標(biāo)題: Titlebook: Big Data Benchmarks, Performance Optimization, and Emerging Hardware; 4th and 5th Workshop Jianfeng Zhan,Rui Han,Chuliang Weng Conference p [打印本頁] 作者: Deflated 時間: 2025-3-21 17:08
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書目名稱Big Data Benchmarks, Performance Optimization, and Emerging Hardware讀者反饋
書目名稱Big Data Benchmarks, Performance Optimization, and Emerging Hardware讀者反饋學(xué)科排名
作者: rectum 時間: 2025-3-21 21:36 作者: Chagrin 時間: 2025-3-22 00:35
MemTest: A Novel Benchmark for In-memory Database institutions have devoted more resources to develop several in-memory databases, which preload the data into memory for further processing. With the boom of in-memory databases, there emerges requirements to evaluate and compare the performance of these systems impartially and objectively. In this 作者: 偏狂癥 時間: 2025-3-22 08:14
DSIMBench: A Benchmark for Microarray Data Using Rtion approaches. Newer data storage systems, such as MySQL Cluster and HBase, have been proposed for R data storage; while the parallel computation frameworks, including MPI and MapReduce, have been applied to R computation. Thus, it is difficult to understand the whole analysis workflows for which 作者: Blood-Clot 時間: 2025-3-22 12:14 作者: CAND 時間: 2025-3-22 13:18 作者: Petechiae 時間: 2025-3-22 17:39 作者: GROSS 時間: 2025-3-22 21:23 作者: 值得 時間: 2025-3-23 02:47 作者: 引水渠 時間: 2025-3-23 09:31 作者: Shuttle 時間: 2025-3-23 13:23
Tuning Hadoop Map Slot Value Using CPU Metrica large number of companies who have adopted Hadoop for their business purposes. One of the configuration parameters that influences the resource allocation and thus the performance of a Hadoop application is map slot value (MSV). MSV determines the number of map tasks that run concurrently on a nod作者: Assault 時間: 2025-3-23 17:35
A Study of SQL-on-Hadoop Systems, providing SQL analysis functionality to the big data resided in HDFS becomes more and more important. Hive is a pioneer system that support SQL-like analysis to the data in HDFS. However, the performance of Hive is not satisfactory for many applications. This leads to the quick emergence of dozens作者: Entreaty 時間: 2025-3-23 20:24 作者: Emg827 時間: 2025-3-24 00:03 作者: Condescending 時間: 2025-3-24 04:16
Efficient HTTP Based I/O on Very Large Datasets for High Performance Computing with the Libdavix Libprotocols are highly optimized for high throughput on very large datasets, multi-streams, high availability, low latency and efficient parallel I/O. The purpose of this paper is to describe how we have adapted a generic protocol, the Hyper Text Transport Protocol (HTTP) to make it a competitive alte作者: 真 時間: 2025-3-24 09:56
DSIMBench: A Benchmark for Microarray Data Using Rthe tool kits are suited for a specific environment. In this paper we propose DSIMBench, a benchmark containing two classic microarray analysis functions with eight different parallel R workflows, and evaluate the benchmark in the IC Cloud testbed platform.作者: 反感 時間: 2025-3-24 13:25 作者: 任意 時間: 2025-3-24 16:03 作者: 大方一點 時間: 2025-3-24 20:50
https://doi.org/10.1007/978-94-017-6798-9Sort, Kmeans and PageRank. We conduct detailed deep analysis of their I/O characteristics, including disk read/write bandwidth, I/O devices utilization, average waiting time of I/O requests, and average size of I/O requests, which act as a guide to design highperformance, low-power and cost-aware big data storage systems.作者: 不足的東西 時間: 2025-3-25 02:16
Generalizations of Dirichlet Convolution,7?% and 50?% speedups compared with those of Hadoop and Spark, respectively. Most of the benefits come from the high-efficiency communication mechanisms in DataMPI. We also notice that the resource (CPU, memory, disk and network I/O) utilizations of DataMPI are also more efficient than those of the other two frameworks.作者: 獸皮 時間: 2025-3-25 04:51 作者: FOIL 時間: 2025-3-25 07:45 作者: Folklore 時間: 2025-3-25 15:14 作者: curettage 時間: 2025-3-25 19:30
Performance Benefits of DataMPI: A Case Study with BigDataBench7?% and 50?% speedups compared with those of Hadoop and Spark, respectively. Most of the benefits come from the high-efficiency communication mechanisms in DataMPI. We also notice that the resource (CPU, memory, disk and network I/O) utilizations of DataMPI are also more efficient than those of the other two frameworks.作者: Carcinoma 時間: 2025-3-25 23:40 作者: 發(fā)酵 時間: 2025-3-26 00:36
Efficient HTTP Based I/O on Very Large Datasets for High Performance Computing with the Libdavix Libes of HTTP. Then, we describe in detail how we solved these issues. Our solutions have been implemented in a toolkit called davix, available through several recent Linux distributions..Finally, we describe the results of our benchmarks where we compare the performance of davix against a HPC specific protocol for a data analysis use case.作者: acrimony 時間: 2025-3-26 06:01 作者: 粗鄙的人 時間: 2025-3-26 11:04
https://doi.org/10.1007/978-3-031-41985-0the tool kits are suited for a specific environment. In this paper we propose DSIMBench, a benchmark containing two classic microarray analysis functions with eight different parallel R workflows, and evaluate the benchmark in the IC Cloud testbed platform.作者: LOPE 時間: 2025-3-26 13:12 作者: 顛簸下上 時間: 2025-3-26 19:06
Computational Morphology Tasks,ks to evaluate and compare big data systems has become an active topic for both research and industry communities. To date, most of the state-of-the-art big data benchmarks are designed for specific types of systems. Based on our experience, however, we argue that considering the complexity, diversi作者: unstable-angina 時間: 2025-3-26 21:08
Introduction to Arakelov Theorylt tolerance. For MapReduce applications, achieving low job execution time is critical. Since a majority of the existing clusters today are equipped with modern, high-speed interconnects such as InfiniBand and 10 GigE, that offer high bandwidth and low communication latency, it is essential to study作者: Seminar 時間: 2025-3-27 02:20 作者: 痛苦一下 時間: 2025-3-27 06:23
https://doi.org/10.1007/978-3-031-41985-0tion approaches. Newer data storage systems, such as MySQL Cluster and HBase, have been proposed for R data storage; while the parallel computation frameworks, including MPI and MapReduce, have been applied to R computation. Thus, it is difficult to understand the whole analysis workflows for which 作者: 圍裙 時間: 2025-3-27 12:18
First- and Second-Class Happiness,deos with metadata (e.g., geospatial properties), which are captured using the sensors available on these devices, are being collected. Clearly, a computing infrastructure is needed to store and manage this ever-growing large-scale video dataset with its structured data. Meanwhile, cloud computing s作者: Folklore 時間: 2025-3-27 15:48
Ontology (Ousiology) and Theology, BigTable, HBase, Cassandra, Azure and many others are designed to handle a large number of concurrent?requests performed on the cloud end. Such systems can elastically scale out to thousands of commodity hardware by using a shared nothing distributed architecture. This implies a strong need of data作者: 頭腦冷靜 時間: 2025-3-27 18:38 作者: Mutter 時間: 2025-3-27 22:03
Ontology (Ousiology) and Theology,ich directed affects the resource utilization and SLA and customer satisfaction. But before any management strategy is made, a good understanding of applications’ workload in virtualized environment is the basic fact and principle to the resource management methods. Unfortunately, little work has be作者: bile648 時間: 2025-3-28 03:43 作者: DEMN 時間: 2025-3-28 08:06
General problems in aroma research, .. . not only covers performance anomaly detection but also root cause inference, both of which are conducted under the consideration of operation context of big data applications. The performance anomaly detection procedure is adopted to trigger the cause inference procedure and accomplished by ch作者: Arb853 時間: 2025-3-28 12:34
General problems in aroma research,a large number of companies who have adopted Hadoop for their business purposes. One of the configuration parameters that influences the resource allocation and thus the performance of a Hadoop application is map slot value (MSV). MSV determines the number of map tasks that run concurrently on a nod作者: MARS 時間: 2025-3-28 17:01 作者: A精確的 時間: 2025-3-28 19:40 作者: Maximizer 時間: 2025-3-29 01:47 作者: 愚蠢人 時間: 2025-3-29 03:15
Undergraduate Topics in Computer Scienceprotocols are highly optimized for high throughput on very large datasets, multi-streams, high availability, low latency and efficient parallel I/O. The purpose of this paper is to describe how we have adapted a generic protocol, the Hyper Text Transport Protocol (HTTP) to make it a competitive alte作者: albuminuria 時間: 2025-3-29 08:06
Jianfeng Zhan,Rui Han,Chuliang WengIncludes supplementary material: 作者: 動機 時間: 2025-3-29 14:25
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/185635.jpg作者: 紳士 時間: 2025-3-29 17:41
https://doi.org/10.1007/978-3-319-13021-7BigBench; MapReduce-based systems; architectures; cloud computing; data management; data warehouse; databa作者: Calibrate 時間: 2025-3-29 22:44 作者: precede 時間: 2025-3-29 23:59
Big Data Benchmarks, Performance Optimization, and Emerging Hardware978-3-319-13021-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Cytology 時間: 2025-3-30 05:17
On Big Data Benchmarkinga systems. We then present the methodology on big data benchmarking designed to address these challenges. Next, the state-of-the-art are summarized and compared, following by our vision for future research directions.作者: FOLD 時間: 2025-3-30 10:03 作者: 古文字學(xué) 時間: 2025-3-30 14:16
