This paper presents TimeLSB, a covert channel built on TCP timestamps using extended Berkeley Packet Filter (eBPF) technology. The channel encodes information by modifying the least significant bit of timestamp values through a CRC32-based scheme in the Linux kernel, while a passive receiver reconstructs the hidden message. We implement and evaluate this channel under a range of controlled network impairments, demonstrating that embedding covert bits does not significantly alter the behavior of normal TCP connections. The eBPF implementation introduces only on average 250 ns of processing overhead per packet, which corresponds to several million packets per second and is negligible compared to typical forwarding performance. Finally, we analyze detectability using the distinct-timestamp ratio, showing that while absolute values differ from prior work, the metric still provides a stable separation between covert and normal flows. These results highlight the practicality of an eBPFbased covert channel and provide defensive insights for network security operations.
Xtended Reality (XR) and holographic telepresence place stringent Quality of Service (QoS) demands on network infrastructure, requiring ultra-low latency, high throughput, and reliable connectivity. Meeting such QoS demands is critical in dynamic, distributed cloud environments, but does not always guarantee a satisfactory user experience. Quality of Experience (QoE) captures the user’s perception of service performance, which may be influenced by factors not fully reflected in systemlevel metrics. Thus, novel orchestration strategies must consider both QoS and QoE. This paper proposes a Reinforcement Learning (RL)-driven approach to edge-cloud orchestration capable of adapting to dynamic network conditions, leveraging a multiobjective reward function, including both QoS and QoE aspects, to guide service placement decisions. Evaluation shows that our RL approach reaches a 21.3% QoE gain over heuristics and 14.7% over balanced strategies, with 100% request acceptance. The results highlight the robustness and scalability of RL-driven orchestration, particularly for latency-sensitive 6 G applications. Our findings also reveal the limitations of traditional heuristics under complex objectives and highlight the potential of RL as a transformative tool for intelligent network and service management in next-generation communication systems.
The ever-increasing automation of complex decisionmaking processes through Operations Research (OR) raises the need for specialized management systems. To support multiple vertical industries, OR service providers must handle a diverse portfolio of solvers having widely varying resource, computational, and availability requirements. To address this fundamental challenge, we present Octopus, a state-of-the-art cloud-native solver orchestrator. Leveraging Kubernetes, Octopus features event-driven autoscaling to align resources with application demand, dedicated queue management to meet service-level agreements, and robust reliability mechanisms to prevent service interruptions. This demonstration showcases Octopus’s capabilities in a production-like environment, illustrating its full end-to-end workflow and commenting on its real-time performance.
Existing traffic steering mechanisms often struggle to meet the dynamic requirements of today’s applications. This paper demonstrates a novel approach that leverages Segment Routing over IPv6 (SRv6) and Intent-Based Networking (IBN) principles to enable advanced, application-aware end-to-end traffic engineering between clients and servers. The solution illustrates how high-level, application-specific traffic steering intents are seamlessly translated into dynamic network instructions, thereby eliminating the need for complex, manual network steering policies. The system further incorporates standardized protocols to enable real-time telemetry data collection and analysis, pioneering next-level traffic engineering capabilities that were previously unachievable. This demonstration showcases the practical feasibility of this approach through targeted use cases, leveraging its open-source foundation, and highlights the significant benefits of creating highly dynamic networks that autonomously adapt to changing conditions and application requirements.
Programmable data plane devices have enabled various in-network applications that rely on locally stored state for delivering low-latency and high-throughput services. However, these applications are susceptible to network failures, which can disrupt state access and network functionality. Timely and reliable failure detection is therefore a critical component of a stateful data plane. In this paper, we propose a data plane framework, FASTR, that enables microsecond-scale fast failure detection between directly connected switches. FASTR can achieve sub- $10 \mu$ s detection latency by implementing a heartbeat mechanism in the data plane. In addition, FASTR also incorporates traffic-awareness to reduce overhead and priority queuing to avoid false alarms. We validate FASTR with hardware experiments, demonstrating that it can consistently detect failures within $10 \mu$ s using a $4 \mu$ s interval while remaining robust to network congestion.
