Flexible Scheduling in Middleware for Distributed rate-based real-time applications - Doctoral Dissertation, May 2002

Distributed rate-based real-time systems, such as process control and avionics mission computing systems, have traditionally been scheduled statically. Static scheduling provides assurance of schedulability prior to run-time overhead. However, static scheduling is brittle in the face of unanticipated overload, and treats invocation-to-invocation variations in resource requirements inflexibly. As a consequence, processing resources are often under-utilized in the average case, and the resulting systems are hard to adapt to meet new real-time processing requirements. Dynamic scheduling offers relief from the limitations of static scheduling. However, dynamic scheduling offers relief from the limitations of static scheduling. However, dynamic scheduling often has a high run-time cost because certain decisions are enforced on-line. Furthermore, under conditions of overload tasks can be scheduled dynamically that may never be dispatched, or that upon dispatch would miss their deadlines. We review the implications of these factors on rate-based distributed systems, and posits the necessity to combine static and dynamic approaches to exploit the strengths and compensate for the weakness of either approach in isolation. We present a general hybrid approach to real-time scheduling and dispatching in middleware, that can employ both static and dynamic components. This approach provides (1) feasibility assurance for the most critical tasks, (2) the ability to extend this assurance incrementally to operations in successively lower criticality equivalence classes, (3) the ability to trade off bounds on feasible utilization and dispatching over-head in cases where, for example, execution jitter is a factor or rates are not harmonically related, and (4) overall flexibility to make more optimal use of scarce computing resources and to enforce a wider range of application-specified execution requirements. This approach also meets additional constraints of an increasingly important class of rate-based systems, those with requirements for robust management of real-time performance in the face of rapidly and widely changing operating conditions. To support these requirements, we present a middleware framework that implements the hybrid scheduling and dispatching approach described above, and also provides support for (1) adaptive re-scheduling of operations at run-time and (2) reflective alternation among several scheduling strategies to improve real-time performance in the face of changing operating conditions. Adaptive re-scheduling must be performed whenever operating conditions exceed the ability of the scheduling and dispatching infrastructure to meet the critical real-time requirements of the system under the currently specified rates and execution times of operations. Adaptive re-scheduling relies on the ability to change the rates of execution of at least some operations, and may occur under the control of a higher-level middleware resource manager. Different rates of execution may be specified under different operating conditions, and the number of such possible combinations may be arbitrarily large. Furthermore, adaptive rescheduling may in turn require notification of rate-sensitive application components. It is therefore desirable to handle variations in operating conditions entirely within the scheduling and dispatching infrastructure when possible. A rate-based distributed real-time application, or a higher-level resource manager, could thus fall back on adaptive re-scheduling only when it cannot achieve acceptable real-time performance through self-adaptation. Reflective alternation among scheduling heuristics offers a way to tune real-time performance internally, and we offer foundational support for this approach. In particular, run-time observable information such as that provided by our metrics-feedback framework makes it possible to detect that a given current scheduling heuristic is underperforming the level of service another could provide. Furthermore we present empirical results for our framework in a realistic avionics mission computing environment. This forms the basis for guided adaption. This dissertation makes five contributions in support of flexible and adaptive scheduling and dispatching in middleware. First, we provide a middle scheduling framework that supports arbitrary and fine-grained composition of static/dynamic scheduling, to assure critical timeliness constraints while improving noncritical performance under a range of conditions. Second, we provide a flexible dispatching infrastructure framework composed of fine-grained primitives, and describe how appropriate configurations can be generated automatically based on the output of the scheduling framework. Third, we describe algorithms to reduce the overhead and duration of adaptive rescheduling, based on sorting for rate selection and priority assignment. Fourth, we provide timely and efficient performance information through an optimized metrics-feedback framework, to support higher-level reflection and adaptation decisions. Fifth, we present the results of empirical studies to quantify and evaluate the performance of alternative canonical scheduling heuristics, across a range of load and load jitter conditions. These studies were conducted within an avionics mission computing applications framework running on realistic middleware and embedded hardware. The results obtained from these studies (1) demonstrate the potential benefits of reflective alternation among distinct scheduling heuristics at run-time, and (2) suggest performance factors of interest for future work on adaptive control policies and mechanisms using this framework

Scheduling policies and system software architectures for mixed-criticality computing

Mixed-criticality model of computation is being increasingly adopted in timing-sensitive systems. The model not only ensures that the most critical tasks in a system never fails, but also aims for better systems resource utilization in normal condition. In this report, we describe the widely used mixed-criticality task model and fixed-priority scheduling algorithms for the model in uniprocessors. Because of the necessity by the mixed-criticality task model and scheduling policies, isolation, both temporal and spatial, among tasks is one of the main requirements from the system design point of view. Different virtualization techniques have been used to design system software architecture with the goal of isolation. We discuss such a few system software architectures which are being and can be used for mixed-criticality model of computation

On the effectiveness of cache partitioning in hard real-time systems

In hard real-time systems, cache partitioning is often suggested as a means of increasing the predictability of caches in pre-emptively scheduled systems: when a task is assigned its own cache partition, inter-task cache eviction is avoided, and timing verification is reduced to the standard worst-case execution time analysis used in non-pre-emptive systems. The downside of cache partitioning is the potential increase in execution times. In this paper, we evaluate cache partitioning for hard real-time systems in terms of overall schedulability. To this end, we examine the sensitivity of (i) task execution times and (ii) pre-emption costs to the size of the cache partition allocated and present a cache partitioning algorithm that is optimal with respect to taskset schedulability. We also devise an alternative algorithm which primarily optimises schedulability but also minimises processor utilization. We evaluate the performance of cache partitioning compared to state-of-the-art pre-emption cost analysis based on benchmark code and on a large number of synthetic tasksets with both fixed priority and EDF scheduling. This allows us to derive general conclusions about the usability of cache partitioning and identify taskset and system parameters that influence the relative effectiveness of cache partitioning. We also examine the improvement in processor utilization obtained using an alternative cache partitioning algorithm, and the tradeoff in terms of increased analysis time

Generalized Extraction of Real-Time Parameters for Homogeneous Synchronous Dataflow Graphs

23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2015). 4 to 6, Mar, 2015. Turku, Finland.Many embedded multi-core systems incorporate both dataflow applications with timing constraints and traditional real-time applications. Applying real-time scheduling techniques on such systems provides real-time guarantees that all running applications will execute safely without violating their deadlines. However, to apply traditional realtime scheduling techniques on such mixed systems, a unified model to represent both types of applications running on the system is required. Several earlier works have addressed this problem and solutions have been proposed that address acyclic graphs, implicit-deadline models or are able to extract timing parameters considering specific scheduling algorithms. In this paper, we present an algorithm for extracting real-time parameters (offsets, deadlines and periods) that are independent of the schedulability analysis, other applications running in the system, and the specific platform. The proposed algorithm: 1) enables applying traditional real-time schedulers and analysis techniques on cyclic or acyclic Homogeneous Synchronous Dataflow (HSDF) applications with periodic sources, 2) captures overlapping iterations, which is a main characteristic of the execution of dataflow applications, 3) provides a method to assign offsets and individual deadlines for HSDF actors, and 4) is compatible with widely used deadline assignment techniques, such as NORM and PURE. The paper proves the correctness of the proposed algorithm through formal proofs and examples

Adaptive Mid-term and Short-term Scheduling of Mixed-criticality Systems

A mixed-criticality real-time system is a real-time system having multiple tasks classified according to their criticality. Research on mixed-criticality systems started to provide an effective and cost efficient a priori verification process for safety critical systems. The higher the criticality of a task within a system and the more the system should guarantee the required level of service for it. However, such model poses new challenges with respect to scheduling and fault tolerance within real-time systems. Currently, mixed-criticality scheduling protocols severely degrade lower criticality tasks in case of resource shortage to provide the required level of service for the most critical ones. The actual research challenge in this field is to devise robust scheduling protocols to minimise the impact on less critical tasks. This dissertation introduces two approaches, one short-term and the other medium-term, to appropriately allocate computing resources to tasks within mixed-criticality systems both on uniprocessor and multiprocessor systems. The short-term strategy consists of a protocol named Lazy Bailout Protocol (LBP) to schedule mixed-criticality task sets on single core architectures. Scheduling decisions are made about tasks that are active in the ready queue and that have to be dispatched to the CPU. LBP minimises the service degradation for lower criticality tasks by providing to them a background execution during the system idle time. After, I refined LBP with variants that aim to further increase the service level provided for lower criticality tasks. However, this is achieved at an increased cost of either system offline analysis or complexity at runtime. The second approach, named Adaptive Tolerance-based Mixed-criticality Protocol (ATMP), decides at runtime which task has to be allocated to the active cores according to the available resources. ATMP permits to optimise the overall system utility by tuning the system workload in case of shortage of computing capacity at runtime. Unlike the majority of current mixed-criticality approaches, ATMP allows to smoothly degrade also higher criticality tasks to keep allocated lower criticality ones

On-line schedulability tests for adaptive reservations in fixed priority scheduling

Adaptive reservation is a real-time scheduling technique in which each application is associated a fraction of the computational resource (a reservation) that can be dynamically adapted to the varying requirements of the application by using appropriate feedback control algorithms. An adaptive reservation is typically implemented by using an aperiodic server (e.g. sporadic server) algorithm with fixed period and variable budget. When the feedback law demands an increase of the reservation budget, the system must run a schedulability test to check if there is enough spare bandwidth to accommodate such increase. The schedulability test must be very fast, as it may be performed at each budget update, i.e. potentially at each instance of a task; yet, it must be as efficient as possible, to maximize resource usage. In this paper, we tackle the problem of performing an efficient on-line schedulability test for adaptive resource reservations in fixed priority schedulers. In the literature, a number of algorithms have been proposed for on-line admission control in fixed priority systems. We describe four of these tests, with increasing complexity and performance. In addition, we propose a novel on-line test, called Spare-Pot al- gorithm, which has been specifically designed for the problem at hand, and which shows a good cost/performance ratio compared to the other tests