Change search
Link to record
Permanent link

Direct link
Alternative names
Publications (10 of 109) Show all publications
Garcia, J., Sundberg, S. & Brunstrom, A. (2024). Fine-Grained Starlink Throughput Variation Examined With State-Transition Modeling. In: 2024 19th Wireless On-Demand Network Systems and Services Conference (WONS): . Paper presented at 19th Wireless On-demand Network systems and Services Conference (WONS), Chamonix, France, January 29-31, 2024. (pp. 69-76). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Fine-Grained Starlink Throughput Variation Examined With State-Transition Modeling
2024 (English)In: 2024 19th Wireless On-Demand Network Systems and Services Conference (WONS), Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 69-76Conference paper, Published paper (Refereed)
Abstract [en]

Leveraging a data set of almost half a billion packets with high-precision packet times and sizes, we process it to extract characteristics of the bursts emitted over Starlink's Ethernet interface. The structure of these bursts directly reflect the physical layer receipt of OFDMA frames on the satellite link. We study these bursts by analyzing their rates, and by proxy the transition between different physical layer rates. The results highlight that  there is definitive structure in the transition behavior, and we note specific behaviors such as  particular transitionsteps associated with rate switching, and that rate switching occurs mainly to neighboring rates. We also study the joint burst rate and burst duration transitions, noting that transitions occur mainly within the same rate, and that changes in burst duration are often performed with an intermediate short burst in-between.Finally, we examine the configurations of the three factors burst rate, burst duration, and inter-burst silent time, which together determine the effective throughput of a Starlink connection.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Series
Annual Conference on Wireless On Demand Network Systems and Services (WONS), ISSN 2688-4917, E-ISSN 2688-4909
National Category
Telecommunications
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-98081 (URN)10.23919/WONS60642.2024.10449629 (DOI)978-3-903176-61-4 (ISBN)979-8-3503-6062-2 (ISBN)
Conference
19th Wireless On-demand Network systems and Services Conference (WONS), Chamonix, France, January 29-31, 2024.
Projects
DRIVE
Funder
Knowledge Foundation
Available from: 2024-01-19 Created: 2024-01-19 Last updated: 2024-04-05Bibliographically approved
Garcia, J., Sundberg, S. & Brunstrom, A. (2024). Inferring Starlink Physical Layer Transmission Rates Through Receiver Packet Timestamps. In: : . Paper presented at IEEE Wireless Communications and Networking Conference, Dubai,United Arab Emirates, April 21-24, 2024.. IEEE
Open this publication in new window or tab >>Inferring Starlink Physical Layer Transmission Rates Through Receiver Packet Timestamps
2024 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Although Starlink has been deployed for several years, a detailed understanding of system internals is still lacking. In this work we employ precise per-packet timestamps obtained from a hardware-timestamp capable NIC connected to a Starlink terminal.We find that Starlink frame timing details are readily observable at the network layer by analyzing the packet timing patterns.Based on a one-week measurement campaign we collect around half a billion of packet size and timing observations. Processing these observations yields 2.3 million transmission bursts. To learn details on the radio resource management we develop a methodology to infer the effective physical layer sending rate. Our findings show that although Starlink throughput can vary widely over multiple time-scales, there are a small number of fundamental physical layer transmission rates. We employ Gaussian Mixture Modeling to determine 14 such fundamental transmission rates, and relate the obtained rates to previous knowledge of the Starlink OFDMA frame structure. Our empirical observations provide an excellent match for a radio resource configuration where a Starlink frame employs 1000 subcarriers and 287 symbols per frame for user traffic transmission, which for uniform 4-QAM modulation yields a base rate of 430.5 Mbps. This physical layer base rate appears to mostly be varied by multiples of 27 Mbps, in several instances likely by modifying the modulation of a subset of the symbols in multiples of 18 symbols. 

Place, publisher, year, edition, pages
IEEE, 2024
National Category
Telecommunications
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-98080 (URN)
Conference
IEEE Wireless Communications and Networking Conference, Dubai,United Arab Emirates, April 21-24, 2024.
Projects
DRIVE
Funder
Knowledge Foundation
Available from: 2024-01-19 Created: 2024-01-19 Last updated: 2024-01-22
Sundberg, S., Garcia, J. & Brunstrom, A. (2023). Characterizing Wireless Link Throughput with eBPF and Hardware Timestamps. In: 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD): . Paper presented at IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). Edinburgh, Scottland. November 6-8 2023. (pp. 302-308). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Characterizing Wireless Link Throughput with eBPF and Hardware Timestamps
2023 (English)In: 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 302-308Conference paper, Published paper (Refereed)
Abstract [en]

With a growing user base and the deployment of new systems, such as 5G and Starlink, a deep understanding of the varying link throughput for wireless systems is highly important. While detailed analysis of link throughput can be done on packet traces, collecting extensive packet traces often faces storage and privacy challenges. Instead, we propose using traces of link-wide inter-packet delay (IPD) to enable highly granular link throughput characterization on a wider scale. To this end, we present an eBPF-based tool designed to capture IPDs, and evaluate the accuracy of captured IPDs with the IPD tool and tcpdump, both with and without access to hardware timestamps. While hardware provided timestamps provide accurate IPDs, we find that software based timestamps lead to IPD values which are very inaccurate, but still useful in aggregate form to characterize throughput at millisecond timescales. Furthermore, we show that concurrent packet processing incurs a significant amount of packet reordering, which necessitates the consideration of several previous packets when computing the link-wide IPD. Finally we present an example use case of IPD collection, characterizing frequent silent periods during a speedtest over a 5G link.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Communication Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-99067 (URN)10.1109/CAMAD59638.2023.10478419 (DOI)
Conference
IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). Edinburgh, Scottland. November 6-8 2023.
Available from: 2024-03-27 Created: 2024-03-27 Last updated: 2024-04-02Bibliographically approved
Garcia, J., Sundberg, S., Caso, G. & Brunstrom, A. (2023). Multi-Timescale Evaluation of Starlink Throughput. In: LEO-NET '23: Proceedings of the 1st ACM Workshop on LEO Networking and Communication. Paper presented at 1st ACM Workshop on LEO Networking and Communication (pp. 31-36). ACM Digital Library
Open this publication in new window or tab >>Multi-Timescale Evaluation of Starlink Throughput
2023 (English)In: LEO-NET '23: Proceedings of the 1st ACM Workshop on LEO Networking and Communication, ACM Digital Library, 2023, p. 31-36Conference paper, Published paper (Refereed)
Abstract [en]

Although Starlink has been rolled-out for several years, there is still a lack of knowledge regarding system details and performance characteristics. To address this, we perform a network layer measurement campaign utilizing precise times-Tamping to analyze throughput variations at multiple time scales, and infer system timing details. On larger timescales we quantify the diurnal variations where the throughput varies with the time of day. On the medium timescales we establish the likely frequency allocation and beam switching period to be 15 seconds. The associated connectivity disturbances contribute to severe link underutilization for single long-lived TCP flows, which typically reach only 46% of the estimated link capacity. On the sub-millisecond timescale our network layer measurements corroborate recent physical layer investigations of the Starlink frame timing, which is confirmed to be 1.33 ms. © 2023 Owner/Author(s).

Place, publisher, year, edition, pages
ACM Digital Library, 2023
Keywords
diurnal variation, frame timing, LEO, Frequency allocation, Timing circuits, Beam switching, Measurement campaign, Multiple time scale, Performance characteristics, Time of day, Time-scales, Timing details, Network layers
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-99013 (URN)10.1145/3614204.3616108 (DOI)2-s2.0-85174990626 (Scopus ID)9798400703324 (ISBN)
Conference
1st ACM Workshop on LEO Networking and Communication
Available from: 2024-03-25 Created: 2024-03-25 Last updated: 2024-03-25Bibliographically approved
Garcia, J. (2022). Change Point Detection in Clustered Network Performance Indicators. In: Varga P., Granville L.Z., Galis A., Godor I., Limam N., Chemouil P., Francois J., Pahl M.-O. (Ed.), Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022. Paper presented at 2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Change Point Detection in Clustered Network Performance Indicators
2022 (English)In: Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022 / [ed] Varga P., Granville L.Z., Galis A., Godor I., Limam N., Chemouil P., Francois J., Pahl M.-O., Institute of Electrical and Electronics Engineers (IEEE), 2022Conference paper, Published paper (Refereed)
Abstract [en]

The detailed performance characteristics of networking equipment is to a large extent a function of the software that controls the underlying hardware components. Most networking equipment is regularly updated with new software versions. By studying performance changes related to such changes in software, it is possible to identify particular software versions that affect the performance of the system. Consequently, having automated methods for detecting changes in network equipment performance is crucial. In this work we study the change point detection problem arising when the placement in time of software updates is known a priori, but the presence of any performance implications on any of the thousands of performance indicators that can be collected is unknown. The ability to improve the automated detection of such change points by clustering the monitored systems according to the set of collected indicators has not been fully evaluated. We here report our experience with employing clustering, together with a bootstrap-based change point detection, across a range of performance indicators. We evaluate four variations of clustering approaches, and demonstrate the resulting improvement in change point detection sensitivity. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Series
IEEE IFIP Network Operations and Management Symposium
Keywords
bootstrapping; Change points; clustering; NFV
National Category
Human Aspects of ICT Information Systems, Social aspects
Identifiers
urn:nbn:se:kau:diva-91686 (URN)10.1109/NOMS54207.2022.9789781 (DOI)000851572700037 ()2-s2.0-85133171506 (Scopus ID)
Conference
2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022
Available from: 2022-08-31 Created: 2022-08-31 Last updated: 2022-09-22Bibliographically approved
Garcia, J. (2022). Change Point Evaluation in Networking Logs with Periodicity Filtering and Bootstrapping. In: Varga P., Granville L.Z., Galis A., Godor I., Limam N., Chemouil P., Francois J., Pahl M.-O. (Ed.), Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022. Paper presented at 2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Change Point Evaluation in Networking Logs with Periodicity Filtering and Bootstrapping
2022 (English)In: Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022 / [ed] Varga P., Granville L.Z., Galis A., Godor I., Limam N., Chemouil P., Francois J., Pahl M.-O., Institute of Electrical and Electronics Engineers (IEEE), 2022Conference paper, Published paper (Refereed)
Abstract [en]

Efficient operation of networking systems is important from resource utilization, OPEX, and energy consumption perspectives. A major factor in efficient operations is the underlying software that controls the networking hardware or virtualized network functions. Most software in hardware-based networking devices is periodically updated, which may or may not have impact on various aspects of the performance of the device. We consider the issue of change point detection in network performance indicators, aiming to detect when such software updates co-occur with changes to any subset of collected performance metrics. In particular, we study the change point detection problem that arises when the placement in time of firmware changes is known a priori, but the presence of any performance implications is unknown. We focus on evaluating change point detection in operational network equipment log data, and consider diurnal variation suppression approaches. We propose the use of periodicity filtering to remove anomalous data sources, and apply a resampling technique using bootstrapping to determine when a software update has performance implications. Our results show that this automated change point detection approach can locate performance-related changes, and that load normalization appears to be the most sensitive approach to diurnal variation suppression.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Series
IEEE IFIP Network Operations and Management Symposium
Keywords
bootstrapping; NFV; periodogram; SDN
National Category
Computer Sciences Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kau:diva-91687 (URN)10.1109/NOMS54207.2022.9789925 (DOI)000851572700178 ()2-s2.0-85133170314 (Scopus ID)
Conference
2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022
Available from: 2022-08-31 Created: 2022-08-31 Last updated: 2022-09-22Bibliographically approved
Garcia, J., Hurtig, P. & Hammar, J. (2022). Evaluating and Modeling 5G MPTCP Performance. In: International Conference on Wireless and Mobile Computing, Networking and Communications: . Paper presented at 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob),Thessaloniki, Greece, October 10-12, 2022. (pp. 406-411). Institute of Electrical and Electronics Engineers (IEEE), 2022-October
Open this publication in new window or tab >>Evaluating and Modeling 5G MPTCP Performance
2022 (English)In: International Conference on Wireless and Mobile Computing, Networking and Communications, Institute of Electrical and Electronics Engineers (IEEE), 2022, Vol. 2022-October, p. 406-411Conference paper, Published paper (Refereed)
Abstract [en]

Multipath connectivity and aggregation of multiple communication links is actively being researched with the aim to achieve higher throughput and lower latency. In this work we perform an emulation-based evaluation of the relative goodput of MPTCP and TCP in a 5G usage context. A large range of path capacity and delay conditions is explored, for both the primary and secondary paths, with over 2000 different configurations evaluated. Evaluations are performed over eight combinations of MPTCP schedulers and congestion controls. The results show that MPTCP running over two links provide lower goodput than TCP over a single link for the majority of cases. Asymmetry in link conditions is in many cases a major complication for the MPTCP scheduler. To examine the predictability of poor performance, and to obtain further insight on the structure of this phenomena, we perform regression modeling of the relative good put. In addition to the traditional approaches of Linear Regression and Random Forest, we also employ Sym-bolic Regression to obtain mathematical expressions capable of providing insight on the path conditions most contributing to poor MPTCP performance. Such regression expressions can be informative when evaluating different schedulers or link aggregation approaches. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
5G mobile communication systems, Regression analysis, Scheduling, Transmission control protocol, Delay condition, Good put, High-low, High-throughput, Low latency, Multipath, Path capacity, Path delay, Performance, Usage context, Decision trees
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-92705 (URN)10.1109/WiMob55322.2022.9941555 (DOI)2-s2.0-85142724742 (Scopus ID)978-1-6654-6975-3 (ISBN)
Conference
18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob),Thessaloniki, Greece, October 10-12, 2022.
Funder
.SE (The Internet Infrastructure Foundation)Knowledge Foundation
Available from: 2022-12-09 Created: 2022-12-09 Last updated: 2022-12-09Bibliographically approved
Garcia, J., Beckman, C., Reinhagen, R. & Brunström, A. (2022). Measuring and Modeling Aggregate LTE Connection Reliability for Train Operations. In: 2022 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR): . Paper presented at IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR 2022),Arlington, USA, September 13-15, 2022. (pp. 31-36). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Measuring and Modeling Aggregate LTE Connection Reliability for Train Operations
2022 (English)In: 2022 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR), Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 31-36Conference paper, Published paper (Refereed)
Abstract [en]

We examine the connection reliability of LTE cellular infrastructure for supporting train signaling systems. In particular, the impact of simultaneous use of multiple networks on reliability is considered, along with failure correlation effects. We present a tailored reliability model, and report on data collected from many train-mounted cellular routers. Connection reliability reaches 99.994% when aggregation is used, compared to 99.953% for the best single link. Both modeling and measurement results show greatly improved reliability when aggregating over multiple links, thus indicating that commercial cellular networks may be useful for providing connectivity to future train signaling systems.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
connection reliability, train control
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-93972 (URN)10.1109/CQR54764.2022.9918616 (DOI)978-1-6654-1067-0 (ISBN)978-1-6654-1066-3 (ISBN)
Conference
IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR 2022),Arlington, USA, September 13-15, 2022.
Available from: 2023-03-22 Created: 2023-03-22 Last updated: 2023-03-22Bibliographically approved
Hillblom, J., Garcia, J. & Waldenborg, A. (2021). Building Efficient Regular Expression Matchers Through GA Optimization with ML Surrogates. In: Machuca, CM; Martins, L;Sargento, S; Wauters, T; Jorge, L ; Chemouil, P Salhab, N; (Ed.), Proceedings of the 2021 12th International Conference on Network of the Future, NoF 2021: . Paper presented at 12th International Conference on Network of the Future, NoF 2021, 6 October 2021 through 8 October 2021. IEEE
Open this publication in new window or tab >>Building Efficient Regular Expression Matchers Through GA Optimization with ML Surrogates
2021 (English)In: Proceedings of the 2021 12th International Conference on Network of the Future, NoF 2021 / [ed] Machuca, CM; Martins, L;Sargento, S; Wauters, T; Jorge, L ; Chemouil, P Salhab, N;, IEEE, 2021Conference paper, Published paper (Refereed)
Abstract [en]

Important network functions such as traffic classification and intrusion detection often depend on high-throughput regular expression matching. To achieve high performance, regular expressions can be represented as state machines, which are then merged. However, determining which individual state machines should ideally be merged together is a challenging optimization problem. We address this problem by using genetic algorithms with novel problem-specific operators. To allow large scale evaluation of the new operators, we devise two ML-based surrogate models for the expensive fitness evaluation function. Our results from a set of production scale regular expressions show that using the most appropriate operations provides large gains over a naive baseline, but also that no universal best combination of operators exist. We provide some insights into which operators perform best for different objectives, and show the variation between TCP- and UDP-specific regular expressions.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
DFA, DPI, Genetic algorithms, IDS, NFA, Regular expressions, Surrogate models, Traffic classification, Function evaluation, Intrusion detection, Network security, Pattern matching, GA optimization, Network functions, State-machine, Surrogate modeling
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-89079 (URN)10.1109/NoF52522.2021.9609828 (DOI)000859513400004 ()2-s2.0-85123499344 (Scopus ID)9781665424349 (ISBN)978-1-6654-2435-6 (ISBN)
Conference
12th International Conference on Network of the Future, NoF 2021, 6 October 2021 through 8 October 2021
Funder
Swedish Research Council, 2018-05973
Available from: 2022-03-10 Created: 2022-03-10 Last updated: 2022-10-20Bibliographically approved
Korhonen, T. & Garcia, J. (2021). Exploring Ranked Local Selectors for Stable Explanations of ML Models. In: 2021 2nd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2021: . Paper presented at 2nd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2021, 15 November 2021 through 16 November 2021 (pp. 122-129). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Exploring Ranked Local Selectors for Stable Explanations of ML Models
2021 (English)In: 2021 2nd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2021, Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 122-129Conference paper, Published paper (Refereed)
Abstract [en]

While complex machine learning methods can achieve great performance, human-interpretable details of their internal reasoning is to a large extent unavailable. Interpretable machine learning can remedy the lack of access to model reasoning but remains an elusive feat to fully achieve. Here we propose ranked selectors as a method for post-hoc explainability of classification outcomes from arbitrary classification models, with an initial emphasis on tabular data of moderate dimensions. The method is based on constructing a set of selectors, or rules, delimiting a local class consistent domain with maximal cover around the item of interest. The extended adjacent feature space is probed to achieve a ranking of the selectors. The method supports the use of an explicit foil class Q, allowing the formulation of contrastive queries in the form 'Why inference P instead of alternative inference Q?'. The answer is given as a short list of disjoint rules, a format previously shown to be amenable to human interpretation. We demonstrate the proposed method in open datasets, and elaborate on its stability aspects relative to other comparable methods.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2021
Keywords
black box models, contrastivity, Explainability, Adjacent feature, Black box modelling, Classification models, Machine learning methods, Maximal covers, Model reasonings, Performance, Tabular data, Machine learning
National Category
Information Studies Computer Sciences
Identifiers
urn:nbn:se:kau:diva-89479 (URN)10.1109/IDSTA53674.2021.9660809 (DOI)000852877600018 ()2-s2.0-85124559014 (Scopus ID)9781665421805 (ISBN)
Conference
2nd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2021, 15 November 2021 through 16 November 2021
Available from: 2022-04-12 Created: 2022-04-12 Last updated: 2022-09-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3461-7079

Search in DiVA

Show all publications