Uniprocessor Systems Research Articles

A Novel Feasibility Test for Energy Minimization of Real-Time Mixed Task Sets for DVS-Enabled Uniprocessor System

With advancements in computing and communication technologies on mobile devices, the performance requirements of embedded processors have significantly increased, resulting in a corresponding increase in its energy consumption. Dynamic scaling of operating voltage and operating frequency has a strong correlation to energy minimization in CMOS real-time circuits. Simultaneous optimization of ([Formula: see text], [Formula: see text] pairs under dynamic activity levels is thus extensively investigated over several years. The supply voltage is tuned dynamically during runtime (DVS), with a fixed threshold voltage, to achieve energy minimization. This work addresses the issue of maximizing the energy efficiency of real-time periodic, aperiodic and mixed task sets, in a uniprocessor system, by developing a novel task feasibility methodology, with a novel processor performance-based constraint, to generate the optimal operating supply voltage to the individual task of task sets. The energy minimization of real-time mixed task sets is formulated as Geometric Programming (GP) problem, by varying frequency for periodic tasks sets and keeping fixed frequency for aperiodic tasks set, over a range of task sets and hence computing optimal operating voltages. Simulation experiments show energy savings on the cumulative basis of 50%, 38% and 29% for periodic, aperiodic and mixed task sets, respectively, based on the processing timing constraints of task sets.

Artificial intelligence based Parameter Optimization Technique for selection of Optimal Real-Time Scheduling Algorithm

In Modern days, real time system plays an important role in our modern and digital society. The success of any real time application is totally depending upon the selection of optimal scheduling algorithm. In real time application every task should have the nature of deadlines and time when they arrived, on the basis of these parameters we observe the response time of different scheduling algorithm then we select the optimal algorithm for a particular application. So in this paper our aim is to reduce the complexity of real time system researcher for selection of scheduling algorithm for a particular application. This artificial intelligence based approach is an extent the state of any real time system in the area of scheduling. This approach works in any uni-processor system. Introduction

Dynamic power management under the RUN scheduling algorithm: a slack filling approach

Effective energy-aware scheduling is paramount for current and future multiprocessor real-time systems, being Dynamic Power Management (DPM) one of the employed techniques. In this paper we extend the Reduction to Uniprocessor (RUN) algorithm making it DPM-compliant. RUN is an optimal multiprocessor real-time scheduling for periodic implicit-deadline tasks and it is known to generate low overhead in terms of preemptions and migrations. It is based on an off-line reduction of the target multiprocessor system into one or more uniprocessor systems. On-line scheduling decisions for the latter is then efficiently translated back to the original system. The developed approach in this paper, called Dynamic Slack Filling (DSF-RUN), extends RUN in two aspects. First, the RUN reduction process is adapted to take into account slack entities, properly defined to represent processor spare capacity for DPM management. Second, on-line scheduling rules of RUN are modified so as to generate long time periods in the schedule by dynamically inserting slacks. Our approach has the same off-line complexity of the original RUN algorithm and takes either quadratic or linear on-line complexity in the number of tasks, depending on the strategy used to compute the maximum allowed idle periods. Results obtained with simulations show that our approach consumes less than 10% more energy than an idealized optimum DPM strategy.

A novel task scheduling approach for dependent non‐preemptive tasks using fuzzy logic

Multiprocessor task scheduling problem is a pressing problem that affects systems' performance and is still being investigated by the researchers. Finding the optimal schedules is considered to be a computationally hard problem. Recently, researchers have used fuzzy logic in the field of task scheduling to achieve optimal performance, but this area of research is still not well investigated. In addition, there are various scheduling algorithms that used fuzzy logic but most of them are often performed on uniprocessor systems. This article presents a new proposed algorithm in which the priorities of the tasks are derived from the fuzzy logic and bottom level parameter. This approach is designed to find task schedules with optimal or sub-optimal lengths in order to achieve high performance for a multiprocessor environment. With respect to the proposed algorithm, the precedence constraints between the non-preemptive tasks and their execution times are known and described by a directed acyclic graph. The number of processors is fixed, the communication costs are negligible and the processors are homogeneous. The suggested technique is tested and compared with the Prototype Standard Task Graph Set.

Temporal consistency maintenance on multiprocessor platforms with instance skipping

Maintaining temporal consistency of real-time data is important for cyber-physical systems. Most of the previous studies focus on uniprocessor systems. In this paper, the problem of temporal consistency maintenance on multiprocessor platforms with instance skipping was formulated based on the (m,k)-constrained model. A partitioned scheduling method SC-AD was proposed to solve the problem. SC-AD uses a derived sufficient schedulability condition to calculate the initial value of m for each sensor transaction. It then partitions the transactions among the processors in a balanced way. To further reduce the average relative invalid time of real-time data, SC-AD judiciously increases the values of m for transactions assigned to each processor. Experiment results show that SC-AD outperforms the baseline methods in terms of the average relative invalid time and the average valid ratio under different system workloads.

On Parametrizing Feasible Reconfigurable Systems Under Real-Time, Energy, and Resource Sharing Constraints

This article deals with reconfigurable uniprocessor systems powered by a renewable energy source under real-time and resource sharing constraints. A reconfigurable system is defined as a set of implementations, each of which is encoded by real-time periodic software tasks. Reconfiguration is a flexible runtime scenario that adapts the current system’s implementation to any related environment evolution under well-defined conditions. A task is characterized by an effective calculated deadline that should be less than a maximum deadline defined in user requirements. The main problem is how to calculate the effective deadlines of the different periodic tasks in the different implementations under possibly the predicted renewable energy source and the sharing of resource constraints. We propose an offline method based on three solutions to calculate the deadlines of tasks. The first serves to compute the deadlines ensuring the real-time system feasibility and also minimizes the number of context switches by assigning the highest priority to the task with the smallest maximum deadline. The second computes the deadlines ensuring the respect of energy constraints, and the third computes the deadlines ensuring the respect of resource sharing constraints. These three solutions calculate the possible deadlines of each task in the hyperperiod of the corresponding implementations. We develop a new simulator called DEAD-CALC, that integrates a new tool called RANDOM-TASK for applying and evaluating the proposed solutions. The conducted experimentation proves that this methodology provides deadlines with affecting neither the load nor the processor speed while reducing the calculation time. Note to Practitioners —Real-time software is particularly difficult to design since, in addition to ever more complex functional constraints, it has to satisfy a set of stringent nonfunctional requirements, such as deadlines. In fact, the violation of deadlines can lead to breakdowns, data loss, and so on, which can be catastrophic for critical applications. This article presents the DEAD-CALC project for computing efficiently the deadlines of reconfigurable real-time devices to run possibly under energy and resource sharing constraints in all the system implementations. A device, in the software level, is a superset of implementations, each of which (i.e., a set of periodic software tasks) is activated and executed at a particular time according to user requirements. DEAD-CALC reduces the development time by computing the deadlines to be certainly respected without any additional feasibility analysis of the device. The calculation can only consider the real-time aspects, or both the harvesting energy and resource sharing constraints, giving the user the ability to configure the system under different conditions. DEAD-CALC is a visual environment that can be simply used by designers to compute and display these deadlines, with a few clicks, in arranged tables, and in a short time. This project can be a future reference for industrial partners who will be focusing on various real-time applications design, such as the intelligent manufacturing industry, intelligent transportation systems, embedded systems, wireless sensor networks, smart grids, medical control devices, and military platforms.

Precise Worst-Case Blocking Time of Tasks Under Priority Inheritance Protocol

The problem of precisely computing the worst-case blocking time that tasks may experience is one of the fundamental issues of schedulability analysis of real-time applications. While exact methods have been proposed for more sophisticated protocols, the problem is indeed complex in case of the Priority Inheritance Protocol, even restricting the attention to uniprocessor systems, non-nested resource accesses, and non-self-suspending tasks. Besides a very simple method leading in general to loose upper bounds, only one algorithm of exponential complexity has been so far reported in literature to tighten such bounds. In this article, we describe a novel approach which, leveraging an operational research technique for modeling the problem, computes the same tight bounds in polynomial time. We then discuss the scenarios in which, assuming no conditional statements in the tasks’ code, the computed bounds derive from an actually impossible blocking chain, and we refine the initial model to more precisely compute the worst-case blocking times for any task set in any possible operating condition.

Suspension-Aware Earliest-Deadline-First Scheduling Analysis

While the earliest-deadline-first (EDF) scheduling algorithm has extensively been utilized in real-time systems, there is almost no literature considering EDF for task sets with dynamic self-suspension behavior. To be precise, there is no specialized result for uniprocessor systems, besides the trivial suspension-oblivious approach. The work by Liu and Anderson (in ECRTS 2013) and Dong and Liu (in RTSS 2016) for suspension-aware multiprocessor global EDF can also be applied to uniprocessor systems and therefore be considered the state-of-the-art. In this work, two novel schedulability analyses (one for sporadic and one for periodic task sets) for suspension-aware EDF on uniprocessor systems are proposed, which outperform the state-of-the-art on such systems in empirical and theoretical comparison. We further show that the analysis by Dong and Liu is in fact not suspension-aware for uniprocessor systems.

Multiprocessor Scheduling of Sensor Transactions for Real-Time Data Quality Maintenance

In cyber-physical systems, sensor transactions should be effectively scheduled to maintain the temporal validity of real-time data objects. Previous studies on sensor transaction scheduling mainly focus on uniprocessor systems. In this paper, we study the problem of data quality-based scheduling of sensor transactions on multiprocessor platforms. The data quality is defined to describe the validity degree of real-time data objects. Two methods, named the Partitioned Scheduling for Quality Maximization (P-QM) and the improved P-QM scheduling (IP-QM), are proposed. P-QM maximizes the data quality by judiciously determining the preallocated computation time of each sensor transaction and assigns the transactions to different processors. IP-QM improves the data quality obtained from P-QM by adaptively executing transaction instances on each processor based on the current status of the system. It is demonstrated through experiments that IP-QM can provide higher data quality than P-QM under different system workloads.

An Evolutionary Approach for the Hierarchical Scheduling of Safety- and Security-Critical Multicore Architectures

The aerospace and defense industry is facing an end-of-life production issue with legacy embedded uniprocessor systems. Most, if not all, embedded processor manufacturers have already moved towards system-on-a-chip multicore architectures. Current scheduling arrangements do not consider schedules related to safety and security. The methods are also inefficient because they arbitrarily assign larger-than-necessary windows of execution. This research creates a hierarchical scheduling framework as a model for real-time multicore systems to integrate the scheduling for safe and secure systems. This provides a more efficient approach which automates the migration of embedded systems’ real-time software tasks to multicore architectures. A novel genetic algorithm with a unique objective function and encoding scheme was created and compared to classical bin-packing algorithms. The simulation results show the genetic algorithm had 1.8–2.5 times less error (a 56–71% difference), outperforming its counterparts in uniformity in utilization. This research provides an efficient, automated method for commercial, private and defense industries to use a genetic algorithm to create a feasible two-level hierarchical schedule for real-time embedded multicore systems that address safety and security constraints.

Generalized and Scalable Offset-Based Response Time Analysis of Fixed Priority Systems

Abstract Offset-based response time analysis techniques obtain tight worst-case response time (WCRT) bounds by accounting release time dependencies between tasks. The maximum response time variation of a task or a message (i.e., the difference between the worst-case and best-case response time) is used to compute the end-to-end delays of distributed systems (Palencia and Harbour, 1998), (Tindell and Clark, 1994). Hence, the WCRT evaluation plays an important role in determining tight end-to-end delays of distributed systems. In real-time theory, there exist two approaches to compute the WCRT of a task: the classical response time analysis (RTA) approach and the modular performance analysis with the Real-Time Calculus (MPA-RTC). MPA-RTC has its roots in Network Calculus (NC). MPA-RTC offers more powerful abstraction than RTA based techniques and allows composition in terms of tasks, event streams, and resource sharing which makes it a strong candidate to analyze distributed systems (Wandeler, 2006), (Perathoner, 2011). However, one of the key limitations of the MPA-RTC is its inability to handle offset dependencies between tasks, whereas the classical RTA techniques can handle them. In this paper, we propose a method to consider offset dependencies between tasks using an MPA-RTC framework for a fixed priority scheduler in a uniprocessor system. Hence, our approach leverages the advantages of the expressive MPA-RTC framework model. We propose novel heuristics and approximation to reduce the inherent complexity of an offset-based RTA. We quantitatively evaluate the effectiveness of our approach to the state-of-the-art classical offset-based RTA techniques.

Priority Assignment on Partitioned Multiprocessor Systems With Shared Resources

Driven by industry demand, there is an increasing need to develop real-time multiprocessor systems which contain shared resources. The Multiprocessor Stack Resource Policy (MSRP) and Multiprocessor resource sharing Protocol (MrsP) are two major protocols that manage access to shared resources. Both of them can be applied to Fixed-Priority Preemptive Scheduling (FPPS), which is enforced by most commercial real-time systems regulations, and which requires task priorities to be assigned before deployment. Along with MSRP and MrsP, there exist two forms of schedulability tests that bound the worst-case blocking time due to resource accesses: the traditional ones being more widely adopted and the more recently developed holistic ones which deliver tighter analysis. On uniprocessor systems, there are several well-established optimal priority assignment algorithms. Unfortunately, on multiprocessor systems with shared resources, the issue of priority assignment has not been adequately understood. In this article, we investigate three mainstream priority assignment algorithms–Deadline Monotonic Priority Ordering (DMPO), Audsley’s Optimal Priority Assignment (OPA), and Robust Priority Assignment (RPA), in the context of partitioned multiprocessor systems with shared resources. Our contributions are multifold: First, we prove that DMPO is optimal with the traditional schedulability tests. Second, two counter examples are given as evidence that DMPO is not optimal with the tighter holistic schedulability tests. Third, we then analyze the pessimism arising from the adoption of OPA and RPA with the holistic tests. Lastly, we propose a Slack-based Priority Ordering (SPO) algorithm that minimises such pessimism, and has polynomial time complexity. Comprehensive experiments show that SPO outperforms (i.e., results in a larger number of schedulable systems) DMPO, OPA, and RPA in general with the holistic schedulability tests, by up to 15 percent. With the theoretical contributions, this paper is a useful guide to priority assignment in real-time partitioned multiprocessor systems with shared resources.

Techniques and Analysis for Mixed-criticality Scheduling with Mode-dependent Server Execution Budgets

In mixed-criticality systems, tasks of different criticality share system resources, mainly to reduce cost. Cost is further reduced by using adaptive mode-based scheduling arrangements, such as Vestal’s model, to improve resource efficiency, while guaranteeing schedulability of critical functionality. To simplify safety certification, servers are often used to provide temporal isolation between tasks. In its simplest form, a server is a periodically recurring time window, in which some tasks are scheduled. A server’s computational requirements may greatly vary in different modes, although state-of-the-art techniques and schedulability tests do not allow different budgets to be used by a server in different modes. This results in a single conservative execution budget for all modes, increasing system cost. The goal of this paper is to reduce the cost of mixed-criticality systems through three main contributions: (i) a scheduling arrangement for uniprocessor systems employing fixed-priority scheduling within periodic servers, whose budgets are dynamically adjusted at run-time in the event of a mode change, (ii) a new schedulability analysis for such systems, and (iii) heuristic algorithms for assigning budgets to servers in different modes and ordering the execution of the servers. Experiments with synthetic task sets demonstrate considerable improvements (up to 52.8%) in scheduling success ratio when using dynamic server budgets vs. static “one-size-fits-all-modes” budgets.

Resource-Oriented Partitioning for Multiprocessor Systems with Shared Resources

Predictable scheduling and resource sharing primitives are fundamental aspects of real-time systems. To prevent race conditions, access to shared resources must ensure mutual exclusion, e.g., using semaphores. Further, real-time locking protocols are required to avoid un-controlled priority inversions. For uniprocessor systems, the Priority Ceiling Protocol (PCP) has been widely accepted and supported in real-time operating systems. However, it remains arguable as to whether there exists a preferable approach for resource sharing in multiprocessor systems. In this paper, we show that the proposed Resource-Oriented Partitioned (ROP) scheduling with a distributed resource sharing policy, originating from the concept of the Distributed Priority Ceiling Protocol (DPCP), can achieve a non-trivial speedup factor guarantee. Specifically, we prove that the proposed R-PCP-rm-rm algorithm achieves a speedup factor of $11-6/(m+1)$11-6/(m+1) on a platform consisting of $m$m processors, where each job of a task may request at most one shared resource at most one time. Our empirical evaluations show that the proposed algorithm is highly effective in terms of task sets deemed schedulable.

Co-Scheduling of Hybrid Transactions on Multiprocessor Real-Time Database Systems

Deriving deadlines and periods for update transactions and scheduling hybrid transactions have been recognized as an important issue in real-time database systems. Despite years of research, all existing techniques focus on assigning deadlines and periods for update transactions in uniprocessor systems and, thus, cannot be applied in multiprocessor systems. Two partitioned scheduling methods for hybrid transactions in multiprocessor systems are proposed in this paper, by developing existing scheduling methods for uniprocessor systems to multiprocessor environments. An effective strategy is proposed to improve the efficiencies of the proposed methods, under which the improvement of efficiency is at the price of the acceptance ratio of transaction sets. For addressing the low acceptance ratio of the improvement strategy, we propose a novel scheduling method. A set of experiments is conducted to test the performances of the proposed methods. To the best of our knowledge, this is the first paper for co-scheduling of hybrid transactions on multiprocessor platforms.

DPVFS: a dynamic procrastination cum DVFS scheduler for multi-core hard real-time systems

Optimising energy consumption has become the primary focus of research in recent years. Static and dynamic energy optimisation during task scheduling is one of the most prominent measures available. This is achieved mainly by shutdown and slowdown techniques. In uni-processor real-time systems, the most widely used shutdown and slowdown techniques are dynamic procrastination (DP) and dynamic voltage and frequency scaling (DVFS). This paper proposes DPVFS a hard real-time task scheduler for multi-core (MC) system to optimise overall energy consumption without deadline misses. DPVFS combines DP and DVFS for MC systems to save overall energy consumption. DPVFS shuts the processor down whenever possible with the help of procrastination. If shutdown is not possible, it adjusts the voltage and frequency to reduce dynamic energy consumption. The experimental evaluation of DPVFS with synthetically generated benchmark program suites shows savings of 18.8% and 33.2% of overall energy over DP based schedulers and DVFS based schedulers respectively.

Modelling shared resource competition for multicores using adapted Tilman model

The need to meet the high computing demands under constraints of power, low latency, inter-process interference, etc. has led to a shift in the paradigm from uniprocessor systems to multicore systems. The challenge in these multicore systems arises from the fact that these cores are not independent in functioning, rather they share few on-chip and off-chip resources. This resource sharing also affects performance due to cross-core interference. Different workloads running on these cores demand different resources for their growth in performance. In this work we examine the contention due to resource sharing amongst co-runners using the adapted multi-species Tilman model. Thereafter, alternative solutions like application scheduling and resource partitioning are simulated using the built model. Based on the simulation results a comparison between solo and co-running systems for different applications under both the solution regimes is conducted at various resource levels. The observed phenomenon can be used for prior study or prediction of performance of applications when co-run to mitigate contention due to shared resources.

Modelling shared resource competition for multicores using adapted Tilman model

The need to meet the high computing demands under constraints of power, low latency, inter-process interference, etc. has led to a shift in the paradigm from uniprocessor systems to multicore systems. The challenge in these multicore systems arises from the fact that these cores are not independent in functioning, rather they share few on-chip and off-chip resources. This resource sharing also affects performance due to cross-core interference. Different workloads running on these cores demand different resources for their growth in performance. In this work we examine the contention due to resource sharing amongst co-runners using the adapted multi-species Tilman model. Thereafter, alternative solutions like application scheduling and resource partitioning are simulated using the built model. Based on the simulation results a comparison between solo and co-running systems for different applications under both the solution regimes is conducted at various resource levels. The observed phenomenon can be used for prior study or prediction of performance of applications when co-run to mitigate contention due to shared resources.

Improving and Estimating the Precision of Bounds on the Worst-Case Latency of Task Chains

One major issue that hinders the use of performance analysis in industrial design processes is the pessimism inherent to any analysis technique that applies to realistic system models. Indeed, such analyses may conservatively declare unschedulable systems that will in fact never miss any deadlines. We advocate the need to compute not only tight upper bounds on worst-case behaviors but also tight lower bounds. As a first step, we focus on uniprocessor systems executing a set of sporadic or periodic hard real-time task chains. Each task has its own priority, and the chains are scheduled according to the fixed-priority pre-emptive scheduling policy. Computing the worst-case end-to-end latency (WCEL) of each chain is complex because of the intricate relationship between the task priorities. Compared to the state of the art, our analysis provides upper bounds on the WCEL in the more general case of asynchronous task chains, and also provides lower bounds on the WCEL both for synchronous and asynchronous chains. Our computed lower bounds correspond to actual system executions exhibiting a behavior that is as close to the worst case as possible, while all other approaches rely on simulations. Extensive experiments show the relevance of lower bounds on the worst-case behavior for the industrial design of real-time embedded systems.

Scheduling Analysis of Imprecise Mixed-Criticality Real-Time Tasks

In this paper, we study the scheduling problem of the imprecise mixed-criticality model (IMC) under earliest deadline first with virtual deadline (EDF-VD) scheduling upon uniprocessor systems. Two schedulability tests are presented. The first test is a concise utilization-based test which can be applied to the implicit deadline IMC task set. The suboptimality of the proposed utilization-based test is evaluated via a widely-used scheduling metric, speedup factors . The second test is a more effective test but with higher complexity which is based on the concept of demand bound function (DBF). The proposed DBF-based test is more generic and can apply to constrained deadline IMC task set. Moreover, in order to address the high time cost of the existing deadline tuning algorithm, we propose a novel algorithm which significantly improve the efficiency of the deadline tuning procedure. Experimental results show the effectiveness of our proposed schedulability tests, confirm the theoretical suboptimality results with respect to speedup factor, and demonstrate the efficiency of our proposed algorithm over the existing deadline tunning algorithm. In addition, issues related to the implementation of the IMC model under EDF-VD are discussed.