基于资源紧迫度的实时ETL弹性调度机制

在ETL系统中,当数据生产速度遇到较大的波动时,为了使现有资源在有限的条件下极大程度地满足用户的使用需求,提出了基于资源紧迫度的E TL弹性调度机制.首先,构建基于历史数据的时间序列预测模型,用来预测数据生产速度;然后,根据各个E TL过程提交的资源清单和各个服务器负载情况,对单个用户的多个E TL过程的资源进行调整.该调度机制将资源调度问题转换为多维背包问题,提出了基于资源紧迫度的动态调度算法.实验结果表明,当用户资源对所有的E TL过程稀缺的时候,该调度算法会使资源得到更好的利用.通过实验验证了弹性调度比传统的调度方式在资源利用方面具有优势.     

Multiobjective pseudo‐variable neighborhood descent for a bicriteria parallel machine scheduling problem with setup time

A multiobjective variable neighborhood descent (VND) based heuristic is developed to solve a bicriteria parallel machine scheduling problem. The problem considers two objectives, one related to the makespan and the other to the flow time, where the setup time depends on the sequence, and the machines are identical. The heuristic has a set of neighborhood structures based on swap, remove, and insertion moves. We propose changing the local search inside the VND to a sequential search through the neighborhoods to obtain nondominated points for the Pareto‐front quickly. In the numerical tests, we consider a single‐objective version of the heuristic, comparing the results on 510 benchmark instances to show that it is quite effective. Moreover, new instances are generated in accordance with the literature for the bicriteria problem, showing the ability of the proposed heuristic to return an efficient set of nondominate solutions compared with the well‐known nondominated sorting genetic algorithm II.     

Approximation algorithms for shop scheduling problems with minsum objective: A correction

We present a correction to the paper, “Approximation algorithms for shop scheduling problems with minsum objective” (Journal of Scheduling 2002; 5:287–305) by Queyranne and Sviridenko. This correction provides a correct derivation of its 2eρ approximation result. Wenhua Li and Jinjiang Yuan: Project supported by NNSFC (Grant 10371112) and NSFHN (Grant 0411011200). Maurice Queyranne: Supported by research grants from NSERC, the Natural Sciences and Engineering Research Council of Canada.     

Approximation algorithms for multi-agent scheduling to minimize total weighted completion time

We consider a multi-agent scheduling problem on a single machine in which each agent is responsible for his own set of jobs and wishes to minimize the total weighted completion time of his own set of jobs. It is known that the unweighted problem with two agents is NP-hard in the ordinary sense. For this case, we can reduce our problem to a Multi-Objective Shortest-Path (MOSP) problem and this reduction leads to several results including Fully Polynomial Time Approximation Schemes (FPTAS). We also provide an efficient approximation algorithm with a reasonably good worst-case ratio.     

Finding frequent items over sliding windows with constant update time

In this paper, we consider the problem of finding ?-approximate frequent items over a sliding window of size N. A recent work by Lee and Ting (2006) [7] solves the problem by giving an algorithm that supports query and update time, and uses space. Their query time and memory usage are essentially optimal, but the update time is not. We give a new algorithm that supports O(1) update time with high probability while maintaining the query time and memory usage as .     

Real time scheduling with a budget: Parametric-search is better than binary search

We are given a set of jobs each has a processing time, a non-negative weight, a set of possible time intervals in which it can be processed and a cost. The goal is to schedule a feasible subset S of the jobs on a single machine such that the total weight of S is maximized, and the total cost of S is within a given budget. Using Megiddo's parametric method we improve an earlier algorithm that is based on applying binary search.     

An improved approximation algorithm for two-machine flow shop scheduling with an availability constraint

We study the problem of scheduling n jobs in a two-machine flow shop where the second machine is not available for processing during a given time interval. A resumable scenario is assumed, i.e., if a job cannot be finished before the down period it is continued after the machine becomes available again. The objective is to minimize the makespan. The best fast approximation algorithm for this problem guarantees a relative worst-case error bound of 4/3. We present an improved algorithm with a relative worst-case error bound of 5/4.     

Unbounded parallel-batch scheduling with family jobs and delivery coordination

We study a scheduling problem that integrates parallel-batch production with family jobs and job delivery at the same time. The jobs are first processed on an unbounded parallel-batch machine and then delivered in batches to their specified customers by a transportation vehicle. We assume that jobs from different families (customers) cannot be processed together by the batch machine and also transported together by the vehicle. The objective is to minimize the time when the vehicle finishes delivering the last delivery batch to its customer and returns to the machine. We first show that the problem is NP-hard, and then propose for it a heuristic algorithm with a worst-case performance ratio of 3/2.     

Adaptive playout scheduling algorithm tailored for real-time packet-based voice conversations over wireless ad-hoc networks

The effective provision of real-time, packet-based voice conversations over multi-hop wireless ad-hoc networks faces several stringent constraints not found in conventional packet-based networks. Indeed, MANETs (mobile ad-hoc networks) are characterized by mobility of all nodes, bandwidth-limited channel, unreliable wireless transmission medium, etc. This environment will surely induce a high delay variation and packet loss rate impairing dramatically the user experienced quality of conversational services such as VoIP. Indeed, such services require the reception of each media unit before its deadline to guarantee a synchronous playback process. This requirement is typically achieved by artificially delaying received packets inside a de-jitter buffer. To enhance the perceptual quality the buffering delay should be adjusted dynamically throughout the vocal conversation.In this work, we describe the design of a playout algorithm tailored for real-time, packet-based voice conversations delivered over multi-hop wireless ad-hoc networks. The designed playout algorithm, which is denoted MAPA (mobility aware playout algorithm), adjusts the playout delay according to node mobility, which characterizes mobile ad-hoc networks, and talk-spurt, which is an intrinsic feature of voice signals. The detection of mobility is done in service passively at the receiver using several metrics gathered at the application layer. The perceptual quality is estimated using an augmented assessment approach relying on the ITU-T E-Model paradigm while including the time varying impairments observed by users throughout a packet-based voice conversation. Simulation results show that the tailored playout algorithm significantly outperforms conventional playout algorithms, specifically over a MANET with a high degree of mobility.     

A polynomial-time approximation scheme for the two-machine flow shop scheduling problem with an availability constraint

We study the problem of scheduling n preemptable jobs in a two-machine flow shop where the first machine is not available for processing during a given time interval. The objective is to minimize the makespan. We propose a polynomial-time approximation scheme for this problem. The approach is extended to solve the problem in which the second machine is not continuously available.     

Resource allocation with time intervals

We study a resource allocation problem where jobs have the following characteristic: each job consumes some quantity of a bounded resource during a certain time interval and induces a given profit. We aim to select a subset of jobs with maximal total profit such that the total resource consumed at any point in time remains bounded by the given resource capacity.While this case is trivially NP-hard in general and polynomially solvable for uniform resource consumptions, our main result shows the NP-hardness for the case of general resource consumptions but uniform profit values, i.e. for the case of maximizing the number of performed jobs. This result applies even for proper time intervals.We also give a deterministic (1/2−ε)-approximation algorithm for the general problem on proper intervals improving upon the currently known 1/3 ratio for general intervals. Finally, we study the worst-case performance ratio of a simple greedy algorithm.     

最小化空闲时间的Job-shop调度进化算法

为了研究Job-shop调度问题,分析了调度结果和调度过程,认为传统Job-shop调度模型的调度过程,实质是减少并减小空闲时间的组合优化过程,而且不同空闲时间对调度结果的影响程度不同。据此提出了最小化空闲时间的两个处理过程和不同空闲时间的处理顺序规则;并设计了进化算法中最小化空闲时间的初始种群生成过程、重组算子和变异算子。经典的调度基准问题对比测试表明最小化空闲时间的分析结论是正确的;最小化空闲时间过程高效可靠;最小化空闲时间的进化算法缩小了算法的搜索空间,大大提高了搜索效率,有效避免了早熟收敛现象,稳定可靠。     

灾变遗传算法求解带时间窗的车辆调度问题

提出一种可以有效求解带时间窗的车辆调度问题的灾变遗传算法.遗传算法作为一种高效的启发式算法被用于解决这类组合优化问题,但是该算法存在过早收敛、易陷入局部最优等缺陷.针对此问题,在搜索过程中采用灾变算子使遗传算法跳出局部最优,并针对车辆调度问题设计一种可以直接产生可行解的交叉算子,避免染色体交叉过程中产生不可行的子代.通过仿真算例验证了所提出的算法求解带时间窗的车辆调度问题的有效性;通过与标准遗传算法、改进遗传算法和粒子群算法的比较,进一步验证了灾变遗传算法在优化性能以及算法鲁棒性方面的优势.     

基于改进遗传算法的有时间窗车辆调度问题研究*

在分析带有时间窗车辆调度问题的基础上,建立了车辆调度问题的数学模型,并构造了不同时间窗的惩罚函数.设计了针对车辆调度问题基于自然数编码的遗传算法,并改进了传统的交叉运算,避免优秀基因在交叉操作中被破坏,提高了遗传算法的寻优能力.最后,结合算例进行了仿真计算,分析了载重体积约束和时间窗约束对车辆调度的影响,验证了算法的有...     

基于滚动变时间窗的重组批处理机调度研究

针对具有等待时间限制和工件动态到达的重组批处理机调度问题,以拖延时间和最小为目标,提出基于滚动变时间窗的三层混合调度算法。该调度算法是应用滚动时域策略,将重组批处理机调度问题分解为许多变时间窗的子问题;每个子问题调度分三层执行：即产生触发并传递参数、重组批及排序、派工并更新参数。通过实时调度仿真平台和CPLEX平台进行实例验证,结果表明基于滚动变时间窗的三层混合调度算法能够在较短计算时间内获得满意优化解。     

Scheduling with conflicts: online and offline algorithms

We consider the following problem of scheduling with conflicts (swc): Find a minimum makespan schedule on identical machines where conflicting jobs cannot be scheduled concurrently. We study the problem when conflicts between jobs are modeled by general graphs. Our first main positive result is an exact algorithm for two machines and job sizes in {1,2}. For jobs sizes in {1,2,3}, we can obtain a -approximation, which improves on the -approximation that was previously known for this case. Our main negative result is that for jobs sizes in {1,2,3,4}, the problem is APX-hard. Our second contribution is the initiation of the study of an online model for swc, where we present the first results in this model. Specifically, we prove a lower bound of on the competitive ratio of any deterministic online algorithm for m machines and unit jobs, and an upper bound of 2 when the algorithm is not restricted computationally. For three machines we can show that an efficient greedy algorithm achieves this bound. For two machines we present a more complex algorithm that achieves a competitive ratio of when the number of jobs is known in advance to the algorithm.     

Flexible job-shop scheduling with parallel variable neighborhood search algorithm

Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. FJSP is NP-hard and mainly presents two difficulties. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on the machines. This paper proposes a parallel variable neighborhood search (PVNS) algorithm that solves the FJSP to minimize makespan time. Parallelization in this algorithm is based on the application of multiple independent searches increasing the exploration in the search space. The proposed PVNS uses various neighborhood structures which carry the responsibility of making changes in assignment and sequencing of operations for generating neighboring solutions. The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the FJSP.     

Single machine scheduling problems with resource dependent release times

We consider two single machine scheduling problems with resource dependent release times that can be controlled by a non-increasing convex resource consumption function. In the first problem, the objective is to minimize the total resource consumption with a constraint on the sum of job completion times. We show that a recognition version of the problem is NP-complete. In the second problem, the objective is to minimize the weighted total resource consumption and sum of job completion times with an initial release time greater than the total processing times. We provide some optimality conditions and show that the problem is polynomially solvable.     

求解VSPSTW问题的混合差分演化算法

在描述带软时间窗车辆调度问题数学模型基础上,提出将模拟退火算法与差分演化算法相结合的混合优化算法求解该问题。该算法利用了模拟退火算法具有的较强局部搜索能力和差分演化算法的强全局搜索能力,克服了差分演化算法的“早期收敛”问题。实验结果表明,该算法比单一的差分演化算法计算效率高,收敛速度快,计算结果也比较稳定,是解决车辆调度问题的有效方法。     

Converging to periodic schedules for cyclic scheduling problems with resources and deadlines

Cyclic scheduling has been widely studied because of the importance of applications in manufacturing systems and in computer science. For this class of problems, a finite set of tasks with precedence relations and resource constraints must be executed repetitively while maximizing the throughput. Many applications also require that execution schedules be periodic i.e. the execution of each task is repeated with a fixed global period w.The present paper develops a new method to build periodic schedules with cumulative resource constraints, periodic release dates and deadlines. The main idea is to fix the period w, to unwind the cyclic scheduling problem for some number of iterations, and to add precedence relations so that the minimum time lag between two successive executions of any task equals w. Then, using any usual (not cyclic) scheduling algorithm to compute task starting times for the unwound problem, we prove that either the method converges to a periodic schedule of period w or it fails to compute a schedule. A non-polynomial upper bound on the number of iterations to unwind in order to guarantee that cyclic precedence relations and resource constraints are fulfilled is also provided. This method is successfully applied to a real-life problem, namely the software pipelining of inner loops on an embedded VLIW processor core by using a Graham list scheduling algorithm.