In energy harvesting communication systems, it is possible for a transmitter to schedule the transmission by jointly scaling the rate and turning the transmitter ON/OFF adaptively. Such a joint rate and sleep schedule can greatly increase the throughput achieved by the transmitter with battery constraints. However, most existing works on joint rate and sleep scheduling assume the transition between different states does not have any cost, i.e., energy or time consumption. This is not realistic while the energy and time needed for booting a transmitter, i.e., turning a transmitter from OFF to ON, are not small enough to be ignored in most cases. In this paper, we investigate the joint rate and sleep scheduling on system throughput with more general booting consumption considered in energy harvesting communication systems. We first identify the structural properties of the optimal solution for the model with booting consumption considered. Inspired by these observations, we develop an optimal offline algorithm and an online heuristic algorithm to solve the problem. Experimental results from simulations and real tests show that the proposed algorithms can achieve much higher throughput on average in a realistic energy harvesting communication system, compared to those algorithms that only consider rate scheduling or ignore the booting consumption.

A scaled smart city consists of a combination of infrastructure and vehicular agents working in concert to direct traffic throughout the vehicular network at a smaller-thanlife scale. In this paper, we outline our plans for routing to satisfy arrival deadlines, where vehicles are routed with the primary objective of getting somewhere on time. We consider vehicle routing through a traffic sub-network, using a centralized scheme as a guiding traffic assignment agent. We introduce our preliminary implementation of a routing algorithm built on the Selfless Traffic Routing (STR) model and Dynamic User Equilibrium (DUE) to show the viability of such a scheme on a traffic network. We present our experimental results from running this scheme on a real-world traffic network.

In this paper, we consider the scheduling of dual-criticality sporadic task systems with arbitrary deadlines using demand bound functions. In dual-criticality systems, tasks are assigned either low-criticality or high-criticality based on assurance needs with associated worst-case execution times. Arbitrary deadlines are those that allow the deadline to be larger than the minimum separation between consecutive task instances. Demand bound functions have been used to successfully schedule dual-criticality task sets for constrained deadlines, i.e., deadlines that are always less than or equal to minimum inter-arrival separation time. We formulate a new demand bound function for a more generalized dual-criticality task system with both constrained and arbitrary deadlines on a preemptive uniprocessor.

In embedded real-time virtualized systems (ERTVS), the ARINC 653 standard specifies a cyclic scheduling policy to guarantee the real-time performance of tasks in multiple Virtual Machines (VMs) residing on shared hardware. Based on this policy, the Regularity-based Resource Partitioning (RRP) model defines an efficient interface specification to hierarchically partition and assign resource slices among VMs. Although this model has received plenty of attention recently, three major pieces remain missing for applying this model in ERTVS. (1) Embedded systems are more sensitive to resource utilization efficiency since this may drastically affect their deployment cost for including additional cores. Therefore, this paper proposes an optimal and an approximate RRP resource scheduler for multi-core platforms. (2) A resource reconfiguration is required when an embedded system has to switch between operating modes, resulting in the current cyclic schedule being replaced by another pre-configured and verified cyclic schedule. This paper formalizes a new One-Hop Reconfiguration (OHR) problem tailored for mode-switch-capable embedded systems and introduces a corresponding optimal solution. (3) No RRP-based toolset is currently available for embedded systems. This paper thus presents an optimized RRP toolset tailored for embedded systems. Numerous experiments are conducted to evaluate the efficacy of this toolset.

A multitude of cloud-native applications take up a significant share of today's world wide web, the majority of which implicitly require soft-real-time guarantees when hosted on servers at various data centers across the globe. With the rapid development of cloud computing and virtualization techniques, many applications have been moved onto cloud and edge platforms that require efficient virtualization techniques. This means a set of applications must be executed on a Virtual Machine (VM) and multiple VMs must be temporally and spatially scheduled on a set of CPUs. Designed to leverage the cloud infrastructure model, many of these cloud-native applications such as media servers strongly demand low data latency and high compute-resource availability, both of which must be predictable. However, state-of-art VM schedulers fail to satisfy these requirements simultaneously. The scheduling of cloud-native applications on VMs and the scheduling of VMs on physical resources (CPUs), collectively need to be real-time in nature as specified by the Hierarchical Real-Time Scheduling (HiRTS) framework. Conforming to the specifications of this framework, the Regularity-based Resource Partitioning (RRP) model has been proposed that introduces the concept of regularity to provide a near-ideal resource supply to all VMs. In this paper, we make the theoretically superior Regularity-based Resource Partitioning (RRP) model ready for prime time by implementing its associated resource partitioning algorithms for the first time ever on the popular x-86 open-source hypervisor Xen, i.e., RRP-Xen. This paper also compares and contrasts the real-time performance of RRP-Xen against contemporary Xen schedulers such as Credit and RTDS. Our contributions include: (1) a novel implementation of the RRP model on Xen's x-86 based hypervisor, thereby providing a test-bed for future researchers; (2) the first-ever multi-core ARINC 653 VM scheduler prototype on Xen; and (3) numerous experiments and theoretical analysis to determine the real-time performance of RRP-Xen under a stringent workload environment.

Embedded real-time virtualized systems serve a wide range of functions for many important industries. They can encompass multiple independent applications sharing limited computational resources. Many models have been introduced to ensure reliability and energy efficiency for these systems. Hierarchical Real-Time Scheduling (HiRTS) is a framework to enable the sharing of resources. It is used alongside the Regularity-Based Resource Partition model (RRP) to achieve transparent scheduling. A checkpointing system is an effective method to resolve transient faults. However, checkpoint insertions are known to incur high time and energy overheads. This paper proposes a two-state regularity-based checkpointing model within the HiRTS framework. It will ensure fault tolerance when scheduling independent, mixedcriticality real-time task sets on limited resources. By reducing checkpoint insertions before the first fault, the system will achieve higher utilization and less overhead while still ensuring fault tolerance. The simulation-based experiments presented suggest the model could offer a significant increase in utilization and reliability. They will be used as a guideline for future simulations using more complex schedules.

As virtualization becomes increasingly popular, more critical applications that require real-time performance guarantees are deployed on virtualized systems. In such systems, the Hierarchical Real-Time Scheduling (HiRTS) framework divides the scheduling problem into task-level scheduling and resource-level scheduling. Specifically, resource-level scheduling divides a physical resource into multiple resource partitions while task-level scheduling schedules the tasks on each resource partition. Accordingly, the Regularity-based Resource Partitioning (RRP) model offers efficient resource-level scheduling. Despite the availability of adequate resource-level tools, the task-level scheduling based on the RRP model still has a scope for improvements. To extend the applicability of the RRP model under a variety of task workload environments, this paper offers: (1) a more practical schedulability test for independent tasks whose key parameters, i.e., Worst-Case Execution Time (WCET), period and deadline, are non-integral multiples of a time slice; (2) a tuned Earliest Deadline First (EDF) scheduling approach and a corresponding schedulability test for intra-VM dependent tasks; (3) schedulability tests for self-suspended tasks based on Fixed-Relative-Deadline (FRD) scheduling strategies.

Many safety-critical applications employ embedded real-time systems where both timing and fault tolerance requirements must be continually satisfied. The Regularity-based Resource Partition Model (RRP), which is known for its code level independence between resource level and task level, is used to schedule resource partitions in virtualized real-time systems. This paper presents a fault tolerance model for Regularity-based Real-Time Virtual Resources to recover from transient hardware faults without modifying user applications. The proposed framework consists of a checkpointing mechanism called Fault-Tolerant RRP with a checkpointing partition followed by a redundancy partition prepared for re-execution to satisfy task deadlines despite the occurrence of faults. The frequency of checkpoints and the number of time slices in the redundancy partition are parameterized by the fault rate of the hardware resource and the sum of the availability factors of the original partition sets. Extensive theoretical analysis and simulation-based experiments show the effectiveness of the proposed framework while incurring minimal overhead.

With increasing traffic in urban areas, it is crucial to examine strategies to reduce traffic network congestion. Popular navigation policies currently tend to select the fastest path available for each vehicle. However, a top-down approach to navigation, which considers the traffic network as a whole, offers several speedup possibilities. Minimizing the average travel time of all vehicles in the network with respect to their separate travel deadlines improves traffic throughput. Because such a strategy does not guarantee an optimal navigation route for individual vehicles, we refer to it as a "selfless" policy and based on this observation we propose the Selfless Traffic Routing (STR) model. Hence, we propose a test bed based on Simulation of Urban MObility (SUMO) that can evaluate the performance of a traffic routing policy based on the average travel time of all vehicle agents in a given traffic grid. Continuously calculating optimal actions for multiple agents in real-time is computationally complex. We therefore introduce a value-based reinforcement learning strategy to achieve the benefits offered by a selfless traffic routing model. We explore how this approach can potentially achieve an optimal balance between action quality and the real-time performance of each decision.