一类带有deadline约束的作业车间提前/拖期调度问题研究

针对带有零件deadline时间约束的一类作业车间提前/拖期调度问题,设计了一种改进型遗传算法(EGA)。EGA算法采用拖期优先的调度策略,将原有的非正规性能指标的E/T调度问题转化为拖期子问题、修复子问题和提前子问题,以此来降低E/T调度问题的求解复杂度。采用基于工序的编码方法,在染色体解码过程中,分别采用了主动解码、染色体修复和逆向重调度三阶段的解码操作,以期实现在满足零件deadline约束的前提下尽可能降低提前/拖期惩罚总成本。180个调度测试用例仿真结果表明,EGA算法在解决问题数、寻优能力、调度结果的均衡性等方面具有一定的优势。

面向Deadline约束的网格QoS任务调度

根据调度算法中对QoS的匹配需求,提出了一种基于QoS优先级分组的网格任务调度思想,并且在此基础上引入任务的Deadline约束,综合考虑任务的可接受率以及系统的makespan性能,提出一种面向Dead-line约束的网格任务调度策略QD-Sufferage.实验表明,该算法在makespan、吞吐率等方面的综合性能较以往的算法有较大的提高.

面向Deadline的云代理资源预留策略

由于传统的代理模式在应对短期用户大量到达高峰和实例利用率上存在优化空间,为节约资源和节省开支,提出了一种新的面向Deadline的云服务代理构想,利用中间件技术整合用户请求和实例资源。分析发现,面向Deadline的云代理能有效平滑需求高峰,并大幅度提高实例利用率。针对用户deadline相同与不同2种模式,分别设计了贪心算法和EDF平移算法,实现了高峰平滑和利用率提升,降低了开支。实验表明,相比于未采用云代理,面向Deadline云服务代理处理大量用户请求时能减少50%以上的费用,具有良好的应用前景。

Is Minimizing Flow Completion Time the Optimal Way in Meeting Flow's Deadline in Datacenter Networks

In modern datacenters, the most common method to solve the network latency problem is to minimize flow completion time during the transmission process. Following the soft real-time nature, the optimization of transport latency is relaxed to meet a flow's deadline in deadline-sensitive services. However, none of existing deadline-sensitive protocols consider deadline as a constraint condition of transmission.They can only simplify the objective of meeting a flow's deadline as a deadline-aware mechanism by assigning a higher priority for tight-deadline constrained flows to finish the transmission as soon as possible, which results in an unsatisfactory effect in the condition of high fan-in degree. It drives us to take a step back and rethink whether minimizing flow completion time is the optimal way in meeting flow's deadline. In this paper, we focus on the design of a soft real-time transport protocol with deadline constraint in datacenters and present a flow-based deadline scheduling scheme for datacenter networks(FBDS).FBDS makes the unilateral deadline-aware flow transmission with priority transform into a compound centralized single-machine deadlinebased flow scheduling decision. In addition, FBDS blocks the flow sets and postpones some flows with extra time until their deadlines to make room for the new arriving flows in order to improve the deadline meeting rate. Our simulation resultson flow completion time and deadline meeting rate reveal the potential of FBDS in terms of a considerable deadline-sensitive transport protocol for deadline-sensitive interactive services.

A novel resource co-allocation model with constraints to budget and deadline in computational grid

To address the issue of resource co-allocation with constraints to budget and deadline in grid environments, a novel co-allocation model based on virtual resource agent was proposed. The model optimized resources deployment and price scheme through a three-side co-allocation mechanism, and applied queuing system to model the work of grid resources for providing quantitative deadline guarantees for grid applications. The validity and solutions of the model were presented theoretically. Extensive simulations were conducted to examine the effectiveness and the performance of the model by comparing with other co-allocation policies in terms of deadline violation rate, resource benefit and resource utilization. Experimental results show that compared with the three typical co-allocation policies, the proposed model can reduce the deadline violation rate to about 3.5% for the grid applications with constraints to budget and deadline. Also, the system benefits can be increased by about 30% compared with the those widely-used co-allocation policies.

Online scheduling of jobs with kind release times and deadlines on a single machine

This paper studies online scheduling of jobs with kind release times on a single machine. Here "kind release time" means that in online setting, no jobs can be released when the machine is busy. Each job J has a kind release time r(J) ≥ 0, a processing time p(J) > 0 and a deadline d(J) > 0. The goal is to determine a schedule which maximizes total processing time( p(J)E(J)) or total number( E(J)) of the accepted jobs. For the first objective function p(J)E(J), we first present a lower bound 2(1/2), and then provide an online algorithm LEJ with a competitive ratio of 3. This is the first deterministic algorithm for the problem with a constant competitive ratio. When p(J) ∈ {1, k}, k > 1 is a real number, we first present a lower bound min{(1 + k)/k, 2 k/(1 + k)}, and then we show that LEJ has a competitive ratio of1 + k/k. In particular, when all the k length jobs have tight deadlines, we first present a lower bound max{4/(2 + k), 1}(for p(J)E(J)) and 4/3(for E(J)). Then we prove that LEJ is k/k-competitive for p(J)E(J) and we provide an online algorithm H with a competitive ratio of 2 k/( k + 1) for the second objective function E(J).

Two-stage optimization model for renewing warranty considering warranty deadline

Renewing warranty can provide customers with better service,and thus help manufacturers to gain market opportunities.In engineering practice,the cost for replacement is usually higher than the cost for maintenance,hence manufacturers often face huge challenge to reduce the warranty service cost.With consideration of the warranty deadline,we propose a two-stage optimization model for renewing warranty.In the first stage,a renewing warranty with deadline(RWD)policy is implemented,where the deadline represents the cumulative uptime threshold.When the cumulative uptime exceeds the deadline,the product will be minimally repaired and kept to the residual warranty period.When RWD is expired,the replacement warranty with limited repairs(RWLR)policy is applied.Under the free replacement and pro-rata warranty policy,the corresponding two-stage cost optimization model is established from the manufacturer’s perspective,the aim is to minimize the cost rate and obtain the optimal warranty period.A numerical example is provided to illustrate the validity of the proposed model,and the sensitivity analysis is also carried out.

Priority probability deceleration deadline-aware TCP

In modern data centers, because of the deadline- agnostic congestion control in transmission control protocol （TCP）, many deadline-sensitive flows can not finish before their deadlines. Therefore, providing a higher deadline meeting ratio becomes a critical challenge in the typical online data intensive （OLDI） ap- plications of data center networks （DCNs）. However, a problem named as priority synchronization is found in this paper, which de- creases the deadline meeting ratio badly. To solve this problem, we propose a priority probability deceleration （P2D） deadline-aware TCP. By using the novel probabilistic deceleration, p2D prevents the priority synchronization problem. Simulation results show that P2D increases the deadline meeting ratio by 20% compared with D2TCP.

玻璃行业能否跑赢deadline?

和火电厂、钢铁厂一样,因大气污染物排放量大,平板玻璃厂内那高耸的烟囱,总是牵动人心、屡遭诟病。近年来,面对玻璃行业经济下行压力,不少企业使用低质能源以次充好,同时为了避免二次能源回收利用及烟气排放治理的费用,废气偷排行为时有发生,平板玻璃企业成了名副其实的＂藏污纳垢＂之地。

Deadline-Constrained and Cost-Effective Multi-Workflow Scheduling with Uncertainty in Cloud Control Systems

In cloud control systems,generating an efficient and economical workflow scheduling strategy for deadline-constrained workflow applications,especially in uncertain multi-workflow dynamic scheduling processes,is a crucial challenge.To optimize the total cost of workflow scheduling,the authors propose a cost-driven heuristic scheduling algorithm F-MWSA which consists of two phases:Fuzzy deadline distribution and fuzzy task scheduling.In the fuzzy deadline distribution phase,a new workflow deadline distribution strategy with fuzziness is designed to obtain the sub-deadline constraint of each task.The fuzzy task scheduling phase focuses on a cost-effective strategy to assign tasks to cloud resources,reducing multi-workflow scheduling costs.Performance evaluations on five real-world workflows demonstrate that the proposed F-MWSA outperforms the baseline policy in terms of total cost,success ratio,resource utilization,and makespan.

Insider Trading with a Random Deadline under Partial Observations:Maximal Principle Method

For a revised model of Caldentey and Stacchetti(Econometrica,2010)in continuous-time insider trading with a random deadline which allows market makers to observe some information on a risky asset,a closed form of its market equilibrium consisting of optimal insider trading intensity and market liquidity is obtained by maximum principle method.It shows that in the equilibrium,(i)as time goes by,the optimal insider trading intensity is exponentially increasing even up to infinity while both the market liquidity and the residual information are exponentially decreasing even down to zero;(ii)the more accurate information observed by market makers,the stronger optimal insider trading intensity is such that the total expect profit of the insider is decreasing even go to zero while both the market liquidity and the residual information are decreasing;(iii)the longer the mean of random time,the weaker the optimal insider trading intensity is while the more both the residual information and the expected profit are,but there is a threshold of trading time,half of the mean of the random time,such that if and only if after it the market liquidity is increasing with the mean of random time increasing.

ORDER SCHEDULING WITH CONTROLLABLE PROCESSING TIMES, COMMON DUE DATE AND THE PROCESSING DEADLINE

Due date quotation and scheduling are important tools to match demand with production capacity in the MTO （make-to-order） environment. We consider an order scheduling problem faced by a manufacturing f＇trm operating in an MTO environment, where the firm needs to quote a common due date for the customers, and simultaneously control the processing times of customer orders （by allocating extra resources to process the orders） so as to complete the orders before a given deadline. The objective is to minimize the total costs of earliness, tardiness, due date assignment and extra resource consumption. We show the problem is NP-hard, even if the cost weights for controlling the order processing times are identical. We identify several polynomially solvable cases of the problem, and develop a branch and bound algorithm and three Tabu search algorithms to solve the general problem. We then conduct computational experiments to evaluate the performance of the three Tabu-search algorithms and show that they are generally effective in terms of solution quality.

Meeting deadlines for approximation processing in MapReduce environments

To provide timely results for big data analytics, it is crucial to satisfy deadline requirements for MapReduce jobs in today＇s production environments. Much effort has been devoted to the problem of meeting deadlines, and typically there exist two kinds of solutions. The first is to allocate appropriate resources to complete the entire job before the specified time limit, where missed deadlines result because of tight deadline constraints or lack of resources; the second is to run a pre-constructed sample based on deadline constraints, which can satisfy the time requirement but fail to maximize the volumes of processed data. In this paper, we propose a deadline-oriented task scheduling approach, named ＇Dart＇, to address the above problem. Given a specified deadline and restricted resources, Dart uses an iterative estimation method, which is based on both historical data and job running status to precisely estimate the real-time job completion time. Based on the estimated time, Dart uses an approach-revise algorithm to make dynamic scheduling decisions for meeting deadlines while maximizing the amount of processed data and mitigating stragglers. Dart also efficiently handles task failures and data skew, protecting its performance from being harmed. We have validated our approach using workloads from OpenCloud and Facebook on a cluster of 64 virtual machines. The results show that Dart can not only effectively meet the deadline but also process near-maximum volumes of data even with tight deadlines and limited resources.

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 work-flow 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.Work-flow 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 called MPCP.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 representa-tive 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.

An Intuitive Formal Prooffor Deadline Driven Scheduler

This paper presents another formal proof for the correctness of the Deadline Driven Scheduler (DDS). This proof is given in terms of Duration Calculus which provides abstraction for random preemption of processor. Compared with other approaches, this proof relies on many intuitive facts. Therefore this proof is more intuitive, while it is still formal.

SAMES： deadline-constraint scheduling in MapReduce

MapReduce is a popular parallel data-processing system, and task scheduling is one of the kernel techniques in MapReduce. In many applications, users have requirements that their MapReduce jobs should be completed before specific deadlines. Hence, in this paper, a novel scheduling algorithm based on the most effective sequence （SAMES） is proposed for deadline-constraint jobs in MapReduce. First, according to the characteristics of MapReduce, we propose a novel sequence-based execution strategy for MapReduce jobs and a new concept, the effective sequence （ES）. Then, we design some efficient approaches for finding ESes and choose the most effective sequence （MES） for job execution. We also propose methods for MES-updates and exception handling. Finally, we verify the effectiveness of SAMES through experiments. The experimental results show that SAMES is an efficient scheduling algorithm for deadline-constraint jobs in MapReduce.

Deadline based scheduling for data-intensive applications in clouds

Cloud computing emerges as a new computing pattern that can provide elastic services for any users around the world. It provides good chances to solve large scale scientific problems with fewer efforts. Application deployment remains an important issue in clouds. Appropriate scheduling mechanisms can shorten the total completion time of an application and therefore improve the quality of service（QoS） for cloud users. Unlike current scheduling algorithms which mostly focus on single task allocation, we propose a deadline based scheduling approach for data-intensive applications in clouds. It does not simply consider the total completion time of an application as the sum of all its subtasks＇ completion time. Not only the computation capacity of virtual machine（VM） is considered, but also the communication delay and data access latencies are taken into account. Simulations show that our proposed approach has a decided advantage over the two other algorithms.

古今含义大不同的deadline(英文)

Reporters and authors know deadlines must be met for their story tobe told.But today’s deadlines are not the matter of"life and death"theyonce were.In the1860s,at the Confederate prison camp at Andersonville,Georgia,the expression"deadline"was coined(杜撰)to represent a linedrawn approximately17feet from the camp’s fence.Officers on duty hadorders to shoot any prisoner who crossed the deadline.

今天也是被deadline支配的一天!

在瞬息万变、信息爆炸的时代里,值得公众追逐的除了新鲜的话题和事件,还有它背后所蕴含的真知。培根说:'思想是行动的先导。'善于思考的人,才能在这个社会辨明真相,少走弯路。201 9年',思想实验室'正式开启,郁喆隽——复旦大学哲学学院副教授、德国莱比锡大学哲学博士,一位集专业深度与大众普及于一身的'哲学小王子'将带领我们一起化解困惑,学会深度思考,建立更成熟的认知体系。

Efficient-Cost Task Offloading Scheme in Fog-Internet of Vehicle Networks

Fog computing became a traditional OffLad Destination(OLD)to compute the offloaded tasks of the Internet of Vehicles(IoV).Nevertheless,the limited computing resources of the fog node leads to re-offload these tasks to the neighboring fog nodes or the cloud.Thus,the IoV will incur additional offloading costs.In this paper,we propose a new offloading scheme by utilizing RoadSide Parked Vehicles(RSPV)as an alternative OLD for IoV.The idle computing resources of the RSPVs can compute large tasks with low offloading costs compared with fog nodes and the cloud.Finally,a performance evaluation of the proposed scheme has been presented and discussed with other benchmark offloading schemes.