Cloud Gaming (CG) research faces challenges due to the unpredictability of game engines and restricted access to commercial platforms and their logs. This creates major obstacles to conducting fair experimentation and evaluation. CGReplay captures and replays player commands and the corresponding video frames in an ordered and synchronized action-reaction loop, ensuring reproducibility. It enables Quality of Experience/Service (QoE/QoS) assessment under varying network conditions and serves as a foundation for broader CG research. The code is publicly available for further development11https://github.com/dcomp-leris/CGReplay.git.

Network demand for real-time applications like self-driving cars and cloud gaming strains existing networks. Latency and congestion hurt user experience. Realistic testing is vital to improving networks, but real-world data is scarce. In this context, we propose to analyze existing network data and identify traffic patterns and anomalies. We believe this knowledge can be used to feed generative adversarial network (GAN) models, which can create realistic synthetic data, supplementing existing real traces while protecting end-user privacy. This augmented data can then be used, for instance, to empower improved routing algorithms designed to benefit from programmable hardware (e.g., SmartNICs) and collected data plane metrics, paving the way for improved network performance and enhanced user experience with more autonomous decisions. This paper presents our initial analysis of synthetic network data generation technologies and summarizes the main ideas guiding my Ph. D. research.

In recent years, the increasing demand for network resources due to real-time applications and data-intensive activities has posed challenges in managing and optimizing network performance. To assess network performance, security, and efficiency, traffic generation plays a crucial role. We introduce PIPO-TG, a Tofino-based traffic generation for high-performance experiments. The primary objective of PIPO-TG is to generate realistic and diverse traffic patterns, enabling researchers to evaluate network performance under varying conditions providing customizable packet forwarding with P4 programmable data planes. Our main contributions include user-defined packet header customization and open-source code for reproducibility. These efforts foster collaboration within the research community to advance traffic generation techniques. We show that PIPO-TG only requires a few lines of code to simulate heterogeneous network scenarios (e.g., traffic bursts and DDoS attacks) while maintaining hardware performance and flexibility.

In-band Network Telemetry (INT) is a novel network monitoring approach mainly fostered by programmable network devices. Despite existing efforts toward the orchestration of INT, little has yet been done to provide fault-tolerant mechanisms in the data plane (e.g., to address hardware failure). In this paper, we introduce InPatching - an in-network approach to fast recover INT-based monitoring from network link failures. InPatching is implemented in the data plane and allows the application of detours in an autonomous and coordinated manner without the control plane intervention. To provide efficient detours to INT solutions, we formalize the fault-tolerant probing planning for INT by means of a MILP (Mixed-Integer Linear Programming) model. We prototype InPatching in P4 and we show that it can recover from fault conditions much faster than control plane solutions (up to 18X), while not imposing substantial overhead.

Network emulation traditionally relies on software-based solutions. While extremely useful in many scenarios, it suffers from performance fidelity and inherent scalability constraints. With the advent of P4 and programmable switches like Tofino, new opportunities for hardware-based network emulation are emerging. P7 (P4 Programmable Patch Panel) offers a solution for high-fidelity 100G traffic network emulation, including different link characteristics such as latency, jitter, packet loss, and bandwidth, as well as the ability to define custom topologies. However, it currently lacks support for custom P4 code in emulated devices. This is where multiple pipelines network emulation comes in. In this demonstration, we show how to emulate a topology using P7 and incorporate custom P4 code into each emulated node. We allocate a dedicated pipe for user-defined P4 code and allow users to conFigure tables for each node separately.

In recent years, advances in virtual reality (VR) technologies (e.g., high-quality VR headsets) have enabled a new perspective of experiences for users (e.g., gaming, online events). However, ensuring the user experience is still a challenge. Existing solutions are limited to measuring and estimating QoE at the user plane (e.g., VR player) or at the control plane, imposing unfeasible latency for different scenarios (5G networks and beyond). In this work, we propose QoEyes, an in-network QoE estimation based on the use of Inter-Packet-Gap (IPG) in programmable devices. Our results show that the IPG measured on the data plane is strongly linked to QoE, yielding an accurate data plane QoE estimate.

Recent advances in VR technology have created new user experiences (e.g., online events, gaming). However, ensuring the user experience is still a challenge. Mostly because Quality of Experience (QoE) measurement is limited to the user or control plane, causing high latencies for different scenarios (e.g., 5G networks and beyond). To address this challenge, we present QoEyes, an in-network QoE estimation technique based on Inter-Packet-Gap (IPG) measured in programmable devices. Our results show that a strong estimate of the user’s QoE can be provided by measuring the IPG on the data plane. Additionally, in this demonstration, we show this QoE estimate and other related metrics in real time, using a Grafana dashboard running in our monitoring server.

In-band Network Telemetry (INT) is a novel net-work monitoring mechanism that improves fine-grained net-work visibility. Despite the increasing research efforts towards the orchestration of INT data acquisition, little has yet been done to efficiently collect telemetry data from the network considering monitoring applications requirements. In this paper, we introduce DyPro - a dynamic probing planning for INT. In particular, DyP ro ensures that telemetry dependencies are always satisfied by monitoring application requirements. We theoretically formalize it as a Mixed-Integer Linear Programming (MILP) optimization model and propose a heuristic procedure to efficiently solve it. Results show that DyP ro can outperform state-of-the-art solutions by up to 5x regarding the percentage of monitoring applications satisfied.