On‐demand data co‐allocation with user‐level cache for grids

Conventional remote data access middlewares usually provide client applications with either a pre‐staging scheme or an on‐demand access scheme to fetch data. The pre‐staging scheme uses parallel downloads to fetch a completed input file from multiple data sources, even when only a tiny file fragment is required. Such a transfer scheme consumes unnecessary data transmission time and storage space. In contrast, the on‐demand scheme downloads only the required data blocks from a single data source and does not fully utilize the downstream bandwidth of the computing nodes. This paper presents a middleware called ‘Spigot’ that facilitates legacy (grid‐unaware) applications to transparently access remote data by using native I/O function calls. Spigot uses the on‐demand concept to avoid unnecessary data transfer and adopts a co‐allocation download algorithm to improve the data transfer performance. Moreover, it uses the pre‐fetching strategy to reduce the data waiting time by overlapping data acquisition and data processing. It also provides the client application with its own user‐level cache, which is advantageous since a larger cache space is available in comparison with the kernel‐level cache. Further, it is easy to maintain data consistency between Spigot nodes. The experimental results indicate that Spigot achieves superior performance in reducing the data waiting time than the pre‐staging and the on‐demand access schemes. Copyright © 2010 John Wiley & Sons, Ltd.     

虚拟变量法在用电量预测中的应用

用电量预测是开展电力需求侧管理的一项基础性工作。在实际预测中,许多经济变量是可以定量度量的,而某些影响经济变量的因素却无法量化,定性因素对模型预测有很大的影响。采用虚拟变量方法,将定性因素定量化,建立含有多个虚拟变量的回归模型,在模型中反映这些因素的影响。将这一方法用于电量预测,可以有效提高模型的预测精度,取得理想的效果。