Evaluation of Online Schedule Synthesis Algorithms for Time-based - PowerPoint PPT Presentation

Chair of Network Architectures and Services Department of Informatics Technical University of Munich Evaluation of Online Schedule Synthesis Algorithms for Time-based Scheduled Time Sensitive Networks Final talk for the Master’s Thesis by Alexander Mildner advised by Max Helm, Benedikt Jaeger, Dr. Marcel Wagner (Intel), Hector Blanco Alcaine (Intel) Wednesday 4 th March, 2020 Chair of Network Architectures and Services Department of Informatics Technical University of Munich

Outline • Introduction to Time Sensitive Networking • Thesis Objectives • Design and Approach • Implementation • Network Calculus Model for WCD Analysis in TSN Networks • TSN emulation environment based on Mininet • GCL Synthesis Algorithms • TSN Testbed Setup • Evaluations • Conclusion and Future Works A. Mildner — TSN 2

Introduction to Time Sensitive Networking Time Sensitive Networking (TSN) • A set of IEEE standards and additions to the IEEE 802.3 Ethernet standard, for providing deterministic communication on common Ethernet technology (Layer 2) • IEEE Time Sensitive Networking Task Group, former Audio Video Bridging (AVB) Task Group • High interest on TSN in various use-cases: • Industrial Automation (Industry 4.0) • Automotive • Aerospache/Avionic • Autonomous Driving • Convergence of Information Technology (IT) and Operational Technology (OT) possible using TSN • The Standarization process is still ongoing, some important standards are not yet finished and published A. Mildner — TSN 3

Introduction to Time Sensitive Networking IEEE TSN Standards Figure 1: TSN components [2] A. Mildner — TSN 4

Introduction to Time Sensitive Networking Example TSN Network CUC CNC ES1 ES3 Flow 1 GCL GCL GCL SW1 SW2 GCL GCL GCL Flow 2 ES2 ES4 Figure 2: IEEE 802.1Qbv enabled TSN Network with a centralized Configuration Approach. Legend: • CUC - Centralized User Configuration • CNC - Centralized Network Configuration • ESx - End Station • SWx - Switch • GCL - Gate Control List A. Mildner — TSN 5

Introduction to Time Sensitive Networking IEEE 802.1Qbv - Time Aware Scheduler ingress Switching Fabric Q7 Q6 Q5 Q0 GCL T 1 : 1000 0000 T 2 : 0111 1111 Strict Credit Transmission Transmission T GCL Prirority Based Selection Selection Algorithm Algorithm Scheduling Shaper Gate 7 Gate 6 Gate 5 Gate 0 Transmission Selection egress Figure 3: Time-based Scheduler (GCL: Gate Control List, T GCL : Cycle Time of the schedule) A. Mildner — TSN 6

Introduction to Time Sensitive Networking IEEE 802.1Qbv - Time Aware Scheduler ingress Switching Fabric Q7 Q6 Q5 Q0 GCL T 1 : 1000 0000 T 2 : 0111 1111 Strict Credit Transmission Transmission T GCL Prirority Based Selection Selection Algorithm Algorithm Scheduling Shaper Gate 7 Gate 6 Gate 5 Gate 0 Transmission Selection egress Figure 4: Time-based Scheduler (GCL: Gate Control List, T GCL : Cycle Time of the schedule) A. Mildner — TSN 7

Thesis Objectives Problem Statement • The TSN set of standards is still lacking a proper way for dynamically (re-)configuration of TSN networks • Currently TSN networks need to be statically configured with prior knowledge about the network and the particular flows in it • The scheduling problem introduced by IEEE 802.1Qbv (Time Aware Scheduling) is con- sidered as non-trival ( NP-Complete ) with respect to the network size • But: There have been recent research efforts, in order to tackle the dynamic schedule synthesis Main goal of this Thesis: Provide and evaluate an apporach towards dynamic configuration and schedule synthesis for IEEE 802.1Qbv enabled TSN networks. A. Mildner — TSN 8

Thesis Objectives • Objective 1: Provide an automated framework for worst-case delay analysis on a flow basis in IEEE 802.1Qbv scheduled TSN networks and evaluate and validate the implementation. • Objective 2: Provide an automated framework for online GCL synthesis for IEEE 802.1Qbv based TSN networks and evaluate and validate the implementation. • Objective 3: Provide suitable evaluation environments for automated measurements and validation for IEEE 802.1Qbv based TSN networks. • Objective 4: Perform proper Analysis on the implemented methods for online schedule synthesis and dynamic configuration of IEEE 802.1Qbv enabled TSN networks. A. Mildner — TSN 9

Design and Approach Dynamic Configuration System model design User Stream Input Schedule WCD Create Generator Analysis Configuration Network Topology Deploy Configuration Figure 5: Proposed system model design for Dynamic Configuration of IEEE 802.1Qbv enabled TSN networks. • Inputs: Network Topology, Stream Definitions • Output: Deployable Qbv configuration for the TSN network • The User can change the stream inputs i.e. add or remove streams or alter stream para- meters • The generated GCLs should be validated using a NC Model for WCD Analysis for verifying, that the end-to-end latency requirements are met • The generated configurations should be in the correct format for deployment in a TSN network using the Deploy Configuration module A. Mildner — TSN 10

Implementation Network Calculus Model for WCD Analysis in TSN Networks • L. Zaho et. al proposed in [5] a Network Calculus model for determining the Worst Case Delay of flows in time-based scheduled TSN networks • Conducts a hop by hop WCD analysis, using leaky bucket arrival ( α ( t ) ) and TDMA service curves ( β ( t ) ) • Works on statically configured time-based scheduled TSN networks (GCLs given) and accounts for impacts of higher and lower priority traffic • Inputs → network topology, GCLs, flow information, Flow of Interest (FOI) • Output → End-to-End WCD of the FOI • Implemented parameter calculation for the resulting service curves for each port on the FOIs path in Python • For reproducable and reiable WCD analysis, we have used the DiscoDNC 1 Framework 1 https://disco.cs.uni-kl.de/index.php/projects/disco-dnc A. Mildner — TSN 11

Implementation Network Calculus Model for WCD Analysis in TSN Networks Figure 6: Example resulting guaranteed service curve for a particular flow provided by a Qbv enabled Switch. [5] A. Mildner — TSN 12

Implementation Building a TSN emulation environment based on Mininet • Nice to Have: A flexible Network Emulator that can do time-based scheduling according to the standard • Idea: Use recently added TAPRIO 2 kernel net-scheduler module to add time-based sched- uling capabilities to mininet 3 • TAPRIO can be configured like any other Queuing Dicipline (qdisc) using the iproute2 tool tc • Problem: • mininet uses veth (virtual ethernet) interfaces • veth interfaces implement no Transmit (TX) Queues • TAPRIO requires TX Queues to work • Result: Problem still persists and could not be solved during this thesis 2 http://man7.org/linux/man-pages/man8/tc-taprio.8.html 3 http://mininet.org/ A. Mildner — TSN 13

Implementation GCL Synthesis Algorithm • We implemented one example of a proposed GCL Synthesis Algorithm • Model is based on Array Theory Encoding ( T A ) for Satisfiability Modulo Theory (SMT) as proposed in [4] • Implemented in Python, using the z3 SMT/OMT solver • Inputs → network topology, flow information, additional network information • Output → For each node in the network: array of open times ( φ ), array of close times ( τ ) and array of frame-to-window assignment ( κ ) • Hide complexity of the underlying model behind a easy to use Class ( GCLATSolver() ) with a sophisticated interface • Included a transformation function, in order to use the generated output with the real Hard- ware on the TSN Testbed A. Mildner — TSN 14

Implementation TSN Testbed Implementations • Idea: Conduct end-to-end latency measurements of a dynamically configurable time-based scheduled TSN Network on real TSN capable Hardware • Setup a TSN Testbed consiting of 2 Qbv capable Switches and four to six TSN capable End Stations at the Intel Office • We have implemented an example real-time application ( talker and listener ), which utilizes the Intel i210 launchtime feature and Hardware TX/RX Timestamping • Implemented sophisticated configuration scripts: • End Stations: VLAN , TAPRIO and ETF configuration (remotely via SSH ) • Switches: Qbv configuration using netopeer2 ( NETCONF/RESTCONF Protocol) A. Mildner — TSN 15

Evaluation NC model evaluation • For very simple scenarios the NC model seems to provide resonable results • We conducted a WCD Analysis on some of the simple example cases presented in the paper [5] • One result was e.g. 1204.6 µ s (our implementation) versus 1287.8 µ s (paper) • Unfortunately, due to the lack of time we could not further investigate the cause of the difference A. Mildner — TSN 16

Evaluation Runtime Evaluation of the GCL synthesis Algorithm 15 Endstations | 5 Switches | Periods=['20ms', '10ms'] | 4 TT-Queues 350 300 300 250 time [s] 200 200 150 100 50 100 16 0 # O p 50 e 8 n 40 W 30 i n 4 d 20 #Streams o w 2 5 10 s Figure 7: Runtime results of the GCL synthesis Algorithm using Array Theory Encoding. A. Mildner — TSN 17

Evaluation Runtime Evaluation of the GCL synthesis Algorithm Figure 8: Scalability Analysis of the GCL synthesis Algorithm in a strict periodic system. A. Mildner — TSN 18

Evaluation GCL Verification GCLs in the Network ('SW1', 'ES3') ('ES1', 'SW1') 0 20 40 60 80 100 T GCL [ s ] Figure 9: Generated GCLs in a simple TSN network setup with 2 ES and 1 SW. A. Mildner — TSN 19