A Lyapunov-based completion-aware completion-aware MAC scheduler that jointly captures the set-level completion dependency, deadline urgency, and completion feasibility of each head-of-line PDU set is proposed.
Abstract
Real-time extended reality (XR) services impose stringent throughput, latency, and reliability requirements. An XR media unit is commonly segmented into a protocol data unit (PDU) set that is useful only when all required PDUs are delivered within its delay budget. Existing PDU set-aware medium access control (MAC) schedulers generally prioritize transmissions according to urgency or progress, without explicitly accounting for whether a PDU set can still be completed on time. In this paper, we propose a Lyapunov-based completion-aware MAC scheduler that jointly captures the set-level completion dependency, deadline urgency, and completion feasibility of each head-of-line PDU set. Long-term quality-of-service (QoS) requirements are represented through virtual debt queues, and resources are allocated according to their marginal contribution to the timely-completion probability of each head-of-line PDU set. These components are integrated into a drift-plus-penalty scheduling metric that balances long-term reliability requirements against current completion opportunities. System-level simulations demonstrate that the proposed scheduler improves PDU set delivery reliability and lower-tail performance under resource contention, while providing robust performance across heterogeneous traffic demands and network conditions.
The multi-agent transformer (MAT) is adopted to model inter-queue dependencies via attention over agents' observations and actions, enabling implicit coordination across heterogeneous co-located XR applications and results show that the proposed method outperforms baselines.
Marcos Carvalho, Fatih Temiz, Shavbo Salehi et al.· 0 citations
The rapid proliferation of Internet of Things (IoT) devices has intensified demands for low-latency, resource-efficient task scheduling at the network edge. Conventional policies such as Round-Robin and First-Come-First-Serve (FCFS) fail to satisfy the Quality-of-Service (QoS) requirements of Industrial-IoT and autonomous-vehicle workloads. This paper presents Multi-Queue Priority-Based Scheduling (MQPBS), a lightweight algorithm that classifies tasks into three dynamic priority queues (High, Medium, Low) using deadline-aware heuristics, applies Shortest-Job-First (SJF) intra-queue ordering, and employs an aging mechanism to prevent starvation. Extensive simulation over task sets of 200–1000 tasks demonstrates that MQPBS reduces average waiting time by up to 17.6%, improves throughput by up to 10.8%, lowers energy consumption by 20%, and cuts the Deadline Miss Ratio (DMR) compared with the Priority-Aware Task-Scheduling (PaTS) baseline. Ablation experiments confirm the independent contribution of each algorithmic component. Scalability and sensitivity analyses further validate the robustness of MQPBS under heterogeneous arrival patterns and varying load intensities. The results establish MQPBS as a scalable, reliable scheduler for next-generation edge infrastructures.
Shibang Maity, Roshan Panda, M. Tanisha et al.· International Conference on...· 0 citations
: Mobile edge services require deterministic communication across Time-Sensitive Networking (TSN) and 5G access, where the standardized integration architecture exposes the 5G System (5GS) to the TSN controller as a logical bridge. We study dynamic admission of time-triggered (TT) flows using reported 5GS bridge delay and TSN-to-5GS Quality of Service (QoS) mapping in route selection and Gate Control List (GCL) scheduling. Arrivals and departures can split available transmission time into noncontiguous windows. Online insertion preserves admitted schedules but may reduce subsequent schedulability, whereas full recomputation can restore schedulability but changes many routes and GCL entries, complicating coordinated activation. Coupling routing and GCL scheduling under timing, bridge-delay, QoS-mapping, and a bound on changes to admitted schedules yields an NP-hard problem. To address it, we propose a two-timescale scheduling mechanism. The fast timescale uses current 5GS bridge information to place arrivals without modifying admitted flows. Fragmented windows, repeated insertion failures, or changes in reported 5GS state invoke the slower timescale, which sequentially reschedules a bounded subset of admitted flows and commits only feasible improvements. At 0.95 offered load across A380, CEV, and Ring6, admission improves by 14.8–18.5 percentage points over online-only scheduling and remains within 1.7–2.2 points of full recomputation, while per-event runtime falls by over one order of magnitude with limited GCL changes.
Zhi-Hao Liu, Yi Zhang, Wei Zhang et al.· Computers, Materials & C...· 0 citations
Time-sensitive networking (TSN) has emerged as a foundational technology to ensure deterministic communication in industrial automation, automotive, and avionics systems. A persistent challenge in TSN is the joint planning of routing and scheduling for time-triggered (TT) flows, where sequential time-slot reservations often lead to fragmentation, dispersing residual bandwidth and limiting subsequent flow allocations. This fragmentation effect, despite its significant impact on network performance, has received limited attention. To address this issue, we first develop a fragmentation-aware metric that quantifies the availability of residual time slots through integer partitioning. Then, a similarity model integrating source–destination and period similarity features is introduced to aggregate compatible TT flows along common routes, thereby improving bandwidth utilization. Finally, we propose a joint planning method for routing and scheduling (JPMRS) grounded in integer splitting theory. Simulation results demonstrate that JPMRS substantially improves TT flow scheduling success rate, reduces best-effort (BE) latency, and enhances overall system efficiency in TSN environments.
Yinzhi Lu, Liu Yang, Xinyue Li et al.· IEEE Systems Journal· 0 citations
Paxos consensus protocol is widely used in distributed systems, yet their performance can degrade under heterogeneous workloads and deadline-constrained requests. Traditional priority-based Paxos extensions rely on static scheduling policies that are unable to adapt to dynamically changing urgency. This paper proposes a deadline-aware scheduling framework for Paxos based on the Shortest Remaining Processing Time (SRPT) discipline. The proposed approach dynamically prioritizes requests according to their expected completion behavior and their likelihood of meeting assigned deadlines, enabling preemptive scheduling decisions at the consensus leader. An analytical queueing model is developed to characterize the mean waiting time, mean residence time, and mean response time under SRPT scheduling. Using these results, a probabilistic measure of deadline satisfaction is derived and employed to guide scheduling decisions. Analytical and numerical evaluations demonstrate that the proposed framework significantly improves deadline satisfaction while preserving the delay-optimal properties of SRPT, making it well suited for latency-sensitive Paxos deployments.
Elham Amini, J. Misic, Vojislav B. Mišić· IEEE Transactions on Network...· 0 citations
Satellite–ground integrated networks (SAGIN) enable wide-area support for latency-sensitive interactive applications such as UAV teleoperation and remote robotic control. Unlike conventional data services that focus on average latency or throughput, these applications operate in closed-loop feedback cycles in which perception frames and control actions must be synchronized and satisfy strict deadlines. Late-arriving data often become obsolete rather than recoverable. Moreover, practical edge servers execute tasks in discrete scheduling cycles, introducing tick-quantized completion times that directly affect deadline violations. This paper proposes a heterogeneous satellite–ground mobile edge computing framework in which terrestrial base stations (BSs) and satellites (SATs) both provide computing services with distinct capacity characteristics. We develop a server-side queuing and scheduling model that captures continuous frame generation, stochastic control inputs, buffer constraints, and discrete tick-based task completion. Based on this model, we formulate a multi-objective optimization problem that jointly determines user association, transmission power, and bandwidth allocation to minimize deadline violations, dropped tasks, and worst-case delay. To solve the resulting mixed-integer nonlinear program under dynamic satellite topology, we design a multi-critic reinforcement learning (RL) algorithm that decomposes synchronization, latency, and capacity constraints. Simulation results demonstrate substantial reductions in deadline violations and maximum delay compared with existing approaches, including proximal policy optimization (PPO) deep deterministic policy gradient (DDPG), greedy algorithm, proportional fairness scheme, random access strategy, and equal resource allocation method.
Wei-Qi Liu, Mohammad Arif Hossain, Nirwan Ansari· IEEE Transactions on Network...· 0 citations
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