Multi-crane scheduling in steel coil warehouse

This paper considers a multi-crane scheduling problem commonly encountered in real warehouse operations in steel enterprises. A given set of coils are to be retrieved to their designated places. If a required coil is in upper level or in lower level without being blocked, it can be picked up directly to its designated place; else, the blocking coils need to be picked up to another position first. Our problem aims to determine which positions for blocking coils to be shuffled to, and a joint transportation sequence of required coils to the designated places and blocking coils to be shuffled as soon as possible by multi-crane carrying these coils simultaneously.For describe the studied problem clearly, we first formulate it as a mixed integer linear programming (MILP) model. Then some feasible and optimal properties for assigning cranes to avoid collisions in possible positions are identified. Since a special case of our problem is demonstrated strongly NP-hard, we further propose a heuristic algorithm. A lower bound to the problem is developed and the performance of the heuristic algorithm is analyzed from the worst case point of view. Finally, computational experiments are used to evaluate the average performance of our proposed methods and the results show that the proposed heuristic algorithm is capable of generating good quality solutions.     

StreamTune: dynamic resource scheduling approach for workload skew in video data center

Video surveillance applications need video data center to provide elastic virtual machine (VM) provisioning. However, the workloads of the VMs are hardly to be predicted for online video surveillance service. The unknown arrival workloads easily lead to workload skew among VMs. In this paper, we study how to balance the workload skew on online video surveillance system. First, we design the system framework for online surveillance service which consists of video capturing and analysis tasks. Second, we propose StreamTune, an online resource scheduling approach for workload balancing, to deal with irregular video analysis workload with the minimum number of VMs. We aim at timely balancing the workload skew on video analyzers without depending on any workload prediction method. Furthermore, we evaluate the performance of the proposed approach using a traffic surveillance application. The experimental results show that our approach is well adaptive to the variation of workload and achieves workload balance with less VMs.     

Cognitive workload analysis in rail signalling environments

When working in safety critical environments, such as railway signalling, technologies are often used to assist the operator in order to make safe decisions as well as to increase efficiency. Keeping mental workload at manageable levels is one of the most critical factors to ensure that tasks can be completed in a safe and effective manner. Numerous methods have been used to measure mental workload, but none of these have managed to show the use of distinct resource pools. This paper therefore measures the levels of usage of different cognitive processes when typical tasks are carried out in two railway signalling environments. To that end, the Multiple Resource Questionnaire is used. The aims of this paper are twofold: First, it establishes the value of the Multiple Resource Questionnaire as a technique in human factors. Second, it investigates and compares the cognitive resources required to carry out the railway signalling task in two different signalling environments.     

Proactive workload management in dynamic virtualized environments

Recently, with the improvement of Cloud systems technologies and the essential advantages they can provide such as availability, scalability, and costs saving; massive domains in the IT industry are directing their business to the Cloud. To fit the computing demands of this trend along with nowadays fluky applications (e.g. social networks, media contents), Cloud systems require rapid resource changes. As a result, the workload management in a virtualized environment becomes a complex task. In this paper we propose a new proactive workload management model for virtualized resources to inspect the workload behavior of the running Virtual Machines, and to assent an appropriate scheduling and resource consolidation schema in order to improve the system efficiency, utilization, and throughput. We have carried out our model by modifying Xen Cloud Platform, then we tested the model performance through different representative benchmarks. The results show that the Proactive model can decrease the average response time remarkably.     

On the performance of real-time multi-item request scheduling in data broadcast environments

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and capability to handle dynamic data access patterns. Previous studies on time-critical on-demand data broadcast were conducted under the assumption that each client requests only one data item at a time. With the rapid growth of time-critical information dissemination services in emerging applications, there is an increasing need for systems to support efficient processing of real-time multi-item requests. Little work, however, has been done. In this paper, we study the behavior of six representative single-item request based scheduling algorithms in time-critical multi-item request environments. The results show that the performance of all algorithms deteriorates when dealing with multi-item requests. We observe that data popularity, which is an effective factor to save bandwidth and improve performance in scheduling single-item requests, becomes a hindrance to performance in multi-item request environments. Most multi-item requests scheduled by these algorithms suffer from a starvation problem, which is the root of performance deterioration. Based on our analysis, a novel algorithm that considers both request popularity and request timing requirement is proposed. The performance results of our simulation study show that the proposed algorithm is superior to other classical algorithms under a variety of circumstances.     

Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments

Data Grid integrates graphically distributed resources for solving data intensive scientific applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Scheduling is a traditional problem in parallel and distributed system. However, due to special issues and goals of Grid, traditional approach is not effective in this environment any more. Therefore, it is necessary to propose methods specialized for this kind of parallel and distributed system. Another solution is to use a data replication strategy to create multiple copies of files and store them in convenient locations to shorten file access times. To utilize the above two concepts, in this paper we develop a job scheduling policy, called hierarchical job scheduling strategy （HJSS）, and a dynamic data replication strategy, called advanced dynamic hierarchical replication strategy （ADHRS）, to improve the data access efficiencies in a hierarchical Data Grid. HJSS uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers network characteristics, number of jobs waiting in queue, file locations, and disk read speed of storage drive at data sources. Moreover, due to the limited storage capacity, a good replica replacement algorithm is needed. We present a novel replacement strategy which deletes files in two steps when free space is not enough for the new replica： first, it deletes those files with minimum time for transferring. Second, if space is still insufficient then it considers the last time the replica was requested, number of access, size of replica and file transfer time. The simulation results show that our proposed algorithm has better performance in comparison with other algorithms in terms of job execution time, number of intercommunications, number of replications, hit ratio, computing resource usage and storage usage.     

Safeguarding the data warehouse

Data warehousing and the Internet are regarded as the two most important changes to the information systems landscape in the past few years. Although they are agents of powerful change, their deployment presents many challenges and opportunities. Here is outlined one set of challenges that has emerged with the convergence of the Internet and data warehousing security.     

On workload characterization of relational database environments

A relational database workload analyzer (REDWAR) is developed to characterize the workload in a DB2 environment. This is applied to study a production DB2 system where a structured query language (SQL) trace for a two-hour interval and an image copy of the database catalog were obtained. The results of the workload study are summarized. The structure and complexity of SQL statements, the makeup and run-time behavior of transactions/queries, and the composition of relations and views are discussed. The results obtained provide the important information needed to build a benchmark workload to evaluate the alternative design tradeoffs of database systems     

Evaluating data warehouse toolkits

Historically, building a data warehouse has been a risky proposition. According to the Gartner Group and similar sources, the failure rate for data warehouse projects runs as high as 60 percent. The dilemma facing businesses today is this: although implementing an enterprise-wide database is risky, an organization that does so gains a significant business advantage over competitors who do not have data warehouse capability or who continue to rely on localized, group-specific data marts. The author discusses the evaluation of data warehouse toolkits and considers critical toolkit components     

数据仓库中重复记录清理算法研究

针对重复记录清理中的"排序、识别、合并"算法存在的问题进行了改进.改进后的重复记录清理算法在保证记录匹配率的情况下有效地提高了记录排序的效率;在重复记录识别时,考虑了匹配字段的文字数量、在2个字段中出现的频率、在记录中各字段的重要性(权重)、中文字段的语义和语义重点偏后等5个因素;合并重复记录时采用了聚类和实用算法并用的策略,有效地提高了数据仓库中重复记录清理算法的准确性和健壮性.     

Intelligent scheduling processes and controlling workload in computing systems

Methods and algorithms of adaptive scheduling processes and workload control in computing systems are considered.     

A hybrid scheduling scheme for data broadcast over a single channel in mobile environments

Various broadcast schemes have been proposed to reduce the data access time of mobile clients. However, they are based either on the mobile users’ data access frequencies or on the semantic relations of the data. In this paper, we present a hybrid data broadcast scheme based both on semantic relationships and access probabilities. The broadcast scheme we propose generates a broadcast sequence according to the semantic relationships and replicates popular data items several times according to the data access probabilities. The efficiency of our broadcast scheme is shown via performance evaluations.     

数据仓库的样本模型

提出了数据仓库即样本库的观点,给出了用于数据仓库的概念设计样本模型(SM)。SM以事件为中心,每类事件即是数据仓库的一个主题,通过对参与事件的实体及其上关系(层次、分类和组合)的描述定义事件的表示空间。     

构建数据仓库实例

介绍了数据仓库技术的概念和内容,以数据仓库D系统的构建为实例,对建立数据仓库系统的理论和实践上的研究.分析了W企业的实际情况,设计出了适合企业需求的系统整体结构.对确定系统开发模式,以及设计数据模型的方法和过程加以描述.以数据仓库系统建设中的数据的提取、加载和联机分析处理（OLAP）为重点,对数据仓库的设计开发过程进行了详细的阐述.     

Cooperative power-aware scheduling in grid computing environments

Energy usage and its associated costs have taken on a new level of significance in recent years. Globally, energy costs that include the cooling of server rooms are now comparable to hardware costs, and these costs are on the increase with the rising cost of energy. As a result, there are efforts worldwide to design more efficient scheduling algorithms. Such scheduling algorithm for grids is further complicated by the fact that the different sites in a grid system are likely to have different ownerships. As such, it is not enough to simply minimize the total energy usage in the grid; instead one needs to simultaneously minimize energy usage between all the different providers in the grid. Apart from the multitude of ownerships of the different sites, a grid differs from traditional high performance computing systems in the heterogeneity of the computing nodes as well as the communication links that connect the different nodes together. In this paper, we propose a cooperative, power-aware game theoretic solution to the job scheduling problem in grids. We discuss our cooperative game model and present the structure of the Nash Bargaining Solution. Our proposed scheduling scheme maintains a specified Quality of Service (QoS) level and minimizes energy usage between all the providers simultaneously; energy usage is kept at a level that is sufficient to maintain the desired QoS level. Further, the proposed algorithm is fair to all users, and has robust performance against inaccuracies in performance prediction information.     

数据仓库技术在呼叫中心中的应用

针对现有的呼叫中心中存在历史数据量大、分析和处理数据能力不足、导致企业决策缺乏数据支持的问题,在对呼叫中心和数据仓库技术研究的基础上,结合呼叫中心的呼叫管理系统的特点,设计了呼叫中心数据仓库的体系结构,详细论述了对该数据仓库的体系结构、逻辑模型、物理模型以及联机分析处理（OLAP）系统的设计和具体实现方案。     

Fault-aware grid scheduling using performance prediction by workload modeling

Computational grids hold great promise in utilizing geographically separated heterogeneous resources to solve large-scale complex problems. However, they suffer from a number of major technical hurdles, including distributed resource management and effective job scheduling. The main focus of this work is devoted on online scheduling of real time applications in distributed environments such as grids. Specifically, we are interested in applications with several independent tasks, each task with a prespecified lifecycle called deadline. Here, our goal is to schedule applications within an optimum overall time considering the specified deadlines. To achieve this, the resource performance prediction based on workload modeling and with the help of queuing techniques is employed. Afterward, a mathematical neural model is used to schedule the subtasks of the application. The main contributions of this work is to incorporate the impatiency factor as well as resource fault in performance modeling of nondedicated distributed systems, and also presenting an efficient and fast parallel scheduling algorithm under time constraint and heterogeneous resources. The proposed model is appropriate for implementation on parallel machines and in O(1) time. The new model was implemented on GridSim toolkit and under various conditions and with different parameters to evaluate the performance of scheduling algorithm. Simulation outcomes have shown that approximately in 87.8% of cases, our model schedules the tasks in such a way that all constraints are satisfied.

数据仓库技术在工程项目中的实现

简单介绍了数据仓库的基本概念和特点,结合华北空管局流量管理系统,对数据仓库的体系结构作了剖析,并在总结 系统开发时碰到的技术问题的基础上,对一些问题提出了解决方案。     

Conceptual scheduling model and optimized release scheduling for agile environments

Context

Release scheduling deals with the selection and assignment of deliverable features to a sequence of consecutive product deliveries while several constraints are fulfilled. Although agile software development represents a major approach to software engineering, there is no well-established conceptual definition and sound methodological support of agile release scheduling.

Objective

To propose a solution, we present, (1) a conceptual model for agile scheduling, and (2) a novel multiple knapsack-based optimization model with (3) a branch-and-bound optimization algorithm for agile release scheduling.

Method

To evaluate our model simulations were carried out seven real life and several generated data sets.

Results

The developed algorithm strives to prevent resource overload and resource underload, and mitigates risks of delivery slippage.

Conclusion

The results of the experiment suggest that this approach can provide optimized semi-automatic release schedule generations and more informed and established decisions utilizing what-if-analysis on the fly to tailor the best schedule for the specific project context.