Current congestion control algorithms ignore the application-layer delay, where the untransmitted data waiting at source nodes degrades the delay performance of services. Moreover, differentiated priorities are necessary for the application services with various delay requirements, especially for mission-critical services. Different from the existing works only considering the network delay, in this paper, by adding the flow queueing delay at source nodes, we formulate the network utility maximization (NUM) problem with additional deterministic service delay constraints. We propose a general TCP-based two-timescale congestion window control (TCWC) framework with delay-aware priority to enhance traditional algorithms. Specifically, to handle the obstacle of new delay constraints, we transform them to the time-average stability of virtual queues. By solving the new NUM problem via Lyapunov optimization, we design a short-term congestion window adjustment strategy in each time slot. To further guarantee the service delay, we apply extreme value theory (EVT) to evaluate the priorities of different flows, and determine the long-term control of window update rates. We deploy the proposed framework in three classic algorithms including NewReno, Vegas and DCTCP. In addition, simulation results show that our TCWC framework can significantly reduce the average service delay and provide deterministic guarantees compared with time-aware TCP congestion control algorithms such as TIMELY and BBRv2.

Performance measurement is one of the key enablers towards the realization of future Smart Grids (SGs). However, balancing the trade-off among measurement completeness, accuracy, and timeliness under constrained resources, while providing targeted support for communication-intensive areas consisting of critical infrastructure remains a challenge. In this paper, we propose a hybrid network Performance Measurement (PM) framework for deterministic SGs in two steps. 1) To obtain PM information for the whole network without incurring overhead, passive measurement with missing data imputation is tailored for meeting the divergence of packet loss types of large-scale SGs based on the proposed Temporal-Spatial based Distinguishable Generative Adversarial Imputation Network (STD-GAIN), which explores and exploits both the spatial-temporal correlation and higher-order local correlation for refined imputation performance. 2) Regarding the circumstance that the reliability of the imputed data does not meet the requirement, active measurement is conducted at a finer granularity, i.e., along a specified path, where All-Pair Shortest Path (APSP) model is utilized to find the best paths. Finally, the simulation results show that the proposed framework could achieve complete, accurate and real-time network performance measurement for SGs.

Deterministic demand of mission-critical applications is essential in edge computing systems for realizing Industry 4.0. However, the conventional average-based network slicing schemes incur unexpected long-tail delay, resulting in the failure to meet strict deterministic delay guarantee. To resolve this issue, in this paper, we construct a two-scale TNS-Net architecture for the URLLC slice under the network slicing paradigm, aiming to meet deterministic end-to-end (E2E) delay requirements of multiple users with minimal resource usage. We consider multi-access edge computing (MEC) and model it as a many-to-one cascade queue, which includes the offloading queues at the user equipments (UEs) and a computation queue at the server. To analyze the delay performance, we decompose the offloading process into transmission and vacation periods, and employ the weighted approximation to address the multi-UE coupling in the computation process to derive the closed-form approximate E2E delay distribution. Based on the derived delay distribution, we propose an iterative two-scale network slicing (TNS) algorithm to guarantee deterministic delay, and construct a TNS-based deep-unfolding neural network, called TNS-Net, to improve the solution in presence of inaccurate channel statistics. Moreover, for the training of TNS-Net with deterministic delay as the network input, we apply extreme value theory (EVT) to analyze the distribution characteristic of delay bound violation. Finally, simulation results demonstrate that our theoretical analysis provides a relatively accurate estimate and the proposed TNS-Net ensures better delay guarantee with lower resource consumption.