Miyuki Nakano

Query execution for large relations on functional disk system

The second version of FDS-R (functional disk system with relational database engine), FDS-RII, which is designed to handle large relations efficiently, is discussed. On FDS-RII, the processing algorithm is selected at run time from two algorithms (nested loop algorithms, grace hash algorithm) by comparing their estimated I/O costs. The processing strategy is discussed in detail. The I/O cost formula is examined by measuring the execution time of a join query on the FDS-RII. With the expanded version of Wisconsin Benchmark, the performance of FDS-RII is measured. FDS-RII attained a high performance level for large relations as compared to other large database systems such as Gamma and Teradata. While FDS uses just one disk and three MC68020s, Teradata uses 40 disks and 20 AMPs and Gamma requires eight disks and 17 VAX 11/750s.<<ETX>>

Functional disk system for relational database

The major performance bottle neck in the current computer system is in the low-performance secondary system. The performance of the CPU has increased dramatically so far, about several orders of magnitude improvement has been achieved. On the other hand, that of the disk system has shown little advance since nineteen sixties. The von Neumann bottle neck between the CPU and the secondary storage subsystem has been much more enlarged.

Modeling I/O interference for data intensive distributed applications

Data intensive applications such as MapReduce can have large performance degradation from the effects of I/O interference when multiple processes access the same I/O resources simultaneously, particularly in the case of disks. It is necessary to understand this effect in order to improve resource allocation and utilization for these applications. In this paper, we propose a model for predicting the impact of I/O interference on MapReduce application performance. Our model takes basic parameters of the workload and hardware environment, and knowledge of the I/O behavior of the application to predict how I/O interference affects the scalability of an application. We compare the models predictions for several workloads (TeraSort, WordCount, PFP Growth and PageRank) against the actual behavior of those workloads in a real cluster environment, and confirm that our model can provide highly accurate predictions.

Application-aware power saving for online transaction processing using dynamic voltage and frequency scaling in a multicore environment

Power consumption in data centers has been growing remarkably recent years, and power saving of their servers is essential. For power saving of these servers, power saving of an online transaction processing (OLTP) systems, which are major applications in data centers, is important. The OLTP system consumes relatively large amount of power because it is often equipped with a lot of computing and storage resources. Its power saving is difficult because it is required to meet a service level agreement (SLA), and few power saving technologies have been proposed so far. In this paper, we proposed an application-aware power saving for OLTP in a multicore environment. Our proposed methodology aims to save power consumption of OLTP systems by dynamically scaling the operating frequency of processors based on response time observation. Response time is often an important metric of SLA. Application-aware power saving enables power saving in such systems subject to SLAs. In our experimental evaluations using industrial standard benchmark TPC-C and real server workloads, 7.6% of total power consumption was saved. This reduction corresponds to 1000kJ a day in a typical entry level server.

Parallel GRACE hash join on shared-everything multiprocessor: implementation and performance evaluation on symmetry S81

The authors implemented a parallel hash join algorithm on a Symmetry S81 shared-everything multiprocessor environment and evaluated the performance. They evaluated the input/output (I/O) performance on a multiple-disk environment, and showed linear performance increase of up to eight disks. The performance of the implemented join operation was examined on each phase, and the effect of parallel processing by the multiprocessor and the multiple disks was clarified. It was concluded from the experimental result that on such a shared-everything multiprocessor system parallelism could be easily exploited for the construction of high-performance relational database systems.<<ETX>>

A Mobile Robot for Fall Detection for Elderly-Care☆

Abstract In 2015, the population of people over the age of 65 is 25.0% in Japan. This means that Japan has already become a super-aging society. In such society, the number of elderly people living alone has been also increased. For such people, a fall accident is serious because it can lead to serious injury or death. Researches and services to monitor behaviours of such people have been proposed. For example, by monitoring the status of use of home appliances, something unusual happened to them can be predicted. However, such systems cannot recognize the detailed behaviours like fall. Surveillance cameras have been introduced only outside the house because of the privacy issues. In this paper, we propose a mobile robot to detect human fall and report it to their observers. The mobile robot consists of a household mobile robot (Yujin Robots Kobuki), a sensor (Microsofts Kinect), and a computer (PC) to detect a human and control the robot. For simplicity of the robot and accurate fall detection, the sensor is installed on the robot to follow the target harmoniously. Thus, the sensor can move around with the robot to minimize blind area. The results of our experiments show that improvement of up to 80% in fall detection rate compared to a conventional monitoring technique using position-fixed sensors. Finally, we discuss the capabilities and future works of the robot.

Early Experience and Evaluation of File Systems on SSD with Database Applications

Flash SSDs are being incorporated in many enterprise storage platforms recently. However, the characteristics of the flash SSD are quite different from that of hard disk. The IO strategies in the existing systems should be carefully evaluated. This paper provides an evaluation on the flash-based transaction processing system. Two file systems, traditional in-place update-based file system and log-structured file system, are selected as the representative of two write strategies. Usually, the log-structured file system is believed to play better on flash SSDs. Our experiment shows that the performance results of two database applications are diverse with two file systems on different flash SSDs. We analyze the performance in different configurations. Based on the analysis, we provide our experience on building the flash-based database system to better utilize the performance benefits of flash SSDs.

Performance evaluation of functional disk system with nonuniform data distribution

In this paper, we analyze the performance of a Functional Disk System with Relational database engine (FDS-RII) for a nonuniform data distribution. FDS-RII is a relational storage system, designed to accelerate relational algebraic operations, which employs a hash-based algorithm to process relational operations. Basically, in the hash-based algorithm, a relation is first partitioned into several clusters by a split function. Then each cluster is staged onto the main memory and, further, a hash function is applied to each cluster to perform a relational operation. Thus, the nonuniformity of split and hash functions is considered to be resulting from a nonuniform data distribution on the hash-based algorithm. We clarify the effect of nonuniformity of the hash and split functions on the join performance. It is possible to attenuate the effect of the hash function nonuniformity by increasing the number of processors and processing the buckets in parallel. Furthermore, in order to tackle the nonuniformity of split function, we introduce the Combined Hash Algorithm. This algorithm combines the Grace Hash Algorithm with the Nested Loop Algorithm in order to handle the overflown bucket efficiently. Using the Combined Hash Algorithm, we find that the execution time of the nonuniform data distribution is almost equal to that of the uniform data distribution. Thus we can get sufficiently high performance on FDS-RII also for nonuniformly distributed data.

Exploration on efficient similar sentences extraction

Measuring the semantic similarity between sentences is an essential issue for many applications, such as text summarization, Web page retrieval, question-answer model, image extraction, and so forth. A few studies have explored on this issue by several techniques, e.g., knowledge-based strategies, corpus-based strategies, hybrid strategies, etc. Most of these studies focus on how to improve the effectiveness of the problem. In this paper, we address the efficiency issue, i.e., for a given sentence collection, how to efficiently discover the top-k semantic similar sentences to a query. The previous methods cannot handle the big data efficiently, i.e., applying such strategies directly is time consuming because every candidate sentence needs to be tested. In this paper, we propose efficient strategies to tackle such problem based on a general framework. The basic idea is that for each similarity, we build a corresponding index in the preprocessing. Traversing these indices in the querying process can avoid to test many candidates, so as to improve the efficiency. Moreover, an optimal aggregation algorithm is introduced to assemble these similarities. Our framework is general enough that many similarity metrics can be incorporated, as will be discussed in the paper. We conduct extensive experimental evaluation on three real datasets to evaluate the efficiency of our proposal. In addition, we illustrate the trade-off between the effectiveness and efficiency. The experimental results demonstrate that the performance of our proposal outperforms the state-of-the-art techniques on efficiency while keeping the same high precision as them.

Performance analysis of parallel hash join algorithms on a distributed shared memory machine implementation and evaluation on HP exemplar SPP 1600

The distributed shared memory (DSM) architecture is considered to be one of the most likely parallel computing environment candidate for the near future because of its ease of system scalability and facilitation for parallel programming. However, a naive program based on shared memory execution on a DSM machine often deteriorates performance, because of the overhead involved for maintaining cache coherency particularly with frequent remote memory accesses. We show that careful buffer management of parallel join processing on DSM can produce considerable performance improvements in comparison with a naive implementation. We propose four buffer management strategies for parallel hash join processing on the DSM architecture and actually implement them on the HP Exemplar SPP 1600. The basic strategy is to begin with the hash join algorithm for the shared everything architecture and then to consider the memory locality of DSM by distributing the hash table and data pool buffers among the nodes. The results of four buffering strategies are analyzed in detail. Consequently, we can conclude that, in order to achieve high performance on a DSM machine, our buffer management strategy in which the memory access pattern is extracted and buffers are allocated in the local memory of nodes to minimize memory access cost is very efficient.