实时数据库数据特征对事务处理的影响

实时数据库系统事务调度一般只考虑事务的截止期,这对保证满足事务访问数据的时间正确性是不够的。文中针对时序数据的特点给出了同时考虑事务的截止及“数据截止期”的事务优先级分派、调度及并发控制方法。     

基于数据时态特性的实时事务并发控制

通过对数据时态特性及其对事务调度的影响进行分析,提出了基于数据时态特性的实时事务并发控制算法.该算法根据数据截止期及事务的执行时间估算,改进了事务的验证规则,对事务的提交顺序进行调整,提高了系统的实时性能.理论分析与实验结果表明:该算法降低了事务重启个数及超截止期百分率,性能要优于已有的实时并发控制算法.     

移动环境中实时事务数据的广播调度

提出一种基于需求广播模型的实时事务数据的广播调度策略．在本策略中，综合考虑了事务存取多个数据项和满足定时限制的要求．本策略的目标是使错过截止期的事务数量最少，对我们提出的算法，通过建立模拟实验进行评价，结果显示它比现有的策略有明显的优越性．     

嵌入式实时数据库ARTs-EDB事务调度实现技术

着重讨论了嵌入式实时数据库系统ARTs-EDB中事务调度实现技术。给出了ARTs-EDB中事务的执行模型、状态及状态变迁；设计了一个考虑事务类型和截止期的多层动态可调整优先级分派方法；给出了一个优先级驱动可抢占的实时事务调度策略，以保证更多事务满足截止期要求。     

一种改进的实时事务调度策略

文中提出一种改进的实时事务调度策略，由改进的优先级分配机制ＨＡＥＤ和相应的实时并发控制算法两部分组成。针对嵌套事务模型，提出了一种子事务截止期的分配方法。最后对改进策略进行了性能模拟。模拟结果表明，改进策略的系统性能在一定程度上优于原有的调度策略。     

支持断接的移动实时事务调度

在移动计算环境中,事务移动性和频繁断接使得传统实时事务调度机制不足以支持移动实时事务的执行。该文给出了一个支持无线网络频繁断接的软实时事务调度协议SSRTD。该协议是基于事务具有扩展截止期和结果相似性进行设计的,并可根据移动主机的连接状况决定事务执行结果的发送时间。通过性能测试,表明所提出的调度策略能提高移动实时事务的成功率。     

实时数据库的定时调度策略

本文针对目前实时系统中优先级调度的不足,提出了一种新的定时调度的事务调度方法。该方法能以系统时钟的最小量度精确地实现事务的定时调度。它与传统的优先级调度方法结合使用,可以更好地满足事务的截止期。本文还讨论了实现该策略的一些问题。     

基于预分析的实时事务处理

实时数据库系统中事务可能有实时限制（典型地为截止期），事务超过截止期可能给系统带来灾难性后果，这就要求系统能对事务的执行进行预分析与预安排，传统事务处理由于事务执行过程中的多种不确定因素（数据I／O，页面缺失，事务之间的各种相关性等），事务执行具有动态不可预报性，提出了一种事务预分析处理的三段法，数据说明阶段的静态预分析提取事务静态信息；初始动态预分析阶段构造事务的“影了事务”，并在适当的时机调度影子事务执行，以保证事务操作之前所需数据已在内存，事务执行时动态预分析阶段构造事务的相关性，为事务执行的结构，行为及时间正确性提供统一的实现机制，事务执行具有更好的可预报性，从而事务能更好地满足截止期。     

基于验证因子的实时多版本并发控制

为了解决乐观并发控制方法的不必要重启与即将超过截止期事务的重启引发的负载问题,提出了验证因子的概念及基于验证因子的实时多版本并发控制方法（MVOCC-VF）．通过对验证因子的检测,优先调度完成度较高的事务;同时结合多版本技术增强事务调度的并发度,避免了更多的不必要重启问题,尤其能有效保证即将超截止期事务的完成,并证明了协议的正确性．实验结果表明：与已有的实时并发控制方法相比,MVOCC—VF降低了事务重启个数及超截止期百分率,提高了系统的总体性能．     

大规模交易事务的一种调度方法

为解决大规模交易中复杂事务密集访问引起的关键事务调度性能低下问题，文中通过研究交易事务的分类特点和结构特征，提出了一种基于虚拟截止期和时间戳排序的双级调度策略，通过分割长撮合事务和确定合理的步长因子，保证关键事务的优先调度，模拟和测试结果表明，在不产生事务延迟的条件下，交易事务的处理速率为1970事务／秒，平均响应时间为0．5ms，满足大规模电子交易的需要．     

一种实时数据库事务处理策略的应用

通过对实时事务特点的分析和对实时数据库中事务处理策略的研究,提出了一种把优先级分配策略与定时调度策略相结合的实时事务调度策略,并融合快照机制与优先级分配策略建立了一种新的基于乐观法的并发控制策略。将这种策略应用于一个实时系统的实时数据库,系统的性能和稳定性明显提高。实验结果表明所给出的新策略及方法的正确性和有效性。     

Chronological scheduling of transactions with temporal dependencies

Database applications often impose temporal dependencies between transactions that must be satisfied to preserve data consistency. The extant correctness criteria used to schedule the execution of concurrent transactions are either time independent or use strict, difficult to satisfy real-time constraints. On one end of the spectrum, serializability completely ignores time. On the other end, deadline scheduling approaches consider the outcome of each transaction execution correct only if the transaction meets its real-time deadline. In this article, we explore new correctness criteria and scheduling methods that capture temporal transaction dependencies and belong to, the broad area between these two extreme approaches. We introduce the concepts ofsuccession dependency andchronological dependency and define correctness criteria under which temporal dependencies between transactions are preserved even if the dependent transactions execute concurrently. We also propose achronological scheduler that can guarantee that transaction executions satisfy their chronological constraints. The advantages of chronological scheduling over traditional scheduling methods, as well as the main issues in the implementation and performance of the proposed scheduler, are discussed.     

Scheduling transactions with temporal constraints: exploiting data semantics

In this paper, issues involved in the design of a real-time database which maintains data temporal consistency are discussed. The concept of data-deadline is introduced and time cognizant transaction scheduling policies are proposed. Informally, data-deadline is a deadline assigned to a transaction due to the temporal constraints of the data accessed by the transaction. Further, two time cognizant forced wait policies which improve performance significantly by forcing a transaction to delay further execution until a new version of sensor data becomes available are proposed. A way to exploit temporal data similarity to improve performance is also proposed. Finally, these policies are evaluated through detailed simulation experiments. The simulation results show that taking advantage of temporal data semantics in transaction scheduling can significantly improve the performance of user transactions in realtime database systems. In particular, it is demonstrated that under the forced wait policy, the performance can be improved significantly. Further improvements result by exploiting data similarity.     

On scheduling transactions in a grid processing system considering load through Ant Colony Optimization

Load balanced transaction scheduling problem is an important issue in distributed computing environments including grid system. This problem is known to be NP-hard and can be solved by using heuristic as well as any meta-heuristic method. We ponder over the problem of the load balanced transaction scheduling in a grid processing system by using an Ant Colony Optimization for load balancing. The problem that we consider is to achieve good execution characteristics for a given set of transactions that has to be completed within their given deadline. We propose a transaction processing algorithm based on Ant Colony Optimization (ACO) for load balanced transaction scheduling. We modify two meta-heuristic along with ACO and three heuristic scheduling algorithms for the purpose of comparison with our proposed algorithm. The results of the comparison show that the proposed algorithm provides better results for the load balanced transaction scheduling in the grid processing system.     

基于功能替代的实时事务调度

实时数据库事务除满足一般的一致性（完整性）限制外必须满足时限，故高成功率的调度策略是系统的关键，该文提出了一种基于功能替代性的事务模型，它定义事务为若干任务（事务步）的集合，每个任务又由若干功能等价的子事务组成，在每个任务（事务步）中取一个子事务就组成该事务的一个替代，该模型具有较强的描述能力，文章研究了事务预分析的策略与方法，分析了事务的可调度性，在此基础上，给出了相应的调度策略与方法，替代成为事务调度和并发控制的基本单位，一个替代成功执行则该事务可提交，但某替代夭折时还可选另外的替代，只有当全部替代都失败或超过（或必定会超过）截止期时，该事务才夭折，事务的替代特性使事务的执行有多条路径，提高了事务的适应能力和应力能力，从而提高了事务的成功率，文章还针对一个实验系统进行了模拟实验与性能分析。     

实时数据库中具有时态限制的事务调度算法

在实时数据库中，事务对时态数据对象的访问在很多领域的应用日益广泛．目前实时数据库中的事务调度算法大多仅考虑数据的逻辑一致性，而忽略了满足时态的一致性．本文提出了临时数据截止期的概念，以定理的形式证明了低成本的预测算法的可行性，尽可能早地终止或推迟无法满足时态一致性要求的事务的执行，节省了计算资源以供其它事务执行．在此基础上提出了新的实时数据库中具有时态限制的事务调度算法TDDBPA(Temporary Data Deadline—Based Predictive Algorithm)．通过与目前所知的有效算法比较，本算法在性能指标上(事务失败率、浪费的CPU时间)明显优于这些算法．     

Improving Predictability of Transaction Execution Times in Real-time Databases

We present a design for multi-versionconcurrency control and recovery in a main memory database, anddescribe logical and physical versioning schemes that allowread-only transactions to execute without obtaining data itemlocks or system latches. Our schemes enable a system to providethe guarantee that updaters will never interfere with read-onlytransactions, and read-only transactions will not be delayeddue to data contention. Consequently, transaction executionsbecome more predictable—this partially alleviates a majorproblem in real-time database system (RTDBS) scheduling, namely,significant unpredictability in transaction execution times.As a result, in addition to a transaction's deadline, a moreaccurate estimate of its execution time can also be taken intoaccount, thus facilitating better scheduling decisions. Our contributionsinclude several space saving techniques for the main-memory implementation,including improved methods for logical aging of data items andthe introduction of physical aging for low-level structures.Some of these schemes have been implemented on a widely-usedsoftware platform within Lucent, and the full scheme is implementedin the Dalí main-memory storage manager.     

Hadoop平台下基于截止时间限制的动态调度算法的研究

为了满足有截止时间限制的MapReduce作业的需求,提出一种基于截止时间限制的动态调度算法（DCDS）。该算法实时监控作业运行状况,并对作业运行时间进行动态估算,从而确定作业优先级;对于时间紧迫的作业,可通过抢占策略来保证在用户要求的截止时间内完成。实验结果表明,与Hadoop平台现有的调度算法相比,该算法不仅能满足作业截止时间的要求,也提高了系统资源的利用率和吞吐量。     

A multi-objective reinforcement learning algorithm for deadline constrained scientific workflow scheduling in clouds

Recently, a growing number of scientific applications have been migrated into the cloud. To deal with the problems brought by clouds, more and more researchers start to consider multiple optimization goals in workflow scheduling. However, the previous works ignore some details, which are challenging but essential. Most existing multi-objective workflow scheduling algorithms overlook weight selection, which may result in the quality degradation of solutions. Besides, we find that the famous partial critical path (PCP) strategy, which has been widely used to meet the deadline constraint, can not accurately reflect the situation of each time step. Workflow scheduling is an NP-hard problem, so self-optimizing algorithms are more suitable to solve it.In this paper, the aim is to solve a workflow scheduling problem with a deadline constraint. We design a deadline constrained scientific workflow scheduling algorithm based on multi-objective reinforcement learning (RL) called DCMORL. DCMORL uses the Chebyshev scalarization function to scalarize its Q-values. This method is good at choosing weights for objectives. We propose an improved version of the PCP strategy calledMPCP. The sub-deadlines in MPCP regularly update during the scheduling phase, so they can accurately reflect the situation of each time step. The optimization objectives in this paper include minimizing the execution cost and energy consumption within a given deadline. Finally, we use four scientific workflows to compare DCMORL and several representative scheduling algorithms. The results indicate that DCMORL outperforms the above algorithms. As far as we know, it is the first time to apply RL to a deadline constrained workflow scheduling problem.