Least space-time first scheduling algorithm : scheduling complex tasks with hard deadline on parallel machines

Both time constraints and logical correctness are essential to real-time systems and failure to specify and observe a time constraint may result in disaster. Two orthogonal issues arise in the design and analysis of real-time systems: one is the specification of the system, and the semantic model describing the properties of real-time programs; the other is the scheduling and allocation of resources that may be shared by real-time program modules. The problem of scheduling tasks with precedence and timing constraints onto a set of processors in a way that minimizes maximum tardiness is here considered. A new scheduling heuristic, Least Space Time First (LSTF), is proposed for this NP-Complete problem. Basic properties of LSTF are explored; for example, it is shown that (1) LSTF dominates Earliest-Deadline-First (EDF) for scheduling a set of tasks on a single processor (i.e., if a set of tasks are schedulable under EDF, they are also schedulable under LSTF); and (2) LSTF is more effective than EDF for scheduling a set of independent simple tasks on multiple processors. Within an idealized framework, theoretical bounds on maximum tardiness for scheduling algorithms in general, and tighter bounds for LSTF in particular, are proven for worst case behavior. Furthermore, simulation benchmarks are developed, comparing the performance of LSTF with other scheduling disciplines for average case behavior. Several techniques are introduced to integrate overhead (for example, scheduler and context switch) and more realistic assumptions (such as inter-processor communication cost) in various execution models. A workload generator and symbolic simulator have been implemented for comparing the performance of LSTF (and a variant -- LSTF+) with that of several standard scheduling algorithms. LSTF\u27s execution model, basic theories, and overhead considerations have been defined and developed. Based upon the evidence, it is proposed that LSTF is a good and practical scheduling algorithm for building predictable, analyzable, and reliable complex real-time systems. There remain some open issues to be explored, such as relaxing some current restrictions, discovering more properties and theorems of LSTF under different models, etc. We strongly believe that LSTF can be a practical scheduling algorithm in the near future

Handling Overload Conditions in Real-Time Systems

This chapter deals with the problem of handling overload conditions, that is, those critical situations in which the computational demand requested by the application exceeds the processor capacity. If not properly handled, an overload can cause an abrupt performance degradation, or even a system crash. Therefore, a real-time system should be designed to anticipate and tolerate unexpected overload situations through specific kernel mechanisms

Improving Responsiveness of Time-Sensitive Applications by Exploiting Dynamic Task Dependencies

In this paper, a mechanism is presented for reducing priority inversion in multi-programmed computing systems. Contrarily to well-known approaches from the literature, this paper tackles cases where the dependency relationships among tasks cannot be known in advance to the operating system (OS). The presented mechanism allows tasks to explicitly declare said relationships, enabling the OS scheduler to take advantage of such information and trigger priority inheritance, resulting in reduced priority inversion. We present the prototype implementation of the concept within the Linux kernel, in the form of modifications to the standard POSIX condition variables code, along with an extensive evaluation including a quantitative assessment of the benefits for applications making use of the technique, as well as comprehensive overhead measurements. Also, we present an associated technique for theoretical schedulability analysis of a system using the new mechanism, which is useful to determine whether all tasks can meet their deadlines or not, in the specific scenario of tasks interacting only through remote procedure calls, and under partitioned scheduling

Safe code transfromations for speculative execution in real-time systems

Although compiler optimization techniques are standard and successful in non-real-time systems, if naively applied, they can destroy safety guarantees and deadlines in hard real-time systems. For this reason, real-time systems developers have tended to avoid automatic compiler optimization of their code. However, real-time applications in several areas have been growing substantially in size and complexity in recent years. This size and complexity makes it impossible for real-time programmers to write optimal code, and consequently indicates a need for compiler optimization. Recently researchers have developed or modified analyses and transformations to improve performance without degrading worst-case execution times. Moreover, these optimization techniques can sometimes transform programs which may not meet constraints/deadlines, or which result in timeouts, into deadline-satisfying programs. One such technique, speculative execution, also used for example in parallel computing and databases, can enhance performance by executing parts of the code whose execution may or may not be needed. In some cases, rollback is necessary if the computation turns out to be invalid. However, speculative execution must be applied carefully to real-time systems so that the worst-case execution path is not extended. Deterministic worst-case execution for satisfying hard real-time constraints, and speculative execution with rollback for improving average-case throughput, appear to lie on opposite ends of a spectrum of performance requirements and strategies. Deterministic worst-case execution for satisfying hard real-time constraints, and speculative execution with rollback for improving average-case throughput, appear to lie on opposite ends of a spectrum of performance requirements and strategies. Nonetheless, this thesis shows that there are situations in which speculative execution can improve the performance of a hard real-time system, either by enhancing average performance while not affecting the worst-case, or by actually decreasing the worst-case execution time. The thesis proposes a set of compiler transformation rules to identify opportunities for speculative execution and to transform the code. Proofs for semantic correctness and timeliness preservation are provided to verify safety of applying transformation rules to real-time systems. Moreover, an extensive experiment using simulation of randomly generated real-time programs have been conducted to evaluate applicability and profitability of speculative execution. The simulation results indicate that speculative execution improves average execution time and program timeliness. Finally, a prototype implementation is described in which these transformations can be evaluated for realistic applications

Schedulability Analysis of periodic task of uniprocessor system on Real Time System

A real time system is a system that must satisfy explicit bounded response-time constraints, otherwise risk severe consequences including failure. Failure happens when a system cannot satisfy one or more of the requirements laid out in the formal system specification. The problem of real-time scheduling spans a broad spectrum of algorithms from simple uniprocessor to highly sophisticated multiprocessor scheduling algorithms. In this project, we will study the characteristics and constraints of real-time tasks which should be scheduled to be executed. Analysis methods and the concept of optimality criteria, which leads to design appropriate scheduling algorithms, will also be addressed. Then, we study real-time scheduling algorithms for uniprocessor systems, which can be divided into two major classes: off-line and on-line. On-line algorithms are partitioned into either static or dynamic-priority based algorithms. We will observe both preemptive and non-preemptive static-priority based algorithms. For dynamic-priority based algorithms, we study the two subsets; namely, planning based and best effort scheduling algorithms. This project compares RM against EDF under several aspects, using existing theoretical results, specific simulation experiments, or simple counter examples to show that many common beliefs are either false or only restricted to specific situations

Bundle: Taming The Cache And Improving Schedulability Of Multi-Threaded Hard Real-Time Systems

For hard real-time systems, schedulability of a task set is paramount. If a task set is not deemed schedulable under all conditions, the system may fail during operation and cannot be deployed in a high risk environment. Schedulability testing has typically been separated from worst-case execution time (WCET) analysis. Each task’s WCET value is calculated independently and provided as input to a schedulability test. However, a task’s WCET value is influenced by scheduling decisions and the impact of cache memory. Thus, schedulability tests have been augmented to include cache-related preemption delay (CRPD). From this classical perspective, the effect of cache memory on WCET and schedulability is always negative; increasing execution times and demand. In this work we propose a new positive perspective, where cache memory benefits multi-threaded tasks by scheduling threads in a manner that shares values predictably. This positive perspective is reached by integrating, rather than separating the disciplines of schedulability analysis and worst-case execution time. These integrated techniques are referred to as the BUNDLE family of worst-case execution time and cache overhead (WCETO) analysis and scheduling algorithm. WCETO calculation divides the task’s structure into conflict free regions and calculates a bound utilizing explicit understanding of the thread-level scheduling algorithm. Conflict free regions are utilized by the scheduling algorithm, which associates with each region a thread container called a bundle. At any time only one bundle may be active, and only threads of the active bundle may execute on the processor. The BUNDLE family of scheduling algorithms developed in this work increase in scope from BUNDLE through ITCB-DAG. As the fundamental contribution, BUNDLE and BUNDLEP apply to a single multi-threaded task running on a uniprocessor architecture with a single level direct mapped instruction cache. NPM-BUNDLE expands the positive perspective to multiple tasks on a uniprocessor system. With ITCB-DAG bringing BUNDLE’s analysis and scheduling techniques to multi-processor systems. Each of the scheduling algorithms require a novel hardware mechanism to anticipate execution and make scheduling decisions. To support anticipation of execution, a novel XFLICT interrupt is proposed. It is a simple mechanism that emulates the behavior of hardware breakpoints. An implementation of the BUNDLEP analytical techniques, scheduling algorithm, and XFLICT interrupt is available as a simulated platform for further research and extension. Future work is planned to expand BUNDLE’s positive perspective and increase adoption. The most significant barrier to adoption is the ability to deploy BUNDLE’s scheduling algorithm, this mandates a viable and available hardware or software mechanism to anticipate execution. NPM-BUNDLE is limited to non-preemptive multi-task scheduling and analysis, support for preemptive scheduling will increase the positive impact of BUNDLE’s integrated perspective

Effective And Efficient Preemption Placement For Cache Overhead Minimization In Hard Real-Time Systems

Schedulability analysis for real-time systems has been the subject of prominent research over the past several decades. One of the key foundations of schedulability analysis is an accurate worst case execution time (WCET) for each task. In preemption based real-time systems, the CRPD can represent a significant component (up to 44% as documented in research literature) of variability to overall task WCET. Several methods have been employed to calculate CRPD with significant levels of pessimism that may result in a task set erroneously declared as non-schedulable. Furthermore, they do not take into account that CRPD cost is inherently a function of where preemptions actually occur. Our approach for computing CRPD via loaded cache blocks (LCBs) is more accurate in the sense that cache state reflects which cache blocks and the specific program locations where they are reloaded. Limited preemption models attempt to minimize preemption overhead (CRPD) by reducing the number of allowed preemptions and/or allowing preemption at program locations where the CRPD effect is minimized. These algorithms rely heavily on accurate CRPD measurements or estimation models in order to identify an optimal set of preemption points. Our approach improves the effectiveness of limited optimal preemption point placement algorithms by calculating the LCBs for each pair of adjacent preemptions to more accurately model task WCET and maximize schedulability as compared to existing preemption point placement approaches. We utilize dynamic programming technique to develop an optimal preemption point placement algorithm. Lastly, we will demonstrate, using a case study, improved task set schedulability and optimal preemption point placement via our new LCB characterization. We propose a new CRPD metric, called loaded cache blocks (LCB) which accurately characterizes the CRPD a real-time task may be subjected to due to the preemptive execution of higher priority tasks. We show how to integrate our new LCB metric into our newly developed algorithms that automatically place preemption points supporting linear control flow graphs (CFGs) for limited preemption scheduling applications. We extend the derivation of loaded cache blocks (LCB), that was proposed for linear control flow graphs (CFGs) to conditional CFGs. We show how to integrate our revised LCB metric into our newly developed algorithms that automatically place preemption points supporting conditional control flow graphs (CFGs) for limited preemption scheduling applications. For future work, we will verify the correctness of our framework through other measurable physical and hardware constraints. Also, we plan to complete our work on developing a generalized framework that can be seamlessly integrated into real-time schedulability analysis

Performance Management in ATM Networks

ATM is representative of the connection-oriented resource provisioning classof protocols. The ATM network is expected to provide end-to-end QoS guaranteesto connections in the form of bounds on delays, errors and/or losses. Performancemanagement involves measurement of QoS parameters, and application of controlmeasures (if required) to improve the QoS provided to connections, or to improvethe resource utilization at switches. QoS provisioning is very important for realtimeconnections in which losses are irrecoverable and delays cause interruptionsin service. QoS of connections on a node is a direct function of the queueing andscheduling on the switch. Most scheduling architectures provide static allocationof resources (scheduling priority, maximum buffer) at connection setup time. Endto-end bounds are obtainable for some schedulers, however these are precluded forheterogeneously composed networks. The resource allocation does not adapt to theQoS provided on connections in real time. In addition, mechanisms to measurethe QoS of a connection in real-time are scarce.In this thesis, a novel framework for performance management is proposed. Itprovides QoS guarantees to real time connections. It comprises of in-service QoSmonitoring mechanisms, a hierarchical scheduling algorithm based on dynamicpriorities that are adaptive to measurements, and methods to tune the schedulers atindividual nodes based on the end-to-end measurements. Also, a novel scheduler isintroduced for scheduling maximum delay sensitive traffic. The worst case analysisfor the leaky bucket constrained traffic arrivals is presented for this scheduler. Thisscheduler is also implemented on a switch and its practical aspects are analyzed.In order to understand the implementability of complex scheduling mechanisms,a comprehensive survey of the state-of-the-art technology used in the industry isperformed. The thesis also introduces a method of measuring the one-way delayand jitter in a connection using in-service monitoring by special cells

Smart real time operating system

Ph.DDOCTOR OF PHILOSOPH

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