arXiv subjects

George Parisis

Publications and source records attributed to George Parisis.

17 recordsLinked to original sources

arXiv2026

Emulation vs Simulation: A Case Study from Congestion Control Algorithms in Low Earth Orbit Satellite Networks

Evaluating congestion control is inherently challenging because performance depends on the interaction between the congestion-control algorithm, transport stack, application behaviour, measurement process, and network dynamics. This challenge is growing as state-of-the-art protocols incorporate pacing, selective loss recovery, model-based control, and, more recently, reinforcement learning. Low Earth Orbit (LEO) satellite networks are a particularly demanding setting: rapidly changing paths, handovers, non-congestive loss, RTT variation, and transient hotspots all affect transport behaviour. This paper reports the lessons learned from an extensive evaluation campaign across both simulation and emulation for LEO satellite congestion control. We compare multiple classes of congestion-control algorithms, including Cubic, BBR variants, LEO-specific protocols, and reinforcement-learning-based control, using comparable implementations across OMNeT++/INET simulation and Mininet-based emulation with the Linux transport stack. This gives us a rare opportunity to examine not only protocol performance, but also the methodological strengths and limitations of each experimental environment. Our findings show that simulation is indispensable for constellation-scale exploration, controlled parameter sweeps, and future deployment studies, but can miss behaviours caused by production transport-stack mechanisms such as pacing, SACK, RACK, kernel timing, and rate sampling. Emulation exposes these implementation-dependent effects and provides a necessary validation step, but is harder to scale and less exactly repeatable. We distil these experiences into practical lessons for combining simulation and emulation to obtain results that are scalable, reproducible, and deployment-relevant.

cs.NI↗

arXiv2026

Tempest: A GPU-Accelerated Engine for Streaming Temporal Random Walks

Temporal random walks, which sample causality-preserving paths, are widely used to analyze time-stamped interactions in domains such as microservices, finance, and online platforms. Generating such walks at scale is challenging because real-world graphs evolve as high-volume streams, making continuous ingestion, efficient memory usage, and strict temporal ordering essential for practical deployment. We present Tempest (TEMPoral nEtwork Streaming Traversals), a GPU-accelerated engine for streaming temporal random walks. Tempest combines a GPU-native dual-index organization over a shared edge store with a hierarchical cooperative scheduler that dispatches walks at thread, warp, or block granularity based on per-step node convergence, enabling efficient start-edge selection, hop-by-hop causality enforcement, and window-based eviction without synchronization. It further provides closed-form constant-time samplers for common temporal bias functions. Our evaluation demonstrates sustained real-time processing of billion-edge streams under sliding windows, outperforming prior systems in ingestion and walk generation throughput while preserving causal correctness.

cs.DC↗

arXiv2025

FC-ADL: Efficient Microservice Anomaly Detection and Localisation Through Functional Connectivity

Microservices have transformed software architecture through the creation of modular and independent services. However, they introduce operational complexities in service integration and system management that makes swift and accurate anomaly detection and localisation challenging. Despite the complex, dynamic, and interconnected nature of microservice architectures, prior works that investigate metrics for anomaly detection rarely include explicit information about time-varying interdependencies. And whilst prior works on fault localisation typically do incorporate information about dependencies between microservices, they scale poorly to real world large-scale deployments due to their reliance on computationally expensive causal inference. To address these challenges we propose FC-ADL, an end-to-end scalable approach for detecting and localising anomalous changes from microservice metrics based on the neuroscientific concept of functional connectivity. We show that by efficiently characterising time-varying changes in dependencies between microservice metrics we can both detect anomalies and provide root cause candidates without incurring the significant overheads of causal and multivariate approaches. We demonstrate that our approach can achieve top detection and localisation performance across a wide degree of different fault scenarios when compared to state-of-the-art approaches. Furthermore, we illustrate the scalability of our approach by applying it to Alibaba's extremely large real-world microservice deployment.

cs.SE↗

arXiv2025

Learning-Based vs Human-Derived Congestion Control: An In-Depth Experimental Study

Learning-based congestion control (CC), including Reinforcement-Learning, promises efficient CC in a fast-changing networking landscape, where evolving communication technologies, applications and traffic workloads pose severe challenges to human-derived, static CC algorithms. Learning-based CC is in its early days and substantial research is required to understand existing limitations, identify research challenges and, eventually, yield deployable solutions for real-world networks. In this paper, we extend our prior work and present a reproducible and systematic study of learning-based CC with the aim to highlight strengths and uncover fundamental limitations of the state-of-the-art. We directly contrast said approaches with widely deployed, human-derived CC algorithms, namely TCP Cubic and BBR (version 3). We identify challenges in evaluating learning-based CC, establish a methodology for studying said approaches and perform large-scale experimentation with learning-based CC approaches that are publicly available. We show that embedding fairness directly into reward functions is effective; however, the fairness properties do not generalise into unseen conditions. We then show that RL learning-based approaches existing approaches can acquire all available bandwidth while largely maintaining low latency. Finally, we highlight that existing the latest learning-based CC approaches under-perform when the available bandwidth and end-to-end latency dynamically change while remaining resistant to non-congestive loss. As with our initial study, our experimentation codebase and datasets are publicly available with the aim to galvanise the research community towards transparency and reproducibility, which have been recognised as crucial for researching and evaluating machine-generated policies.

cs.NI↗

arXiv2025

Evaluating Learning Congestion control Schemes for LEO Constellations

Low Earth Orbit (LEO) satellite networks introduce unique congestion control (CC) challenges due to frequent handovers, rapidly changing round-trip times (RTTs), and non-congestive loss. This paper presents the first comprehensive, emulation-driven evaluation of CC schemes in LEO networks, combining realistic orbital dynamics via the LeoEM framework with targeted Mininet micro-benchmarks. We evaluated representative CC algorithms from three classes, loss-based (Cubic, SaTCP), model-based (BBRv3), and learning-based (Vivace, Sage, Astraea), across diverse single-flow and multi-flow scenarios, including interactions with active queue management (AQM). Our findings reveal that: (1) handover-aware loss-based schemes can reclaim bandwidth but at the cost of increased latency; (2) BBRv3 sustains high throughput with modest delay penalties, yet reacts slowly to abrupt RTT changes; (3) RL-based schemes severely underperform under dynamic conditions, despite being notably resistant to non-congestive loss; (4) fairness degrades significantly with RTT asymmetry and multiple bottlenecks, especially in human-designed CC schemes; and (5) AQM at bottlenecks can restore fairness and boost efficiency. These results expose critical limitations in current CC schemes and provide insight for designing LEO-specific data transport protocols.

cs.NI↗

arXiv2025

OrbCC: High-Throughput and Low-Latency Data Transport for LEO Satellite Networks

The highly dynamic nature of Low-Earth Orbit (LEO) satellite networks introduces challenges that existing transport protocols fail to address, including non-congestive latency variation and loss, transient congestion hotspots, and frequent handovers that cause temporary disconnections and route changes with unknown congestion and delay characteristics. Our contention is that with this increase in complexity, there is insufficient information being returned from the network for existing congestion control algorithms to minimise latency while maintaining high throughput and minimising retransmissions. Our approach, OrbCC, leverages in-network support to collect per-hop congestion information and uses it to (1) minimise buffer occupancy and end-user latency, (2) maximise application throughput and network utilisation, and (3) rapidly respond to congestion hotspots. We evaluate OrbCC against state-of-the-art transport protocols using OMNeT++/INET-based LEO satellite simulations and targeted micro-benchmarks. The simulations capture RTT dynamics in a LEO constellation, while the micro-benchmarks isolate key characteristics such as non-congestive latency variation and loss, path changes, and congestion hotspots. Results show that OrbCC significantly improves goodput while simultaneously reducing latency and retransmissions compared to existing approaches.

cs.NI↗

arXiv2025

Complexity at Scale: A Quantitative Analysis of an Alibaba Microservice Deployment

Microservice management and testbed research often rests on assumptions about deployments that have rarely been validated at production scale. While recent studies have begun to characterise production microservice deployments, they are often limited in breadth, do not compare findings across deployments, and lack consideration of the implications of findings for commonly held assumptions. We analyse a distributed tracing dataset from Alibaba's production microservice deployment to examine its scale, heterogeneity, and dynamicity. By comparing our findings to prior measurements of Meta's MSA we illustrate both convergent and divergent properties, clarifying which patterns may generalise. Our study reveals extreme architectural scale, long-tail distributions of workloads and dependencies, highly diverse functionality, substantial call graph variability, and pronounced time-varying behaviour which diverge from assumptions underlying research models and testbeds. We summarise how these observations challenge common assumptions in research on fault management, scaling, and testbed design, and outline recommendations for more realistic future approaches and evaluations.

cs.DC↗

arXiv2023

RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols

Reinforcement Learning (RL) has gained significant momentum in the development of network protocols. However, RL-based protocols are still in their infancy, and substantial research is required to build deployable solutions. Developing a protocol based on RL is a complex and challenging process that involves several model design decisions and requires significant training and evaluation in real and simulated network topologies. Network simulators offer an efficient training environment for RL-based protocols, because they are deterministic and can run in parallel. In this paper, we introduce \textit{RayNet}, a scalable and adaptable simulation platform for the development of RL-based network protocols. RayNet integrates OMNeT++, a fully programmable network simulator, with Ray/RLlib, a scalable training platform for distributed RL. RayNet facilitates the methodical development of RL-based network protocols so that researchers can focus on the problem at hand and not on implementation details of the learning aspect of their research. We developed a simple RL-based congestion control approach as a proof of concept showcasing that RayNet can be a valuable platform for RL-based research in computer networks, enabling scalable training and evaluation. We compared RayNet with \textit{ns3-gym}, a platform with similar objectives to RayNet, and showed that RayNet performs better in terms of how fast agents can collect experience in RL environments.

cs.NI↗

arXiv2022

Model Checking Software-Defined Networks with Flow Entries that Time Out (version with appendix)

Software-defined networking (SDN) enables advanced operation and management of network deployments through (virtually) centralised, programmable controllers, which deploy network functionality by installing rules in the flow tables of network switches. Although this is a powerful abstraction, buggy controller functionality could lead to severe service disruption and security loopholes, motivating the need for (semi-)automated tools to find, or even verify absence of, bugs. Model checking SDNs has been proposed in the literature, but none of the existing approaches can support dynamic network deployments, where flow entries expire due to timeouts. This is necessary for automatically refreshing (and eliminating stale) state in the network (termed as soft-state in the network protocol design nomenclature), which is important for scaling up applications or recovering from failures. In this paper, we extend our model (MoCS) to deal with timeouts of flow table entries, thus supporting soft state in the network. Optimisations are proposed that are tailored to this extension. We evaluate the performance of the proposed model in UPPAAL using a load balancer and firewall in network topologies of varying size.

cs.NI↗

arXiv2021

Developing and experimenting with LEO satellite constellations in OMNeT++

In this paper, we present our work in designing and implementing a LEO satellite constellation simulation model1 within OMNeT++ and INET, which is validated by comparing the results with existing work. Our model builds upon the fundamentals of the Open Source Satellite Simulator (OS$^3$), which was ported to INET 4.3. We describe how the model was integrated on top of the INET and ported OS$^3$ Framework. We then experiment with the simulation model to demonstrate its viability in simulating LEO satellite constellations. This involved simulating both outdated and more recent satellite constellations, using FCC filing information, to validate latency results.

cs.NI↗

arXiv2021

SCDP: Systematic Rateless Coding for Efficient Data Transport in Data Centres (Complete Version)

In this paper we propose SCDP, a general-purpose data transport protocol for data centres that, in contrast to all other protocols proposed to date, supports efficient one-to-many and many-to-one communication, which is extremely common in modern data centres. SCDP does so without compromising on efficiency for short and long unicast flows. SCDP achieves this by integrating RaptorQ codes with receiver-driven data transport, packet trimming and Multi-Level Feedback Queuing (MLFQ); (1) RaptorQ codes enable efficient one-to-many and many-to-one data transport; (2) on top of RaptorQ codes, receiver-driven flow control, in combination with in-network packet trimming, enable efficient usage of network resources as well as multi-path transport and packet spraying for all transport modes. Incast and Outcast are eliminated; (3) the systematic nature of RaptorQ codes, in combination with MLFQ, enable fast, decoding-free completion of short flows. We extensively evaluate SCDP in a wide range of simulated scenarios with realistic data centre workloads. For one-to-many and many-to-one transport sessions, SCDP performs significantly better compared to NDP and PIAS. For short and long unicast flows, SCDP performs equally well or better compared to NDP and PIAS.

cs.NI↗

arXiv2021

Internet Traffic Volumes Are Not Gaussian -- They Are Log-Normal: An 18-Year Longitudinal Study With Implications for Modelling and Prediction (Complete Version)

Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that traffic obeys the log-normal distribution which is a better fit than the Gaussian distribution commonly claimed in the literature. We also investigate an alternative heavy-tailed distribution (the Weibull) and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which exhibit a poor fit for all distributions tried and show that this is often due to traffic outages or links that hit maximum capacity. We demonstrate that the data we look at is stationary if we consider samples of 15- minute long or even 1-hour long. This gives confidence that we can use the distributions for estimation and modelling purposes. We demonstrate the utility of our findings in two contexts: predicting that the proportion of time traffic will exceed a given level (for service level agreement or link capacity estimation) and predicting 95th percentile pricing. We also show that the log-normal distribution is a better predictor than Gaussian or Weibull distributions in both contexts.

cs.NI↗

arXiv2020

Towards Model Checking Real-World Software-Defined Networks (version with appendix)

In software-defined networks (SDN), a controller program is in charge of deploying diverse network functionality across a large number of switches, but this comes at a great risk: deploying buggy controller code could result in network and service disruption and security loopholes. The automatic detection of bugs or, even better, verification of their absence is thus most desirable, yet the size of the network and the complexity of the controller makes this a challenging undertaking. In this paper we propose MOCS, a highly expressive, optimised SDN model that allows capturing subtle real-world bugs, in a reasonable amount of time. This is achieved by (1) analysing the model for possible partial order reductions, (2) statically pre-computing packet equivalence classes and (3) indexing packets and rules that exist in the model. We demonstrate its superiority compared to the state of the art in terms of expressivity, by providing examples of realistic bugs that a prototype implementation of MOCS in UPPAAL caught, and performance/scalability, by running examples on various sizes of network topologies, highlighting the importance of our abstractions and optimisations.

cs.NI↗

arXiv2019

On the Distribution of Traffic Volumes in the Internet and its Implications

Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that the log-normal distribution is a better fit than the Gaussian distribution commonly claimed in the literature. We also investigate a second heavy-tailed distribution (the Weibull) and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which are a poor fit for all distributions tried and show that this is often due to traffic outages or links that hit maximum capacity. We demonstrate the utility of the log-normal distribution in two contexts: predicting the proportion of time traffic will exceed a given level (for service level agreement or link capacity estimation) and predicting 95th percentile pricing. We also show the log-normal distribution is a better predictor than Gaussian or Weibull distributions.

cs.NI↗

arXiv2018

Inferring the time-varying functional connectivity of large-scale computer networks from emitted events

We consider the problem of inferring the functional connectivity of a large-scale computer network from sparse time series of events emitted by its nodes. We do so under the following three domain-specific constraints: (a) non-stationarity of the functional connectivity due to unknown temporal changes in the network, (b) sparsity of the time-series of events that limits the effectiveness of classical correlation-based analysis, and (c) lack of an explicit model describing how events propagate through the network. Under the assumption that the probability of two nodes being functionally connected correlates with the mean delay between their respective events, we develop an inference method whose output is an undirected weighted network where the weight of an edge between two nodes denotes the probability of these nodes being functionally connected. Using a combination of windowing and convolution to calculate at each time window a score quantifying the likelihood of a pair of nodes emitting events in quick succession, we develop a model of time-varying connectivity whose parameters are determined by maximising the model's predictive power from one time window to the next. To assess the effectiveness of our inference method, we construct synthetic data for which ground truth is available and use these data to benchmark our approach against three state-of-the-art inference methods. We conclude by discussing its application to data from a real-world large-scale computer network.

cs.LG↗

arXiv2017

Constraints and Entropy in a Model of Network Evolution

Barabási-Albert's `Scale Free' model is the starting point for much of the accepted theory of the evolution of real world communication networks. Careful comparison of the theory with a wide range of real world networks, however, indicates that the model is in some cases, only a rough approximation to the dynamical evolution of real networks. In particular, the exponent $γ$ of the power law distribution of degree is predicted by the model to be exactly 3, whereas in a number of real world networks it has values between 1.2 and 2.9. In addition, the degree distributions of real networks exhibit cut offs at high node degree, which indicates the existence of maximal node degrees for these networks. In this paper we propose a simple extension to the `Scale Free' model, which offers better agreement with the experimental data. This improvement is satisfying, but the model still does not explain \emph{why} the attachment probabilities should favor high degree nodes, or indeed how constraints arrive in non-physical networks. Using recent advances in the analysis of the entropy of graphs at the node level we propose a first principles derivation for the `Scale Free' and `constraints' model from thermodynamic principles, and demonstrate that both preferential attachment and constraints could arise as a natural consequence of the second law of thermodynamics.

physics.soc-ph↗

arXiv2015

Multipath-TCP in ns-3

In this paper we present our work on designing and implementing an ns-3 model for MultiPath TCP (MPTCP). Our MPTCP model closely follows MPTCP specifications, as described in RFC 6824, and supports TCP NewReno loss recovery on a per subflow basis. Subflow management is based on MPTCP's kernel implementation. We briefly describe how we integrate our MPTCP model with ns-3 and present example simulation results to showcase its working state.

cs.NI↗