Adaptive Aggregation of Flow Records

This paper explores the problem of processing the immense volume of measurement data arising during network traffic monitoring. Due to the ever-increasing demands of current networks, observing accurate information about every single flow is virtually infeasible. In many cases the existing methods for the reduction of flow records are still not sufficient enough. Since the accurate knowledge of flows termed as "heavy-hitters" suffices to fulfill most of the monitoring purposes, we decided to aggregate the flow records pertaining to non-heavy-hitters. However, due to the ever-changing nature of traffic, their identification is a challenge. To overcome this challenge, our proposed approach - the adaptive aggregation of flow records - automatically adjusts its operation to the actual traffic load and to the monitoring requirements. Preliminary experiments in existing network topologies showed that adaptive aggregation efficiently reduces the number of flow records, while a significant proportion of traffic details is preserved

Towards Machine Learning-based Anomaly Detection on Time-Series Data

The complexity of network infrastructures is exponentially growing. Real-time monitoring of these infrastructures is essential to secure their reliable operation. The concept of telemetry has been introduced in recent years to foster this process by streaming time-series data that contain feature-rich information concerning the state of network components. In this paper, we focus on a particular application of telemetry — anomaly detection on time-series data. We rigorously examined state-of-the-art anomaly detection methods. Upon close inspection of the methods, we observed that none of them suits our requirements as they typically face several limitations when applied on time-series data. This paper presents Alter-Re2, an improved version of ReRe, a state-of-the-art Long Short- Term Memory-based machine learning algorithm. Throughout a systematic examination, we demonstrate that by introducing the concepts of ageing and sliding window, the major limitations of ReRe can be overcome. We assessed the efficacy of Alter-Re2 using ten different datasets and achieved promising results. Alter-Re2 performs three times better on average when compared to ReRe

Balancing Information Preservation and Data Volume Reduction: Adaptive Flow Aggregation in Flow Metering Systems

The critical role of network traffic measurement and analysis extends across a range of network operations, ensuring quality of service, security, and efficient resource management. Despite the ubiquity of flow-level measurement, the escalating size of flow entries presents significant scalability issues. This study explores the implications of adaptive gradual flow aggre- gation, a solution devised to mitigate these challenges, on flow information distortion. The investigation maintains flow records in buffers of varying aggregation levels, iteratively adjusted based on the changing traffic load mirrored in CPU and memory utilization. Findings underscore the efficiency of adaptive gradual flow aggregation, particularly when applied to a specific buffer, yielding an optimal balance between information preservation and memory utilization. The paper highlights the particular significance of this approach in Internet of Things (IoT) and contrasted environments, characterized by stringent resource constraints. Consequently, it casts light on the imperative for adaptability in flow aggregation methods, the impact of these techniques on information distortion, and their influence on network operations. This research offers a foundation for future studies targeting the development of more adaptive and effective flow measurement techniques in diverse and resource-limited network environments

Quality-enabled decentralized IoT architecture with efficient resources utilization

Internet of Things (IoT) is a paradigm aimed at connecting everyday objects to the internet. IoT applications include smart cities, healthcare, agriculture, as well as the industry and manufacturing. The ability to monitor and control the physical world using information technology creates many opportunities. However, it also comes with some costs. The exponential growth of connected devices, the heterogeneity of IoT use cases, and the diversity of the network technologies yield a concern regarding IoT sustainability. With this work, we aim to contribute to this concern. In doing so, we introduce a novel representation model that is destined for (i) monitoring the IoT environment at runtime, (ii) expressing the overall quality of the system, and (iii) helping to utilize the available resources efficiently. We also define a feature set that describes the best the expectations of decentralized IoT platforms. Furthermore, we describe a quality-enabled decentralized IoT architecture too that incorporates the specified feature set as well as our representation model. Such solutions are necessary to improve and maintain IoT of the future and all its application domains, including the Industrial Internet of Things (IIoT). With the presented research, we aim to encourage the efficient utilization of resources and simplify the production of next-generation IoT solutions.Web of Science67art. no. 10200