一种新的基于优先级表的实时调度算法

本文提出了一种新的基于优先级表的实时调度算法 ,称作截止期—价值密度优先 (Deadline ValueDen sityFirst)算法 ,简称DVDF算法 .DVDF算法综合考虑了实时任务的截止期和价值密度两个参数 ,能够更好地适应不同的负载情况 .通过使用正常负载和过载情况下的典型数据对算法进行仿真研究表明 ,这种算法比单纯考虑截止期的EDF(EarliestDeadlineFirst)算法在性能方面有明显的改进 ,特别是在系统过载的情况下 ,能够优雅地降级

基于优先级表的实时调度算法及其实现

讨论了综合考虑任务的截止期和价值两个特征参数的优先级表设计方法,提出了EDV(earliestdeadlinevalue)与VED(valueearliestdeadline)两种不同的基于优先级表的实时任务调度算法,并且利用多重链表给出了这两种算法的实现,包括任务接收策略与任务完成/夭折策略的算法实现.这种优先级表设计方法及其基于多重链表的实现方法也适用于对任务的其他两种甚至3种不同特征参数之间的综合.基于累积实现价值率、加权截止期保证率与差分截止期保证率3个方面,分析了VED算法与EDV算法的性能,实验结果表明,在所有负载条件下VED算法与EDV算法相对于EDF(earliestdeadlinefirst)算法与HVF(highestvaluefirst)算法都有很大的性能改进.

实时异构系统的动态调度算法研究

实时多处理器系统是解决复杂实时应用的有效手段.然而,目前对实时多处理器调度算法的研究却大多集中在同构系统上,对实时异构系统的调度则研究得比较少.提出了一种新的实时异构系统的动态调度算法.该算法采用了集中式的调度方案,同时,引入了一个新的任务分配策略,从而通过提高任务可行性而提高了算法的调度成功率.此外,为了评估该算法的性能,还进行了大量的模拟研究.由于近视算法经简单修改便可以被应用到实时异构系统的动态调度中,因此,在模拟研究中,以近视算法作为基准,将其应用于实时异构系统动态调度时的性能与新算法进行了比较.模拟结果显示,在多种任务参数的取值下,新算法的调度成功率均高于近视算法.

改进的最小空闲时间优先调度算法

最小空闲时间优先(least slack first,简称LSF)算法结合任务执行的缓急程度来给任务分配优先级.任务所剩的空闲时间越少,就越需要尽快执行.然而,LSF算法造成任务之间的频繁切换或严重的颠簸现象,增大了系统开销,并限制了其应用.在调度策略中设置抢占阈值可以减少任务之间的切换,但现有的抢占阈值设置方法因受到固定优先级的限制而不适用于LSF算法.为了减轻LSF算法的颠簸现象,基于抢占阈值的思想,提出适用于LSF算法的抢占阈值分配方法,动态地给每个任务配置抢占阈值.任务的抢占阈值是随着任务执行的缓急程度不同而动态地变化的,而且不受任务个数的限制.仿真结果表明,通过对LSF算法的改进,任务之间的切换大大减少,同时降低了任务截止期错失率.该改进型算法对设计和实现实时操作系统具有一定的参考价值.

一种新的实时多处理器系统的动态调度算法

实时多处理器系统的动态调度算法一直是实时系统研究中的重要课题,而评价实时调度算法性能的一个最重要的指标是调度成功率.在近视算法的基础上提出了一种新的实时多处理器系统的动态调度算法——节约算法.在该算法中,提出了一个新的处理器选择策略,从而提高了算法的调度成功率.同时,为了研究节约算法的有效性,对其进行了大量的模拟,分析了一些任务参数的变化对算法调度成功率的影响,并与近视算法的调度成功率进行了比较.模拟结果显示,节约算法的调度成功率要优于近视算法.

模糊反馈控制实时调度算法

为了解决模糊不确定任务集在不可预测环境下的动态抢占调度问题,应用模糊规则和模糊调度理论,提出一个基于模糊反馈控制的调度算法,并建立相应的调度架构.该架构由基本调度器和模糊反馈控制两部分组成.用模糊调度算法作为基本调度器的调度算法,将任务集按不同优先级等级进行划分,优先级等级高的任务优先调度,从而使得更多的重要任务得到调度;模糊控制器与任务流调节策略一起构成模糊反馈控制部分.仿真结果表明,模糊反馈控制调度可以很好地控制任务的截止期错失率,解决任务特征可能是模糊不确定或不可预测民政部下的调度问题,提高重要任务的调度成功率.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Dynamic global security-aware synthesis using System-C

A dynamic global security-aware synthesis flow using the SystemC language is presented. SystemC security models are first specified at the system or behavioural level using a library of SystemC behavioural descriptions which provide for the reuse and extension of security modules. At the core of the system is incorporated a global security-aware scheduling algorithm which allows for scheduling to a mixture of components of varying security level. The output from the scheduler is translated into annotated nets which are subsequently passed to allocation, optimisation and mapping tools for mapping into circuits. The synthesised circuits incorporate asynchronous secure power-balanced and fault-protected components. Results show that the approach offers robust implementations and efficient security/area trade-offs leading to significant improvements in turnover.

Estudo e proposta de algoritmo para a minimização do corte de eólica no contexto de uma análise custo-benefício multicritério

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Assigning real-time tasks on heterogeneous multiprocessors with two unrelated types of processors

A preliminary version of this paper appeared in Proceedings of the 31st IEEE Real-Time Systems Symposium, 2010, pp. 239–248.

Laxity dynamics and LLF schedulability analysis on multiprocessor platforms

LLF (Least Laxity First) scheduling, which assigns a higher priority to a task with a smaller laxity, has been known as an optimal preemptive scheduling algorithm on a single processor platform. However, little work has been made to illuminate its characteristics upon multiprocessor platforms. In this paper, we identify the dynamics of laxity from the system’s viewpoint and translate the dynamics into LLF multiprocessor schedulability analysis. More specifically, we first characterize laxity properties under LLF scheduling, focusing on laxity dynamics associated with a deadline miss. These laxity dynamics describe a lower bound, which leads to the deadline miss, on the number of tasks of certain laxity values at certain time instants. This lower bound is significant because it represents invariants for highly dynamic system parameters (laxity values). Since the laxity of a task is dependent of the amount of interference of higher-priority tasks, we can then derive a set of conditions to check whether a given task system can go into the laxity dynamics towards a deadline miss. This way, to the author’s best knowledge, we propose the first LLF multiprocessor schedulability test based on its own laxity properties. We also develop an improved schedulability test that exploits slack values. We mathematically prove that the proposed LLF tests dominate the state-of-the-art EDZL tests. We also present simulation results to evaluate schedulability performance of both the original and improved LLF tests in a quantitative manner.

Practical aspects of slot-based task- splitting dispatching in its schedulability analysis

Consider the problem of scheduling a set of sporadic tasks on a multiprocessor system to meet deadlines using a tasksplitting scheduling algorithm. Task-splitting (also called semipartitioning) scheduling algorithms assign most tasks to just one processor but a few tasks are assigned to two or more processors, and they are dispatched in a way that ensures that a task never executes on two or more processors simultaneously. A certain type of task-splitting algorithms, called slot-based task-splitting, is of particular interest because of its ability to schedule tasks at high processor utilizations. We present a new schedulability analysis for slot-based task-splitting scheduling algorithms that takes the overhead into account and also a new task assignment algorithm.

LLF schedulability analysis on multiprocessor platforms

LLF (Least Laxity First) scheduling, which assigns a higher priority to a task with smaller laxity, has been known as an optimal preemptive scheduling algorithm on a single processor platform. However, its characteristics upon multiprocessor platforms have been little studied until now. Orthogonally, it has remained open how to efficiently schedule general task systems, including constrained deadline task systems, upon multiprocessors. Recent studies have introduced zero laxity (ZL) policy, which assigns a higher priority to a task with zero laxity, as a promising scheduling approach for such systems (e.g., EDZL). Towards understanding the importance of laxity in multiprocessor scheduling, this paper investigates the characteristics of ZL policy and presents the first ZL schedulability test for any work-conserving scheduling algorithm that employs this policy. It then investigates the characteristics of LLF scheduling, which also employs the ZL policy, and derives the first LLF-specific schedulability test on multiprocessors. It is shown that the proposed LLF test dominates the ZL test as well as the state-of-art EDZL test.

Estimating Effective Slowdown of Tasks in Energy-Aware Clouds

Consolidation consists in scheduling multiple virtual machines onto fewer servers in order to improve resource utilization and to reduce operational costs due to power consumption. However, virtualization technologies do not offer performance isolation, causing applications’ slowdown. In this work, we propose a performance enforcing mechanism, composed of a slowdown estimator, and a interference- and power-aware scheduling algorithm. The slowdown estimator determines, based on noisy slowdown data samples obtained from state-of-the-art slowdown meters, if tasks will complete within their deadlines, invoking the scheduling algorithm if needed. When invoked, the scheduling algorithm builds performance and power aware virtual clusters to successfully execute the tasks. We conduct simulations injecting synthetic jobs which characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our strategy can be efficiently integrated with state-of-the-art slowdown meters to fulfil contracted SLAs in real-world environments, while reducing operational costs in about 12%.

The three-dimensional morphology of simulated and observed convective storms over southern England

A set of high-resolution radar observations of convective storms has been collected to evaluate such storms in the UK Met Office Unified Model during the DYMECS project (Dynamical and Microphysical Evolution of Convective Storms). The 3-GHz Chilbolton Advanced Meteorological Radar was set up with a scan-scheduling algorithm to automatically track convective storms identified in real-time from the operational rainfall radar network. More than 1,000 storm observations gathered over fifteen days in 2011 and 2012 are used to evaluate the model under various synoptic conditions supporting convection. In terms of the detailed three-dimensional morphology, storms in the 1500-m grid-length simulations are shown to produce horizontal structures a factor 1.5–2 wider compared to radar observations. A set of nested model runs at grid lengths down to 100m show that the models converge in terms of storm width, but the storm structures in the simulations with the smallest grid lengths are too narrow and too intense compared to the radar observations. The modelled storms were surrounded by a region of drizzle without ice reflectivities above 0 dBZ aloft, which was related to the dominance of ice crystals and was improved by allowing only aggregates as an ice particle habit. Simulations with graupel outperformed the standard configuration for heavy-rain profiles, but the storm structures were a factor 2 too wide and the convective cores 2 km too deep.