Chains of tasks are ubiquitous and used in a broad spectrum of applications. In these chains, tasks execute according to their timing. Then, they communicate by writing to and reading from shared memory. The schedule of tasks and the read/write instants are naturally subject to uncertainties (variability in the execution time, interference due to shared resources of higher priority tasks, etc.). Despite the impact of uncertainties, we believe that current analysis of task chains cannot handle them properly. In this paper, we borrow the notion of jitter to model uncertainties and we propose a novel event model that explicitly captures jitter in read and write operations, decoupled from task scheduling. We develop a (linear-time complexity) compositional analysis framework that tracks how this jitter propagates across chains and impacts metrics such as reaction time, data age, and end-to-end latency. Our model supports arbitrary communication paradigms (e.g., implicit, LET, mid-execution) and is applicable to the analysis of real-world frameworks such as ROS2 without requiring intrusive changes.

The expansion of the Internet of vehicles (IoV) has spurred a significant increase in the demand for vehicular computation tasks, posing challenges for in-vehicle task processing. Multi-access edge computing (MEC), which is intended for low-latency task execution, experiences sub-band competition and workload imbalance due to the uneven distribution of vehicle densities. This paper presents a novel IoV architecture leveraging multi-roadside-unit (RSU) capabilities to facilitate efficient load balancing among RSUs through edge-to-edge collaboration. The optimization problem of computation offloading is formulated by minimizing overall task delay, which is further decoupled into two sub-problems: communication resource allocation and load balancing. We devise a two-stage deep reinforcement learning-based communication resource allocation and load balancing (DRLCL) algorithm to tackle these sub-problems sequentially. Based on real-world vehicle trajectories, experimental evaluations reveal that our proposed algorithm outperforms the baselines in reducing overall delay.

The performance benefits of reconfigurable intelligent surfaces (RISs) in enhancing communication capability and coverage are constrained by the static placement of the RIS. However, aerial-RIS, where the RIS is deployed on an unmanned aerial vehicle (UAV), has demonstrated superior potential to improve system performance due to the mobility and adaptability of the UAV. In this paper, we investigate a UAV-mounted RIS (UAV-RIS) relaying system and formulate an optimization problem aimed at maximizing the system sum rate. To address this, we propose a novel algorithm named DL-UTBO, that integrates deep reinforcement learning with a deep neural network (DNN) to jointly optimize the UAV trajectory, base station (BS) active beamforming, and RIS passive beamforming. The simulation results underscore the effectiveness and superiority of the proposed algorithm compared to existing baseline methods.

In urban environments, direct communication links between a base station (BS) and user equipment (UEs) are often obstructed by buildings. To mitigate these blockages, we integrate unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to enhance system flexibility and improve transmission efficiency. This paper investigates an RIS-assisted multi-user multiple-input single-output (MU-MISO) downlink system, where the RIS is mounted on a UAV. To maximize the system rate while minimizing the UAV's energy consumption and flight duration, we formulate a multi-objective optimization problem. To address this problem, we propose a hybrid algorithm that integrates the soft deep deterministic policy gradient (SD3) algorithm with a graph neural network (GNN) architecture, named SD3-GNN-RIS. The original problem is decomposed into two subproblems: joint active beamforming at the BS and passive beamforming at the RIS, optimized via a GNN-based approach, and three-dimensional (3D) UAV trajectory optimization, formulated as a Markov decision process and solved using the SD3 algorithm. Simulation results demonstrate the superior performance of the proposed algorithm compared to baseline methods in terms of system rate, energy efficiency, and UAV trajectory optimization.

Unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) systems are considered a promising solution, especially in disaster scenarios. Furthermore, recon-figurable intelligent surfaces (RISs) have emerged as a novel technology aimed at enhancing the wireless propagation environment in wireless networks. This paper proposes a multi-UAV assisted MEC system with partial task offloading, leveraging RIS to augment the communication performance between ground terminals (GTs) and UAVs. The pre-set hovering positions for the UAVs are determined by clustering the GTs using the K- means algorithm. To minimize system delay and ensure fairness among GTs, the computation offloading strategy and phase-shift of the RIS are optimized using the multi-agent deep deterministic policy gradient (MADDPG) algorithm, a form of multi-agent deep reinforcement learning. Simulation results demonstrate that the proposed cluster-aided RIS-MADDPG algorithm significantly enhances the system delay and fairness performance of the RIS-assisted multi-UAV MEC system when compared to benchmark solutions.

Unmanned aerial vehicles (UAVs) are regarded as a promising solution for mobile edge computing (MEC) systems due to their flexibility and capability to provide computing services to ground terminals (GTs). By leveraging UAVs, the latency in computation tasks can be reduced significantly, particularly in disaster scenarios. Additionally, Reconfigurable Intelligent Surfaces (RIS) have emerged as a novel technology for enhancing the wireless propagation environment in wireless networks. This paper proposes a multi-UAV assisted MEC system where computation tasks of GTs can be computed locally or partially offloaded to UAVs. Furthermore, practical RIS phase shift designs are considered to enhance the communication performance between GTs and UAVs. To minimize the system delay and achieve fairness among GTs, the computation offloading strategy, trajectory of the UAVs are optimized using a markov decision process. Simultaneously, the RIS phase shift is optimized through an alternating optimization algorithm. Additionally, a cooperative multi-agent deep reinforcement learning framework is developed to obtain a optimal solution by employing the multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm. Numerical results indicate that MATD3 can effectively improve the system delay and fairness performance of the RIS-assisted multi-UAV MEC system, as compared to benchmark solutions.

In recent years, the maturation of modern multicore processor technology and its increasing adoption in critical industrial domains have posed significant challenges for real-time systems, primarily due to contention for shared cache resources and the resulting uncertainty. To address this issue, contemporary processors employ cache partitioning techniques, enhancing temporal predictability by isolating cache access among processor cores. However, this isolation technique may lead to real-time tasks missing their deadlines due to an insufficient number of cache partitions. Consequently, this paper investigates the schedulability of preemptive global Earliest Deadline First (EDF) real-time scheduling algorithms that support dynamic cache allocation. We propose an innovative LAG-based schedulability analysis method for these algorithms and present a utilization-based schedulability condition that reduces analysis time complexity while improving analysis accuracy. Lastly, the performance and efficiency of the proposed schedulability determination method are validated through simulation experiments with randomly generated tasks.