A compile-time granularity analysis algorithm and its performance evaluation

A major implementation problem with implicitly parallel languages, is that small grain size can lead to execution overheads and reduced performance. We present a new granularity-analysis scheme that produces estimators, at compile time, of the relative execution weight of each procedure invocation. These estimators can be cheaply evaluated at runtime to approximate the relative task granularities, enabling intelligent scheduling decisions. Our method seeks to balance tradeoffs between analysis complexity, estimator accuracy, and runtime overhead of evaluating the estimator. To this end, rather than analyze data size or dependencies, we introduceiteration parameters to handle recursive procedures. This simplification facilitates solving the recurrence equations that describe the granularity estimators, and reduces the runtime overhead of evaluating these estimators. The algorithm is described in the context of concurrent logic programming languages, although the concepts are applicable to functional languages in general. We show, for a benchmark suite, that the method accurately estimates cost. Multiprocessor simulation results quantify the advantage of dynamically scheduling tasks with the granularity information.     

The EDL Server for Scheduling Periodic and Soft Aperiodic Tasks with Resource Constraints

In this paper, we are concerned with the problem of serving soft aperiodic tasks on a uniprocessor system where periodic tasks are scheduled on a dynamic-priority, preemptive basis and exclusively access to critical sections. Scheduling of tasks is handled by the Dynamic Priority Ceiling Protocol working with an Earliest Deadline scheduler. Our analysis determines the maximum processing time which may be stolen from periodic tasks without jeopardizing both their timing constraints and resource consistency. It provides the basis for an on-line scheduling algorithm, the EDL Server, to deal with the minimization of response times for soft aperiodic tasks.     

Dynamic scheduling of real-time tasks under precedence constraints

While scheduling theory has been developed over a long period of time, it is important to note that most results concern problems with static characteristics. However, a real-time system is dynamic and requires on-line and adaptive scheduling strategies. So, an important aspect of real-time systems research is to devise methods flexible enough to react to a dynamic change of processor load and to attempt to schedule all the tasks judiciously.In this paper, we are particularly concerned with the problem of scheduling tasks which are of two kinds: periodic and sporadic, on a monoprocessor machine. Periodic tasks are independent, run cyclically and their characteristics are known in advance. In addition, we allow for the unpredictable occurrence of aperiodic task groups, with timing and precedence constraints. Clearly, the main problem is to devise a schedulability test that makes it possible to decide whether a new occurring group can be accepted, without upsetting the tight timing behavior requirements.We present an optimal acceptance test which returns a decision on the basis of the current state of processor load and by considering tasks to be scheduled according to the well known preemptive algorithm Earliest Deadline. The acceptance algorithm and a complexity analysis are presented.     

Scheduling aperiodic tasks in dynamic priority systems

In this paper we present five new on-line algorithms for servicing soft aperiodic requests in realtime systems, where a set of hard periodic tasks is scheduled using the Earliest Deadline First (EDF) algorithm. All the proposed solutions can achieve full processor utilization and enhance aperiodic responsiveness, still guaranteeing the execution of the periodic tasks. Operation of the algorithms, performance, schedulability analysis, and implementation complexity are discussed and compared with classical alternative solutions, such as background and polling service. Extensive simulations show that algorithms with contained run-time overhead present nearly optimal responsiveness.A valuable contribution of this work is to provide the real-time system designer with a wide range of practical solutions which allow to balance efficiency against implementation complexity.     

Analyzing Innermost Runtime Complexity of Term Rewriting by Dependency Pairs

We present a modular framework to analyze the innermost runtime complexity of term rewrite systems automatically. Our method is based on the dependency pair framework for termination analysis. In contrast to previous work, we developed a direct adaptation of successful termination techniques from the dependency pair framework in order to use them for complexity analysis. By extensive experimental results, we demonstrate the power of our method compared to existing techniques.     

强实时环境下调度非周期任务的时限寻优方法

文章提出了强实时环境下调度弱时限非周期任务的时限寻优方法（ＤＯＡ）,该方法在保证周期任务和偶发性任务满足时限要求的前提下,使非周期任务的响应时间达到最优。它还可根据实时应用的需要对算法的执行性能和计算复杂度进行折衷调整。仿真实验表明,ＤＯＡ与现有的动态调度算法相比,使非周期任务响应时间更短,同时它收敛快,额外开销小,计算复杂度低,实现方便,因此是强实时环境下对周期任务与非周期任务进行混合调度的一种较好的方法。     

Minimizing aperiodic response times in a firm real-time environment

In certain real-time applications, ranging from multimedia to telecommunication systems, timing constraints can be more flexible than scheduling theory usually permits. In this paper, we deal with the problem of scheduling hybrid sets of tasks, consisting of firm periodic tasks (i.e. tasks with deadlines which can occasionally skip one instance) and soft aperiodic requests, which have to be served as soon as possible to achieve good responsiveness. We propose and analyze an algorithm, based on a variant of earliest-deadline-first scheduling, which exploits skips to minimize the response time of aperiodic requests. One of the most interesting features of our algorithm is that it can easily be tuned to balance performance vs. complexity, for adapting it to different application requirements. Extensive simulation experiments show the effectiveness of the proposed approach with respect to existing methods. Schedulability bounds are also derived to perform off-line analysis     

Learning expressions and programs over monoids

We study the problem of learning an unknown function represented as an expression or a program over a known finite monoid. As in other areas of computational complexity where programs over algebras have been used, the goal is to relate the computational complexity of the learning problem with the algebraic complexity of the finite monoid. Indeed, our results indicate a close connection between both kinds of complexity. We focus on monoids which are either groups or aperiodic, and on the learning model of exact learning from queries. For a group G, we prove that expressions over G are efficiently learnable if G is nilpotent, and impossible to learn efficiently (under cryptographic assumptions) if G is nonsolvable. We present some results for restricted classes of solvable groups, and point out a connection between their efficient learnability and the existence of lower bounds on their computational power in the program model. For aperiodic monoids, our results seem to indicate that the monoid class known as DA captures exactly learnability of expressions by polynomially many Evaluation queries. When using programs instead of expressions, we show that our results for groups remain true, while the situation is quite different for aperiodic monoids.     

CoMem: collaborative memory management for real-time operation within reactive sensor/actor networks

Increasing complexity and modularity of today??s WSAN applications impose demanding challenges on the system design. This especially affects real-time operation, resource sharing and dynamic memory management. Preemptive task systems are one way to retain good reactivity within dynamic environments. Yet, since memory is often too rare for static assignment, this rapidly leads to severe compositional problems among tasks with interfering and even varying requirements. We present our novel CoMem approach for maintaining high reactivity and efficient memory usage in such systems. With respect to task priorities and the typically limited resources of sensor nodes, we facilitate compositional software design by providing independently developed tasks with runtime information for yet collaborative and self-reflective memory sharing. Thereby, we require no special hardware-support like MMUs but operate entirely software-based.     

Preference-oriented fixed-priority scheduling for periodic real-time tasks

Traditionally, real-time scheduling algorithms prioritize tasks solely based on their timing parameters and cannot effectively handle tasks that have different execution preferences. In this paper, for a set of periodic real-time tasks running on a single processor, where some tasks are preferably executed as soon as possible (ASAP) and others as late as possible (ALAP), we investigate Preference-Oriented Fixed-Priority (POFP) scheduling techniques. First, based on Audsley’s Optimal Priority Assignment (OPA), we study a Preference Priority Assignment (PPA) scheme that attempts to assign ALAP (ASAP) tasks lower (higher) priorities, whenever possible. Then, by considering the non-work-conserving strategy, we exploit the promotion times of ALAP tasks and devise an online dual-queue based POFP scheduling algorithm. Basically, with the objective of fulfilling the execution preferences of all tasks, the POFP scheduler retains ALAP tasks in the delay queue until their promotion times while putting ASAP tasks into the ready queue right after their arrivals. In addition, to further expedite (delay) the executions of ASAP (ALAP) tasks using system slack, runtime techniques based on dummy and wrapper tasks are investigated. The proposed schemes are evaluated through extensive simulations. The results show that, compared to the classical fixed-priority Rate Monotonic Scheduling (RMS) algorithm, the proposed priority assignment scheme and POFP scheduler can achieve significant improvement in terms of fulfilling the execution preferences of both ASAP and ALAP tasks, which can be further enhanced at runtime with the wrapper-task based slack management technique.     

Improved Task and Resource Partitioning Under the Resource-Oriented Partitioned Scheduling

Coordinated partitioning and resource sharing have attracted considerable research interest in the field of real-time multiprocessor systems. However, finding an optimal partition is widely known as NP-hard, even for independent tasks. A recently proposed resource-oriented partitioned (ROP) fixed-priority scheduling that partitions tasks and shared resources respectively has been shown to achieve a non-trivial speedup factor guarantee, which promotes the research of coordinated scheduling to a new level. Despite the theoretical elegance, the schedulability performance of ROP scheduling is restricted by the heuristic partitioning methods used in the original study. In this paper, we address the partitioning problem for tasks and shared resources under the ROP scheduling. A unified schedulability analysis framework for the ROP scheduling is proposed in the first place. A sophisticated partitioning approach based on integer linear programming (ILP) is then proposed based on the unified analysis. Empirical results show that the proposed methods improve the schedulability of ROP scheduling significantly, and the runtime complexity for searching a solution is reduced prominently compared with other ILP-based approaches as well.

     

Aperiodic task scheduling for Hard-Real-Time systems

A real-time system consists of both aperiodic and periodic tasks. Periodic tasks have regular arrival times and hard deadlines. Aperiodic tasks have irregular arrival times and either soft or hard deadlines. In this article, we present a new algorithm, the Sporadic Server algorithm, which greatly improves response times for soft deadline aperiodic tasks and can guarantee hard deadlines for both periodic and aperiodic tasks. The operation of the Sporadic Server algorithm, its performance, and schedulability analysis are discussed and compared with previously published aperiodic service algorithms.     

Algorithms and optimality of scheduling soft aperiodic requests in fixed-priority preemptive systems

In this paper, we investigate the problem of scheduling soft aperiodic requests in systems where periodic tasks are scheduled on a fixed-priority, preemptive basis. First, we show that given any queueing discipline for the aperiodic requests, no scheduling algorithm can minimize the response time of every aperiodic request and guarantee that the deadlines of the periodic tasks are met when the periodic tasks are scheduled on a fixed-priority, preemptive basis. We then develop two algorithms: Algorithm is locally optimal in that it minimizes the response time of the aperiodic request at the head of the aperiodic service queue. Algorithm is globally optimal in that it completes the current backlog of work in the aperiodic service queue as early as possible.     

An event-based approach for formally verifying runtime adaptive real-time systems

Real-time and embedded systems are required to adapt their behavior and structure to runtime unpredicted changes in order to maintain their feasibility and usefulness. These systems are generally more difficult to specify and verify owning to their execution complexity. Hence, ensuring the high-level design and the early verification of system adaptation at runtime is very crucial. However, existing runtime model-based approaches for adaptive real-time and embedded systems suffer from shortcoming linked to efficiently and correctly managing the adaptive system behavior, especially that a formal verification is not allowed by modeling languages such as UML and MARTE profile. Moreover, reasoning about the correctness and the precision of high-level models is a complex task without the appropriate tool support. In this work, we propose an MDE-based framework for the specification and the verification of runtime adaptive real-time and embedded systems. Our approach stands for Event-B method to formally verify resources behavior and real-time constraints. In fact, thanks to MDE M2T transformations, our proposal translates runtime models into Event-B specifications to ensure the correctness of runtime adaptive system properties, temporal constrains and nonfunctional properties using Rodin platform. A flood prediction system case study is adopted for the validation of our proposal.

     

Schedulability analysis of periodic and aperiodic tasks with resource constraints

In this paper, we address the problem of scheduling hybrid task sets consisting of hard periodic and soft aperiodic tasks that may share resources in exclusive mode in a dynamic environment, where tasks are scheduled based on their deadlines. Bounded blocking on exclusive resources is achieved by means of a dynamic resource access protocol which also prevents deadlocks and chained blocking. Aperiodic responsiveness is enhanced by an efficient servicing technique which assigns each aperiodic request a suitable deadline. Feasibility conditions are extended to handle tasks with deadlines different from periods and a reclaiming technique is presented to deal with early completions.     

Response modeling runtime schedulers for timing analysis of self-timed dataflow graphs

Constituent tasks of modern day Embedded Streaming Applications (ESAs), such as engine control systems, multimedia and software defined radios often exhibit execution behaviors that do not conform to conventional task models. ESAs consist of iterative, pipelined sequences of tasks that are conditioned by intra- and inter-iteration dependencies, and often have strict throughput and latency requirements. We model ESAs as dataflow graphs, where actors represent computational units, and directed edges represent communication channels between actors. Due to practical constraints like cost-effectiveness, power consumption and chip-area, multiple ESAs are run on a shared (multi-processor) platform. Thus rigorous timing analysis is required to verify whether individual ESAs meet their respective timing requirements.We look at response modeling, a compositional timing analysis approach wherein the local worst-case influence of runtime scheduling is represented within the constructs provided in dataflow. These local representations (called response models) can be composed together to construct a global understanding of an ESA’s worst-case execution which is then used to verify whether its real-time requirements are met. This paper proposes a generic response modeling technique for runtime scheduling of ESAs. We focus on preemptive Fixed Priority Scheduling (FPS) but also demonstrate that we can apply our technique to a wide range of runtime schedulers. In our experiments, we present academic and industrial case-studies that highlight the effectiveness of our approach in the timing analysis of ESAs with unconventional execution behavior.     

Automated refactoring to the Null Object design pattern

ContextNull-checking conditionals are a straightforward solution against null dereferences. However, their frequent repetition is considered a sign of poor program design, since they introduce source code duplication and complexity that impacts code comprehension and maintenance. The Null Object design pattern enables the replacement of null-checking conditionals with polymorphic method invocations that are bound, at runtime, to either a real object or a Null Object.ObjectiveThis work proposes a novel method for automated refactoring to Null Object that eliminates null-checking conditionals associated with optional class fields, i.e., fields that are not initialized in all class instantiations and, thus, their usage needs to be guarded in order to avoid null dereferences.MethodWe introduce an algorithm for automated discovery of refactoring opportunities to Null Object. Moreover, we specify the source code transformation procedure and an extensive set of refactoring preconditions for safely refactoring an optional field and its associated null-checking conditionals to the Null Object design pattern. The method is implemented as an Eclipse plug-in and is evaluated on a set of open source Java projects.ResultsSeveral refactoring candidates are discovered in the projects used in the evaluation and their refactoring lead to improvement of the cyclomatic complexity of the affected classes. The successful execution of the projects’ test suites, on their refactored versions, provides empirical evidence on the soundness of the proposed source code transformation. Runtime performance results highlight the potential for applying our method to a wide range of project sizes.ConclusionOur method automates the elimination of null-checking conditionals through refactoring to the Null Object design pattern. It contributes to improvement of the cyclomatic complexity of classes with optional fields. The runtime processing overhead of applying our method is limited and allows its integration to the programmer’s routine code analysis activities.     

Science in the Cloud: Allocation and Execution of Data-Intensive Scientific Workflows

An important challenge for the adoption of cloud computing in the scientific community remains the efficient allocation and execution of data-intensive scientific workflows to reduce execution time and the size of transferred data. The transferred data overhead is becoming significant with emerging scientific workflows that have input/output files and intermediate data products ranging in the hundreds of gigabytes. The allocation of scientific workflows on public clouds can be described through a variety of perspectives and parameters, and has been proved to be NP-complete. This paper proposes an evolutionary approach for task allocation on public clouds considering data transfer and execution time. In our framework, a solution is represented using an allocation chromosome that encodes the allocation of tasks to nodes, and an ordering chromosome that defines the execution order according to the scientific workflow representation. We propose a multi-objective optimization that relies on a cloud cost model and employs tailored evolution operators. Starting from a population of possible solutions, we employ crossover and mutation operators on both chromosomes aiming at optimizing the data transferred between nodes as well as the total workflow runtime. The crossover operators combine parts of solutions to reduce data overhead, whereas the mutation operators swamp between parts of the same chromosome according to pre-defined rules. Our experimental study compares between the proposed approach and current state-of-the art approaches using synthetic and real-life workflows. Our algorithm performs similarly to existing heuristics for small workflows and shows up to 80 % improvements for larger synthetic workflows. To further validate our approach we compare between the allocation and scheduling obtained by our approach with that obtained by popular scientific workflow managers, when real workflows with hundreds of tasks are executed on a public cloud. The results show a 10 % improvement in runtime over existing schedulers, caused by a 80 % reduction in transferred data and optimized allocation and ordering of tasks. This improved data locality has greater impact as it can be employed to improve and study data provenance and facilitate data persistence for scientific workflows.     

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 algorithm, which has been specifically designed for the problem at hand, and which shows a good cost/performance ratio compared to the other tests.