposes data-efficient zero-shot learning techniques that enable very accurate cost predictions without having observed any query deployment. To overcome these challenges, we propose ZEROTuNE, a graph neural network architecture that can learn from the structural complexity of parallel distributed stream processing systems, enabling them to adapt to unseen workloads and hardware configurations. In our experiments, we show when integrating ZEROTuNE in a distributed streaming system such as Apache Flink, we can accurately set the degree of parallelism, showing an average speed-up of around 5× in comparison to existing approaches.

The centralized control plane in Software-Defined Networking (SDN) offers significant advancements in network management capabilities. However, SDN is also susceptible to cybersecurity risks and vulnerabilities. Deploying the Machine Learning (ML) approach in an Intrusion Detection System (IDS) can facilitate early detection of potential vulnerabilities. However, deploying an ML-based IDS solely in either the SDN control plane or the data plane has its benefits and drawbacks. For instance, a high-capacity ML model deployed in the control plane can enhance the detection performance but may increase network latency and the risk of overwhelming the control plane. In contrast, lightweight ML models deployed in the data plane could accelerate intrusion detection with lower detection performance. However, a functional IDS should provide a good detection performance at a line rate. To accomplish these objectives, we introduce a novel method called Collaborative ML-based IDS (CML-IDS), which involves deploying ML models in both the control and data planes to detect network attacks collaboratively. To facilitate this collaboration, we assess the confidence of the classification model, which is flexibly deployed within the programmable data plane. Our evaluation results demonstrate that the CML-IDS enhances the average intrusion detection performance to 93.46% and reduces the misclassification rate by 54.66% when compared to an IDS that solely relies on the ML model deployed in the data plane. Furthermore, CML-IDS effectively reduces network latency caused by forwarding flows to the control plane.

Crowd sensing exploits users' smart devices and human mobility to collect information on a large scale. To realize a crowd sensing campaign, sensing tasks with spatio-temporal requirements are distributed to the devices that can provide the requested information. Typically, the distribution of sensing tasks relies on a centralized communication infrastructure such as cloud servers. However, such an approach will be unsuitable if access to communication infrastructure is restricted, for example in disaster relief scenarios. To fill this gap, we propose a distributed context-aware framework for disseminating sensing tasks, based on the Named Data Networking (NDN) paradigm. By adding context attributes to Interest packets, we allow a device to utilize this information to make forwarding decisions autonomously, thus guiding the sensing tasks towards the suitable sensing devices. Through intensive evaluation, we show that our framework achieves a timely delivery of sensing tasks, while keeping the communication overhead to a minimum compared to pure geo forwarding and flooding approaches.