Xunfei Jiang

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This research develops an advanced two-phase MapReduce solution that is able to efficiently address skyline queries on large datasets. Unlike existing parallel skyline approaches, our scheme considers data partitioning, filtering, and parallel skyline evaluation as a holistic query process. In particular, we apply filtering techniques and angle-based(More)
Most researches of Solid State Drives (SSDs) architectures rely on Flash Translation Layer (FTL) algorithms and wear-leveling; however, internal parallelism in Solid State Drives has not been well explored. In this research, we proposed a new strategy to improve SSD write performance by enhancing internal parallelism inside SSDs. A SDRAM buffer is added in(More)
Recognizing that power and cooling cost for data centers are increasing, we address in this study the thermal impact of storage systems. In the first phase of this work, we generate the thermal profile of a storage server containing three hard disks. The profiling results show that disks have comparable thermal impacts as processing and networking elements(More)
Numerous geographic information system applications need to retrieve spatial objects which bear user specified keywords close to a given location. In this research, we present efficient approaches to answer spatial keyword queries on spatial networks. In particular, we formally introduce definitions of Spatial Keyword k Nearest Neighbor (SKkNN) and Spatial(More)
In this paper, we present an informed prefetching technique called IPODS that makes use of application-disclosed access patterns to prefetch hinted blocks in distributed multi-level storage systems. We develop a prefetching pipeline in IPODS, where an informed prefetching process is divided into a set of independent prefetching steps among multiple storage(More)
In this paper, we present an energy-aware informed prefetching technique called Eco-Storage that makes use of the application-disclosed access patterns to group the informed prefetching process in a hybrid storage system (e.g., hard disk drive and solid state disks). Since the SSDs are more energy efficient than HDDs, aggressive prefetching for the data in(More)
—An explosive increment of data and a variety of data analysis make it indispensable to lower power and cooling costs of cloud datacenters. To address this issue, we investigate the thermal impact of I/O access patterns on data storage systems. Firstly, we conduct some preliminary experiments to study the thermal behavior of a data storage node. The(More)
In this demonstration, we present a visualization system offering two advanced solutions that can efficiently address a novel multi-criteria optimal location query by using Overlapping Voronoi Diagrams (OVDs). Our system not only displays an example that applies the advanced solutions to a practical optimal location query, but also visualizes the process of(More)
There is a lack of thermal models for storage clusters; most existing thermal models do not take into account the utilization of hard drives (HDDs) and solid state disks (SSDs). To address this problem, we build a thermal model for hybrid storage clusters that are comprised of HDDs and SSDs. We start this study by generating the thermal profiles of hard(More)
This paper presents a novel Predictive EnergyAware Management (PEAM) system that is able to reduce the energy costs of storage systems by appropriately selecting data transmission methods. In particular, we evaluate the energy costs of three methods (1. transfer data without archiving and compression; 2. archive and transfer data; 3. compress and transfer(More)