Multiple query scheduling for distributed semantic caches

In distributed query processing systems, load balancing plays an important role in maximizing system throughput. When queries can leverage cached intermediate results, improving the cache hit ratio becomes as important as load balancing in query scheduling, especially when dealing with computationally expensive queries. The scheduling policies must be designed to take into consideration the dynamic contents of the distributed caching infrastructure. In this paper, we propose and discuss several distributed query scheduling policies that directly consider the available cache contents by employing distributed multidimensional indexing structures and an exponential moving average approach to predicting cache contents. These approaches are shown to produce better query plans and faster query response times than traditional scheduling policies that do not predict dynamic contents in distributed caches. We experimentally demonstrate the utility of the scheduling policies using MQO, which is a distributed, Grid-enabled, multiple query processing middleware system we developed to optimize query processing for data analysis and visualization applications.     

Dynamic slack allocation algorithms for energy minimization on parallel machines

We explore novel algorithms for DVS (Dynamic Voltage Scaling) based energy minimization of DAG (Directed Acyclic Graph) based applications on parallel and distributed machines in dynamic environments. Static DVS algorithms for DAG execution use the estimated execution time. The estimated time in practice is overestimated or underestimated. Therefore, many tasks may be completed earlier or later than expected during the actual execution. For overestimation, the extra available slack can be added to future tasks so that energy requirements can be reduced. For underestimation, the increased time may cause the application to miss the deadline. Slack can be reduced for future tasks to reduce the possibility of not missing the deadline. In this paper, we present novel dynamic scheduling algorithms for reallocating the slack for future tasks to reduce energy and/or satisfy deadline constraints. Experimental results show that our algorithms are comparable to static algorithms applied at runtime in terms of energy minimization and deadline satisfaction, but require considerably smaller computational overhead.     

Partition oriented frame based fair scheduler

Proportionate fair schedulers provide an effective methodology for scheduling recurrent real-time tasks on multiprocessors. However, a drawback in these schedulers is that they ignore a task’s affinity towards the processor where it was executed last, causing frequent inter-processor task migrations which ultimately results in increased execution times. This paper presents Partition Oriented Frame Based Fair Scheduler (POFBFS), an efficient proportional fair scheduler for periodic firm and soft real-time tasks that ensures a bounded number of task migrations. Experimental results reveal that POFBFS can achieve 3 to 100 times reduction in the number of migrations suffered with respect to the General-ERfair algorithm (for a set of 25 to 100 tasks running on 2 to 8 processors) while simultaneously maintaining high fairness accuracy.     

Optimization of quality and operational costs through improved scheduling of harvest operations

The agriculture sector still lacks the tools and models to enhance the utilization of different resources. This paper addresses the vineyard harvesting problem in developing countries, with the objective of optimizing the wine quality and minimizing the operational costs. Heuristics were introduced to better assign the harvesting days to the different grape blocks that exist in the vineyard's field. The quality of the grapes was a key target as it can transform production from a pinnacle wine to a bulk one. We solved several numerical examples for verification and demonstrative purposes and found that our proposed approach finds solutions that significantly reduce the harvesting costs in the vineyard and considerably outperform Branch and Bound algorithm especially for large problems.     

作战行动建模资源库系统研究

该文分析了作战行动建模资源库的特点,确定了基于WEB的分布式作战行动建模资源库系统的体系结构和功能,选取了XML描述资源元数据信息,并提出了基于本体的智能搜索引擎。     

浅谈智能总机在油田专网中的应用前景

该文对智能总机的发展历程、系统结构、业务功能等方面做了详细的介绍。基于智能总机实现新业务方面的优势,提出了在油田专网中的应用,并对其前景进行了展望。     

基于带偏好自适应评价的公交线路选择问题研究

在智能交通系统中公汽线路选择是一个重要的问题。该文首先利用公汽的行驶线路进行深度优先搜索．得到从开始站点到结束站点所有可能的乘车线路。然后分别考虑乘车时间Time和乘车价格Price来评价这些乘车线路优劣,最终得到题目要求的最佳线路。为了能够综合考虑Time和Price,通过引入用户期望度p,提出了一种带偏好的自适应评价函数来对基于Time和Price的评价方法进行进一步的改进,这个带偏好的自适应评价函数合理表达了查询者的主观意向、乘车线路差异,从而可以满足不同查询者的不同需求。最后通过仿真实验对该文方法进行了有效性验证。     

无线网技术的区域家庭防盗报警系统

设计了一种新的区域家庭智能防盗报警系统,利用单片机控制技术、无线传感器网络技术和GSM移动通信技术,实现具有联网功能的区域智能防盗报警系统。无线防盗报警探头采用红外热释电传感器和振动传感器双重监测原理确认盗贼是否入侵,并以无线数据传输方式将报警信息传送给家庭报警主机,家庭报警主机再将报警信息通过短信的方式发送到用户手机、小区或地区报警主机上。报警信息发送及时,准确可靠。家庭报警主机可以设置无线防盗报警探头的数量和安装位置等信息,还可以设置报警用户电话、小区或地区报警中心电话。整个报警网络大小可以灵活配置,而且传感器采用无线方式布局,不需室内布线。     

大功率LED智能化照明控制系统设计

在绿色照明领域,既要体现节能,又要具备较高的光效,大功率LED驱动设计显得越来越重要。针对大功率LED的特性,研究设计了适用于不同参数指标的白光LED的驱动与控制系统。该系统以单片机C8051F040为核心,结合外围电路,用数字控制技术代替模拟电路控制,对大功率LED的恒流驱动电流进行高精度控制,以及对LED亮度进行自动调节控制,实现了多种大功率LED智能化照明。     

嵌入式设备的智能化电源管理系统

在分析传统嵌入式系统电源管理的基础上,主要从策略角度分析传统电源管理存在的弊端,提出智能化电源系统应具备的特点,并提出一套嵌入式设备智能化电源管理系统的完整方案。实践验证了此方案的高效性能。