Simulation of multi-core scheduling in real-time embedded systems

In real-time systems the correctness of a system depends not only on the logical correctness of the running program but also on the time at which the logically correct output is produced. Therefore, in such a system it is necessary to provide the right computational result within a strict time limit called the deadline of a task. In hard real-time systems the deadline of a task must not be missed, whereas in soft real-time systems it can be missed occasionally. In recent years the trend has been observed which shows a shift from single-core to multi-core architectures for real-time systems. The main point of this thesis is to study a few promising multi-core scheduling algorithms both from the partitioned and the global approaches to multi-core scheduling and implement some of them into the existing simulation software. To represent the partitioned approach, Partitioned EDF has been implemented with the capability of specification of a resource access protocol for each core. The partitioned approach requires heuristics for task partitioning, the problem known to be NP-hard in the strong sense. For this reason, the implementation of Partitioned EDF requires manual task partitioning of the system in order to be able to utilize the maximum processing power. Proportionate Fair abbreviated as Pfair is the only known optimal way to schedule a set of periodic tasks on multi-core systems that falls into the global approach of multi-core scheduling. Therefore, to represent the global approach, several variants of Pfair scheduling algorithm have been selected for the implementation into the existing system. To be truly useful in practice, a real-time multi-core scheduling algorithm should support access to shared resources using some resource access protocol. For this reason, the Flexible Multiprocessor Locking Protocol abbreviated as FMLP has been studied and implemented to simulate shared resource access on multi-core systems. This resource access protocol can be used by scheduling algorithms representing both the partitioned and global approaches, but it only supports such variants of those algorithms which allow non-preemptive execution. A variant of Global EDF termed Global Suspendable Non-preemptive EDF was implemented prior to implementing FMLP. The existing simulator provided a set of single-core and some basic multi-core scheduling algorithms for scheduling real-time task sets. No schedulability analysis was implemented in the previous work. So, as part of this thesis, the schedulability analysis for single core scheduling algorithms has been implemented. A schedulability analysis for the partitioned approach of multi-core scheduling has also been provided for systems where a single-core scheduling algorithm runs on each partition. The updated simulation software also supports self-suspension of tasks for a specified duration

An Empirical Analysis of Scheduling Techniques for Real-Time Cloud-Based Data Processing

In this paper, we explore the challenges and needs of current cloud infrastructures, to better support cloud-based data-intensive applications that are not only latency-sensitive but also require strong timing guarantees. These applications have strict deadlines (e.g., to perform time-dependent mission critical tasks or to complete real-time control decisions using a human-in-the-loop), and deadline misses are undesirable. To highlight the challenges in this space, we provide a case study of the online scheduling of MapReduce jobs executed by Hadoop. Our evaluations on Amazon EC2 show that the existing Hadoop scheduler is ill-equipped to handle jobs with deadlines. However, by adapting existing multiprocessor scheduling techniques for the cloud environment, we observe significant performance improvements in minimizing missed deadlines and tardiness. Based on our case study, we discuss a range of challenges in this domain posed by virtualization and scale, and propose our research agenda centered around the application of advanced real-time scheduling techniques in the cloud environment

A Response-Time Analysis for Non-Preemptive Job Sets under Global Scheduling

An effective way to increase the timing predictability of multicore platforms is to use non-preemptive scheduling. It reduces preemption and job migration overheads, avoids intra-core cache interference, and improves the accuracy of worst-case execution time (WCET) estimates. However, existing schedulability tests for global non-preemptive multiprocessor scheduling are pessimistic, especially when applied to periodic workloads. This paper reduces this pessimism by introducing a new type of sufficient schedulability analysis that is based on an exploration of the space of possible schedules using concise abstractions and state-pruning techniques. Specifically, we analyze the schedulability of non-preemptive job sets (with bounded release jitter and execution time variation) scheduled by a global job-level fixed-priority (JLFP) scheduling algorithm upon an identical multicore platform. The analysis yields a lower bound on the best-case response-time (BCRT) and an upper bound on the worst-case response time (WCRT) of the jobs. In an empirical evaluation with randomly generated workloads, we show that the method scales to 30 tasks, a hundred thousand jobs (per hyperperiod), and up to 9 cores.info:eu-repo/semantics/publishedVersio

Global scheduling on temperature-constrained multiprocessor real-time systems

In this thesis, we study temperature-constrained multiprocessor real-time systems, where real-time guarantees must be met without exceeding safe temperature levels within the processors. We focus on Pfair scheduling algorithms, especially ERfair scheduling scheme (a work-conserving extension to Pfair scheduling) as our main multiprocessor real-time scheduling methodology. Then, we study the benefits of simple reactive speed scaling as described in the real-time multiprocessor systems. In this thesis, in support of the temperature-awareness, we extend the applicability of the reactive speed scaling to global scheduling schemes for multiprocessors. We propose temperature-aware scheduling and processor selection schemes motivated by existing (thermally non-optimal) ERfair scheduling in order to reduce thermal stress and therefore increase the processor utilization. Then, we show that the proposed algorithm and reactive scheme can enhance the processor utilization compared with any constant speed scheme on real-time multiprocessor systems. Additionally, we show how the maximum schedulable utilization (MSU) for partitioning heuristics can be determined on the temperature-constrained multiprocessor real-time systems

A Response-Time Analysis for Non-Preemptive Job Sets under Global Scheduling

An effective way to increase the timing predictability of multicore platforms is to use non-preemptive scheduling. It reduces preemption and job migration overheads, avoids intra-core cache interference, and improves the accuracy of worst-case execution time (WCET) estimates. However, existing schedulability tests for global non-preemptive multiprocessor scheduling are pessimistic, especially when applied to periodic workloads. This paper reduces this pessimism by introducing a new type of sufficient schedulability analysis that is based on an exploration of the space of possible schedules using concise abstractions and state-pruning techniques. Specifically, we analyze the schedulability of non-preemptive job sets (with bounded release jitter and execution time variation) scheduled by a global job-level fixed-priority (JLFP) scheduling algorithm upon an identical multicore platform. The analysis yields a lower bound on the best-case response-time (BCRT) and an upper bound on the worst-case response time (WCRT) of the jobs. In an empirical evaluation with randomly generated workloads, we show that the method scales to 30 tasks, a hundred thousand jobs (per hyperperiod), and up to 9 cores

RT-OpenStack: CPU Resource Management for Real-Time Cloud Computing

Clouds have become appealing platforms for not only general-purpose applications, but also real-time ones. However, current clouds cannot provide real-time performance to virtual machines (VMs). We observe the demand and the advantage of co-hosting real-time (RT) VMs with non-real-time (regular) VMs in a same cloud. RT VMs can benefit from the easily deployed, elastic resource provisioning provided by the cloud, while regular VMs effectively utilize remaining resources without affecting the performance of RT VMs through pro per resource management at both the cloud and the hypervisor levels. This paper presents RT-OpenStack, a cloud CPU resource management system for co-hosting real-time and regular VMs. RT-OpenStack entails three main contributions: (1) integration of a real-time hypervisor (RT-Xen) and a cloud management system (OpenStack) through a real-time resource interface; (2) a realtime VM scheduler to allow regular VMs to share hosts with RT VMs without interfering the real-time performance of RT VMs; and (3) a VM-to-host mapping strategy that provisions real-time performance to RT VMs while allowing effective resource sharing with regular VMs. Experimental results demonstrate that RTOpenStack can effectively improve the real-time performance of RT VMs while allowing regular VMs to fully utilize the remaining CPU resources

Analysis and simulation of scheduling techniques for real-time embedded multi-core architectures

In this modern era of technological progress, multi-core processors have brought significant and consequential improvements in the available processing potential to the world of real-time embedded systems. These improvements impose a rapid increment of software complexity as well as processing demand placed on the underlying hardware. As a consequence, the need for efficient yet predictable multi-core scheduling techniques is on the rise. As part of this thesis, in-depth research of currently available multi-core scheduling techniques, belonging to both partitioned and global approaches, is done in the context of real-time embedded systems. The emphasis is on the degree of their usability on hard real-time systems, focusing on the scheduling techniques offering better processor affinity and the lower number of context switching. Also, an extensive research of currently available real-time test-beds as well as real-time operating systems is performed. Finally, a subset of the analyzed multi-core scheduling techniques comprising PSN-EDF, GSN-EDF, PD$^{2}$ and PD$^{2*}$ is simulated on the real-time test-bed LITMUS$^{RT}$

Response-Time Analysis of Limited-Preemptive Parallel DAG Tasks Under Global Scheduling

Most recurrent real-time applications can be modeled as a set of sequential code segments (or blocks) that must be (repeatedly) executed in a specific order. This paper provides a schedulability analysis for such systems modeled as a set of parallel DAG tasks executed under any limited-preemptive global job-level fixed priority scheduling policy. More precisely, we derive response-time bounds for a set of jobs subject to precedence constraints, release jitter, and execution-time uncertainty, which enables support for a wide variety of parallel, limited-preemptive execution models (e.g., periodic DAG tasks, transactional tasks, generalized multi-frame tasks, etc.). Our analysis explores the space of all possible schedules using a powerful new state abstraction and state-pruning technique. An empirical evaluation shows the analysis to identify between 10 to 90 percentage points more schedulable task sets than the state-of-the-art schedulability test for limited-preemptive sporadic DAG tasks. It scales to systems of up to 64 cores with 20 DAG tasks. Moreover, while our analysis is almost as accurate as the state-of-the-art exact schedulability test based on model checking (for sequential non-preemptive tasks), it is three orders of magnitude faster and hence capable of analyzing task sets with more than 60 tasks on 8 cores in a few seconds

Real-Time Wireless Sensor-Actuator Networks for Cyber-Physical Systems

A cyber-physical system (CPS) employs tight integration of, and coordination between computational, networking, and physical elements. Wireless sensor-actuator networks provide a new communication technology for a broad range of CPS applications such as process control, smart manufacturing, and data center management. Sensing and control in these systems need to meet stringent real-time performance requirements on communication latency in challenging environments. There have been limited results on real-time scheduling theory for wireless sensor-actuator networks. Real-time transmission scheduling and analysis for wireless sensor-actuator networks requires new methodologies to deal with unique characteristics of wireless communication. Furthermore, the performance of a wireless control involves intricate interactions between real-time communication and control. This thesis research tackles these challenges and make a series of contributions to the theory and system for wireless CPS. (1) We establish a new real-time scheduling theory for wireless sensor-actuator networks. (2) We develop a scheduling-control co-design approach for holistic optimization of control performance in a wireless control system. (3) We design and implement a wireless sensor-actuator network for CPS in data center power management. (4) We expand our research to develop scheduling algorithms and analyses for real-time parallel computing to support computation-intensive CPS