Time-triggered Flows Research Articles

RC performance analysis based on model optimization with the aid of network calculus

Time-Triggered Ethernet (TTE) Network is a communication network which combines the time-triggered traffic and event-triggered traffic. Based on network calculation, the adopted model in TTE considers the worst case which assumes a number of traffic simultaneously arrive at a node and each virtual link work is fully loaded. Such assumptions lead to the analysis of a certain RC data flow’s delay performance obsessing too much pessimism. In this paper, based on the partition scheduling model, we introduced the loss packet period of the time-triggered data flow $$ p $$ and the correction parameter of rate-constrained (RC) data flow $$ \mu $$ and then optimized the model of service curve and arrival curve of a certain RC data flow. The delay performance of RC data flow is analyzed under proposed optimized model. Simulation results verify that our optimized model relieves the pessimism on delay analysis. The smaller the $$ p $$ value is and the larger the $$ \mu $$ value is, the stricter delay bound can be obtained by our optimized model than by the traditional model. According to the optimized delay analysis model, the scheduling table can be better rearranged and the system resources can be used reasonably.

Routing algorithms for IEEE802.1Qbv networks

The recently published IEEE 802.1Qbv standard specifies enhancements for providing real-time communication guarantees for time-triggered flows while also handling best-effort traffic in a converged Ethernet network. The enhancements include a programmable time-based gating mechanism for stipulating which of the queues of an egress port are available for transmission at any given point of time. By appropriately programming (opening and closing) these gates, the traversal of packets through the network can be controlled to precisely follow a precomputed schedule that satisfies the timing constraints of the time-triggered flows. Computing such transmission schedules requires routing of the flows in the first step, followed by the computation of gate schedules for the flows along their respective routes. So far off-the-shelf algorithms like shortest path routing, which optimize the number of hops over which flows are routed, have been used for computing routes for the time-triggered traffic. In this paper, we explore how the routing of time-triggered flows affects their schedulability. Moreover, we identify additional parameters that must be considered while routing time-triggered traffic and propose ILP-based algorithms for the purpose. Our evaluations show that the proposed routing algorithms could improve the slack in the computed schedules by upto 60 % and 30 % compared to shortest path routing and equal cost multi-pathing (ECMP), respectively, and, thus, increase the capacity of the network to accommodate more time-triggered traffic.

Incremental Flow Scheduling and Routing in Time-Sensitive Software-Defined Networks

Several networking architectures have been developed atop IEEE 802.3 networks to provide real-time communication guarantees for time-sensitive applications in industrial automation systems. The basic principle underlying these technologies is the precise transmission scheduling of time-triggered traffic through the network for providing deterministic and bounded latency and jitter. These transmission schedules are typically synthesized offline (computational time in the order of hours) and remain fixed thereafter, making it difficult to dynamically add or remove network applications. This paper presents algorithms for incrementally adding time-triggered flows in a time-sensitive software-defined network (TSSDN). The TSSDN is a network architecture based on software-defined networking, which provides real-time guarantees for time-triggered flows by scheduling their transmissions on the hosts (network edge) only. These algorithms exploit the global view of the control plane on the data plane to schedule and route time-triggered flows needed for the dynamic applications in the Industrial Internet of Things (Industry 4.0). The evaluations show that these algorithms can compute incremental schedules for time-triggered flows in subseconds with an average relative optimality of 68%.