Performance evaluation and energy consumption of a real-time heterogeneous grid system using DVS and DPM

Energy consumption of large scale systems has been severely studied due to economic and ecological reasons. This paper studies energy gains that come from the application of two popular energy saving techniques, Dynamic Voltage Scaling (DVS) and Dynamic Power Management (DPM), in a real-time 2-level heterogeneous grid system. While these techniques generally work in a competitive way, we show that under certain circumstances they can work together and achieve greater savings when they are both applied at the processor level. A simulation model is used to evaluate the performance of the system. Experimental results show encouraging energy savings up to 46% and minimum performance degradation when both energy saving techniques are applied.     

面向可信嵌入式系统的随机实时任务能耗优化

针对可信嵌入式系统应用中将任务的最坏情况下的执行时间(WCET)作为任务的实际执行时间,导致系统资源的极大浪费的问题,提出了一种基于随机任务概率模型的方法。首先,考虑任务执行时间具有特定概率分布,并且任务具有不错过其死限的概率(NDVP)需求,同时考虑了动态电压和频率调整(DVFS)对系统可靠性的影响,利用该技术降低能耗。然后,基于动态规划算法,提出了一种具有多项式运行时间的优化算法,并进一步设计了状态剔除规则降低算法运行开销。仿真表明,所提算法与最坏执行时间模型下的最优算法相比,系统能耗降低了30%以上。实验结果表明,考虑任务的随机执行时间能在保证系统可靠性的同时大大节约系统资源。     

Transition-aware DVS algorithm for real-time systems using tree structure analysis

Dynamic voltage scaling (DVS) is a key technique for embedded real-time systems to reduce energy consumption by lowering the supply voltage and operating frequency. Many existing DVS algorithms have to generate the canonical schedules or estimate the lengths of slack time in advance for generating the voltage scaling decisions. Therefore, these methods have to compute the schedules with exponential time complexities in general. In this paper, we consider a set of jitter-controlled, independent, periodic, hard real-time tasks scheduled according to preemptive pinwheel model. Our approach constructs a tree structure corresponding to a schedule and maintains the data structure at each early-completion point. Our approach consists of off-line and on-line algorithms which consider the effects of transition time and energy. The off-line and on-line algorithm takes O(k + n log n) and O(k + (p_max/p_min)) time complexity, respectively, where n, k, p_max and p_min denotes the number of tasks, jobs, longest and shortest task period, respectively. Experimental results show that the proposed approach is effective in reducing computational complexity, transition time and energy overhead.     

考虑生产任务的制造设备关键部件的机会维修优化

为解决制造设备关键部件的维修决策问题,将生产任务信息、视情维修以及机会维修相结合,考虑设备关键部件的剩余价值以及可靠性风险建立维修决策优化模型.在已有关于视情维修和机会维修成果的基础上,考虑关键部件的剩余寿命与下一阶段生产任务时长间的关系,以总成本最小化为目标确定是否在相邻生产任务间利用维修机会,使得在任务顺利进行的条件下降低成本.基于逆高斯过程进行部件退化建模,计算不同维修组合对应的失效概率,进而构建成本最小化目标规划函数并通过仿真算法得到预防性维修的最优值.最后通过数值实验验证所提出维修策略的有效性.     

服务器关键能耗部件实时功率测量系统的设计与实现

针对计算机能耗较多、功率实时监测系统较为缺乏的不足,提出基于atmegal16单片机的服务器关键能耗部件测量系统的设计方案和实现方法。该系统能分别对服务器整机、硬盘、CPU与内存的功耗进行实时测量并最终显示实时变化曲线;传感器模块采用穿孔式外接元器件,无插入损耗,从而达到测量无损的目的;单片机软件部分基本采用中断方式完成。实验结果表明：该系统测量无损且运行稳定,测量结果与标准功率计值保持一致,能够实时反映服务器关键部件功率的变化情况。     

Assigning real-time tasks to heterogeneous processors by applying ant colony optimization

The problem of determining whether a set of periodic tasks can be assigned to a set of heterogeneous processors without deadline violations has been shown, in general, to be NP-hard. This paper presents a new algorithm based on ant colony optimization (ACO) metaheuristic for solving this problem. A local search heuristic that can be used by various metaheuristics to improve the assignment solution is proposed and its time and space complexity is analyzed. In addition to being able to search for a feasible assignment solution, our extended ACO algorithm can optimize the solution by lowering its energy consumption. Experimental results show that both the prototype and the extended version of our ACO algorithm outperform major existing methods; furthermore, the extended version achieves an average of 15.8% energy saving over its prototype.     

A multiframe model for real-time tasks

The well known periodic task model of C.L. Liu and J.W. Layland (1973) assumes a worst case execution time bound for every task and may be too pessimistic if the worst case execution time of a task is much longer than the average. We give a multiframe real time task model which allows the execution time of a task to vary from one instance to another by specifying the execution time of a task in terms of a sequence of numbers. We investigate the schedulability problem for this model for the preemptive fixed priority scheduling policy. We show that a significant improvement in the utilization bound can be established in our model     

Least-space-time-first scheduling algorithm: A policy for complex real-time tasks in multiple processor systems

Significant prior work exists on scheduling for the ‘simple-tasks-single-processor’, ‘simple-tasks-multiple-processor’, and ‘complex-tasks-single-processor’, but few researchers have yet considered the ‘complex-tasks-multiple-processor’ model. We propose a new algorithm, Least Space-Time First (LSTF), to deal with the general ‘complex-task-multiple-processor’ model. We demonstrate that LSTF outperforms the Highest-Level-First, Earliest-Deadline-First and Least-Laxity-First scheduling disciplines in the sense of minimizing maximum tardiness of a set of tasks. LSTF can gracefully incorporate some realistic overhead assumptions, including context switch and communication. The Unit Precedence Graph and Soft-Precedence Edges significantly facilitate implementation of LSTF.     

Efficient and robust probabilistic guarantees for real-time tasks

This paper presents a new method for providing probabilistic real-time guarantees to tasks scheduled through resource reservations. Previous work on probabilistic analysis of reservation-based schedulers is extended by improving the efficiency and robustness of the probability computation. Robustness is improved by accounting for a possibly incomplete knowledge of the distribution of the computation times (which is typical in realistic applications). The proposed approach computes a conservative bound for the probability of missing deadlines, based on the knowledge of the probability distributions of the execution times and of the inter-arrival times of the tasks. In this paper, such a bound is computed in realistic situations, comparing it with simulative results and with the exact computation of deadline miss probabilities (without pessimistic bounds). Finally, the impact of the incomplete knowledge of the execution times distribution is evaluated.     

Optimizing resource speed for two-stage real-time tasks

Multiple resource co-scheduling algorithms and pipelined execution models are becoming increasingly popular, as they better capture the heterogeneous nature of modern architectures. The problem of scheduling tasks composed of multiple stages tied to different resources goes under the name of “flow-shop scheduling”. This problem, studied since the ‘50s to optimize production plants, is known to be NP-hard in the general case. In this paper, we consider a specific instance of the flow-shop task model that captures the behavior of a two-resource (DMA-CPU) system. In this setting, we study the problem of selecting the optimal operating speed of the two resources with the goal of minimizing power usage while meeting real-time schedulability constraints. In particular, we derive an algorithm that finds the optimal speed of one resource while the speed of the other resource is kept constant. Then, we discuss how to extend the proposed approach to jointly optimize the speed of the two resources. In addition, applications to multiprocessor systems and energy minimization are considered. All the proposed algorithms run in polynomial time, hence they are suitable for online operation even in the presence of variable real-time workload.     

A feedback scheduler for real-time controller tasks

The problem studied in this paper is how to distribute computing resources over a set of real-time control loops in order to optimize the total control performance. Two subproblems are investigated: how the control performance depends on the sampling interval, and how a recursive resource allocation optimization routine can be designed. Linear quadratic cost functions are used as performance indicators. Expressions for calculating their dependence on the sampling interval are given. An optimization routine, called a feedback scheduler, that uses these expressions is designed.     

Slack computation for DVS algorithms in fixed-priority real-time systems using fluid slack analysis

This work presents a scheduling algorithm to reduce the energy of hard real-time tasks with fixed priorities assigned in a rate-monotonic policy. Sets of independent tasks running periodically on a processor with dynamic voltage scaling (DVS) are considered as well. The proposed online approach can cooperate with many slack-time analysis methods based on low-power work demand analysis (lpWDA) without increasing the computational complexity of DVS algorithms. The proposed approach introduces a novel technique called low-power fluid slack analysis (lpFSA) that extends the analysis interval produced by its cooperative methods and computes the available slack in the extended interval. The lpFSA regards the additional slack as fluid and computes its length, such that it can be moved to the current job. Therefore, the proposed approach provides the cooperative methods with additional slack. Experimental results show that the proposed approach combined with lpWDA-based algorithms achieves more energy reductions than do the initial algorithms alone.     

Improvement in feasibility testing for real-time tasks

Scheduling analysis in real-time systems require an off-line feasibility (schedulability) test to guarantee the response time of critical tasks. There are fast and efficient tests for static schedulers. However, when a dynamic scheduler is required, the available tests are not as feast and efficient as static ones. In this paper, two different characterizations of feasible task sets are presented. These characterizations lead to an new feasibility algorithm. The proposed algorithm has an worst-case exponential complexity, but experimental results indicated that it runs on pseudo-polynomial time for a very large percentage of task sets. The algorithm also provides a sufficient condition for feasible asynchronous task sets. One of the main contributions of this work is the theoretical approach used to obtain the new feasibility test. The results of a large number of simulations, as well as, the theoretical demonstrations point out the improvements reached over previous tests.This work has been partially supported by a grant of CICYT No. TAP94-0511 of the Spanish Government     

On the fundamentals of leakage aware real-time DVS scheduling for peak temperature minimization

As the consequence of the exponentially increased power density on integrated circuits, thermal issues are becoming critical in design of computing systems. Moreover, as both leakage and thermal issues have become more prominent in the deep sub-micron domain, a power and thermal aware design technique becomes less effective if the leakage/temperature dependency is not appropriately addressed. In this paper, we take into account the dependency among the leakage, the temperature, and the supply voltage in our theoretical analysis and explore the fundamental characteristics on how to employ dynamic voltage scaling (DVS) to reduce the peak operating temperature. We find that, for a specific interval, a real-time schedule using the lowest constant speed is not necessarily the optimal choice any more in minimizing the peak temperature. We identify the scenarios when a schedule using two different speeds can outperform the one using the lowest constant speed. In addition, we find that, when scheduling a periodic task set, the constant speed schedule is still the optimal solution for minimizing the peak temperature when the temperature is at its stable status. We formulate our conclusions into several theorems with formal proofs.     

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.     

Dynamic scheduling of hard real-time tasks and real-time threads

The authors investigate the dynamic scheduling of tasks with well-defined timing constraints. They present a dynamic uniprocessor scheduling algorithm with an O(n log n) worst-case complexity. The preemptive scheduling performed by the algorithm is shown to be of higher efficiency than that of other known algorithms. Furthermore, tasks may be related by precedence constraints, and they may have arbitrary deadlines and start times (which need not equal their arrival times). An experimental evaluation of the algorithm compares its average case behavior to the worst case. An analytic model used for explanation of the experimental results is validated with actual system measurements. The dynamic scheduling algorithm is the basis of a real-time multiprocessor operating system kernel developed in conjunction with this research. Specifically, this algorithm is used at the lowest, threads-based layer of the kernel whenever threads are created     

Bat algorithm for constrained optimization tasks

In this study, we use a new metaheuristic optimization algorithm, called bat algorithm (BA), to solve constraint optimization tasks. BA is verified using several classical benchmark constraint problems. For further validation, BA is applied to three benchmark constraint engineering problems reported in the specialized literature. The performance of the bat algorithm is compared with various existing algorithms. The optimal solutions obtained by BA are found to be better than the best solutions provided by the existing methods. Finally, the unique search features used in BA are analyzed, and their implications for future research are discussed in detail.     

An improved Henry gas solubility optimization for optimization tasks

Applied Intelligence - The henry gas solubility optimization (HGSO) is a new nature-inspired algorithm that mimics Henry Gas Solubility to solve global optimization problems. The main changes of...     

一种启发式多传感器任务实时动态分配算法

传感器任务的分配是传感器管理的重要问题。为提高任务分配的实时性和实效性,基于紧急任务优先、最早完成任务优先、复用能力最小优先和随机分配四条原则,提出了一种启发式多传感器任务实时动态分配算法。计算机模拟仿真表明:该算法既能保证优先级任务较早的执行,又能探测到任务的失败,还能维持传感器负载平衡,是一种快捷、高效的分配算法。