Semi-partitioned mixed-criticality scheduling

Paper presented at the 30TH INTERNATIONAL CONFERENCE ON ARCHITECTURE OF COMPUTING SYSTEMS (ARCS 2017). 3 to 6, Apr, 2017, Session 6: Scheduling. Vienna, Austria.Scheduling isolation in mixed-criticality systems is challenging without sacrificing performance. In response, we propose a scheduling approach that combines server-based semi-partitioning and deadline scaling. Semi-partitioning (whereby only some tasks migrate, in a carefully managed manner), hitherto used in single criticality systems, offers good performance with low overheads. Deadline-scaling selectively prioritizes high-criticality tasks in parts of the schedule to ensure their deadlines are met even in rares case of execution time overrun. Our new algorithm NPS-F-MC brings semi-partitioning to mixed-criticality scheduling and uses Ekberg and Yi’s state-of-the-art deadline scaling approach. It ensures scheduling isolation among different-criticality tasks and only allows low-criticality task migration. We also explore variants that disallow migration entirely or relax the isolation between different criticalities (SP-EKB) in order to evaluate the performance tradeoffs associated with more flexible or rigid safety and isolation requirements.info:eu-repo/semantics/publishedVersio

A Semi-partitioned Model for Scheduling Mixed Criticality Multi-core Systems

Many Mixed Criticality scheduling algorithms have been developed with an assumption that lower criticality level tasks may be abandoned to guarantee the schedulability of higher criticality tasks when the criticality level of the system changes. But it is valuable to explore means by which all of the tasks remain schedulable throughout criticality level changes. This thesis introduces a semi-partitioned model which allows all of the tasks to remain schedulable if only a bounded number of cores increase their criticality levels. In such a model, some lower criticality tasks are allowed to migrate instead of being abandoned. Different possible semi-partitioned approaches are proposed and analysed in this thesis. It is concluded from the experiments results that the semi-partitioned algorithm provides improved schedulability and performance of multi-core mixed criticality systems while enables all tasks to keep executing in the majority of scenarios

A Semi-Partitioned Model for Mixed Criticality Systems

Many Mixed Criticality algorithms have been developed with an assumption that lower criticality-level tasks may be abandoned in order to guarantee the schedulability of higher-criticality tasks when the criticality level of the system changes. But it is valuable to explore means by which all of the tasks remain schedulable through these criticality level changes. This paper introduces a semi-partitioned model for a multi-core platform that allows all of the tasks to remain schedulable if only a bounded number of cores increase their criticality level. In such a model, some lower-criticality tasks are allowed to migrate instead of being abandoned. Detailed response time analysis for this model is derived. This paper also introduces possible approaches for establishing migration routes. Together with related previous work, an appropriate semi-partitioned model for mixed criticality systems hosted on multi-core platforms is recommended

Hierarchical Scheduling for Real-Time Periodic Tasks in Symmetric Multiprocessing

In this paper, we present a new hierarchical scheduling framework for periodic tasks in symmetric multiprocessor (SMP) platforms. Partitioned and global scheduling are the two main approaches used by SMP based systems where global scheduling is recommended for overall performance and partitioned scheduling is recommended for hard real-time performance. Our approach combines both the global and partitioned approaches of traditional SMP-based schedulers to provide hard real-time performance guarantees for critical tasks and improved response times for soft real-time tasks. Implemented as part of VxWorks, the results are confirmed using a real-time benchmark application, where response times were improved for soft real-time tasks while still providing hard real-time performance

Overhead Based Cluster Scheduling of Mixed Criticality Systems on Multicore Platform

The cluster-based technique is gaining focus for scheduling tasks of mixed-criticality (MC) real-time multicore systems. In this technique, the cores of the MC system are distributed in groups known as clusters. When all cores are distributed in clusters, the tasks are partitioned into clusters, which are scheduled on the cores within each cluster using a global approach. In this study, a cluster-based technique is adopted for scheduling tasks of real-time mixed-criticality systems (MCS). The Decreasing Criticality Decreasing Utilization with the worst-fit (DCDU-WF) technique is used for partitioning of tasks to clusters, whereas a novel mixed-criticality cluster-based boundary fair (MC-Bfair) scheduling approach is used for scheduling tasks on cores within clusters. The MC-Bfair scheduling algorithm reduces the number context switches and migration of tasks, which minimizes the overhead of mixed-criticality tasks. The migration and context switch overhead time is added at the time of each migration and context switch respectively for a task. In low critical mode, the low mode context switch and migration overhead time is added to task execution time, while the high mode overhead time of migration and context switch is added to the execution time of a task in high critical mode. The results obtained from experiments show the better schedulablity performance of proposed cluster-based technique as compared to cluster-based fixed priority (CB-FP), MC-EKG-VD-1, global and partitioned scheduling techniques e.g., for target utilization U=0.6, the proposed technique schedule 66.7% task sets while MC-EKG-VD-1, CB-FP, partitioned and global techniques schedule 50%, 33.3%, 16.7% and 0% task sets respectively

MARACAS: a real-time multicore VCPU scheduling framework

This paper describes a multicore scheduling and load-balancing framework called MARACAS, to address shared cache and memory bus contention. It builds upon prior work centered around the concept of virtual CPU (VCPU) scheduling. Threads are associated with VCPUs that have periodically replenished time budgets. VCPUs are guaranteed to receive their periodic budgets even if they are migrated between cores. A load balancing algorithm ensures VCPUs are mapped to cores to fairly distribute surplus CPU cycles, after ensuring VCPU timing guarantees. MARACAS uses surplus cycles to throttle the execution of threads running on specific cores when memory contention exceeds a certain threshold. This enables threads on other cores to make better progress without interference from co-runners. Our scheduling framework features a novel memory-aware scheduling approach that uses performance counters to derive an average memory request latency. We show that latency-based memory throttling is more effective than rate-based memory access control in reducing bus contention. MARACAS also supports cache-aware scheduling and migration using page recoloring to improve performance isolation amongst VCPUs. Experiments show how MARACAS reduces multicore resource contention, leading to improved task progress.http://www.cs.bu.edu/fac/richwest/papers/rtss_2016.pdfAccepted manuscrip

Graceful Degradation in Semi-Clairvoyant Scheduling

In the Vestal model of mixed-criticality systems, jobs are characterized by multiple different estimates of their actual, but unknown, worst-case execution time (WCET) parameters. Some recent research has focused upon a semi-clairvoyant model for mixed-criticality systems in which it is assumed that each job reveals upon arrival which of its WCET parameters it will respect. We study the problem of scheduling such semi-clairvoyant systems to ensure graceful degradation of service to less critical jobs in the event that the systems exhibit high-criticality behavior. We propose multiple different interpretations of graceful degradation in such systems, and derive efficient scheduling algorithms that are capable of ensuring graceful degradation under these different interpretations

A Survey of Research into Mixed Criticality Systems

This survey covers research into mixed criticality systems that has been published since Vestal’s seminal paper in 2007, up until the end of 2016. The survey is organised along the lines of the major research areas within this topic. These include single processor analysis (including fixed priority and EDF scheduling, shared resources and static and synchronous scheduling), multiprocessor analysis, realistic models, and systems issues. The survey also explores the relationship between research into mixed criticality systems and other topics such as hard and soft time constraints, fault tolerant scheduling, hierarchical scheduling, cyber physical systems, probabilistic real-time systems, and industrial safety standards

Resource-Efficient Scheduling Of Multiprocessor Mixed-Criticality Real-Time Systems

Timing guarantee is critical to ensure the correctness of embedded software systems that interact with the physical environment. As modern embedded real-time systems evolves, they face three challenges: resource constraints, mixed-criticality, and multiprocessors. This dissertation focuses on resource-efficient scheduling techniques for mixed-criticality systems on multiprocessor platforms. While Mixed-Criticality (MC) scheduling has been extensively studied on uniprocessor plat- forms, the problem on multiprocessor platforms has been largely open. Multiprocessor al- gorithms are broadly classified into two categories: global and partitioned. Global schedul- ing approaches use a global run-queue and migrate tasks among processors for improved schedulability. Partitioned scheduling approaches use per processor run-queues and can reduce preemption/migration overheads in real implementation. Existing global scheduling schemes for MC systems have suffered from low schedulability. Our goal in the first work is to improve the schedulability of MC scheduling algorithms. Inspired by the fluid scheduling model in a regular (non-MC) domain, we have developed the MC-Fluid scheduling algo- rithm that executes a task with criticality-dependent rates. We have evaluated MC-Fluid in terms of the processor speedup factor: MC-Fluid is a multiprocessor MC scheduling algo- rithm with a speed factor of 4/3, which is known to be optimal. In other words, MC-Fluid can schedule any feasible mixed-criticality task system if each processor is sped up by a factor of 4/3. Although MC-Fluid is speedup-optimal, it is not directly implementable on multiprocessor platforms of real processors due to the fractional processor assumption where multiple task can be executed on one processor at the same time. In the second work, we have considered the characteristic of a real processor (executing only one task at a time) and have developed the MC-Discrete scheduling algorithm for regular (non-fluid) scheduling platforms. We have shown that MC-Discrete is also speedup-optimal. While our previous two works consider global scheduling approaches, our last work con- siders partitioned scheduling approaches, which are widely used in practice because of low implementation overheads. In addition to partitioned scheduling, the work consid- ers the limitation of conventional MC scheduling algorithms that drops all low-criticality tasks when violating a certain threshold of actual execution times. In practice, the system designer wants to execute the tasks as much as possible. To address the issue, we have de- veloped the MC-ADAPT scheduling framework under uniprocessor platforms to drop as few low-criticality tasks as possible. Extending the framework with partitioned multiprocessor platforms, we further reduce the dropping of low-criticality tasks by allowing migration of low-criticality tasks at the moment of a criticality switch. We have evaluated the quality of task dropping solution in terms of speedup factor. In existing work, the speedup factor has been used to evaluate MC scheduling algorithms in terms of schedulability under the worst-case scheduling scenario. In this work, we apply the speedup factor to evaluate MC scheduling algorithms in terms of the quality of their task dropping solution under various MC scheduling scenarios. We have derived that MC-ADAPT has a speedup factor of 1.618 for task dropping solution

Mixed-criticality real-time task scheduling with graceful degradation

”The mixed-criticality real-time systems implement functionalities of different degrees of importance (or criticalities) upon a shared platform. In traditional mixed-criticality systems, under a hi mode switch, no guaranteed service is provided to lo-criticality tasks. After a mode switch, only hi-criticality tasks are considered for execution while no guarantee is made to the lo-criticality tasks. However, with careful optimistic design, a certain degree of service guarantee can be provided to lo-criticality tasks upon a mode switch. This concept is broadly known as graceful degradation. Guaranteed graceful degradation provides a better quality of service as well as it utilizes the system resource more efficiently. In this thesis, we study two efficient techniques of graceful degradation. First, we study a mixed-criticality scheduling technique where graceful degradation is provided in the form of minimum cumulative completion rates. We present two easy-to-implement admission-control algorithms to determine which lo-criticality jobs to complete in hi mode. The scheduling is done by following deadline virtualization, and two heuristics are shown for virtual deadline settings. We further study the schedulability analysis and the backward mode switch conditions, which are proposed and proved in (Guo et al., 2018). Next, we present a probabilistic scheduling technique for mixed-criticality tasks on multiprocessor systems where a system-wide permitted failure probability is known. The schedulability conditions are derived along with the processor allocation scheme. The work is extended from (Guo et al., 2015), where the probabilistic model is first introduced for independent task scheduling on a uniprocessor platform. We further consider the failure dependency between tasks while scheduling on multiprocessor platforms. We provide related theoretical analysis to show the correctness of our work. To show the effectiveness of our proposed techniques, we conduct a detailed experimental evaluation under different circumstances”--Abstract, page iii