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IEEE International Conference on cloud computing, cloud

作者： Bo Xu Song Wu Jiang Xiao Hai Jin Yingxi Zhang Guoqiang Shi Tingyu Lin Jia Rao Li Yi Jizhong Jiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Intelligent Manufacturing System Technology Beijing China The University of Texas at Arlington Arlington TX USA Alibaba Cloud Computing Co. Ltd. Hangzhou China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Modern large-scale cloud platforms require live migration technique on Docker containers with stateful workload to support load balancing, host maintenance, and Quality of Service (QoS) improvement. Efficient and scalable Docker live migration is expected to guarantee the component-integrity (image, runtime, and management context) with negligible downtime. In this paper, we present a highly efficient live migration system called Sledge, which ensures the component-integrity by integrating both images and management context during runtime migration. The key insight is that the layered image can be leveraged to reduce the migration overhead, and appropriately selective migration of management context will effectively improve QoS with negligible downtime. To achieve good scalability, a lightweight container registry mechanism for end-to-end image migration is designed to avoid the redundant layers transmission. In addition, a dynamic context loading scheme is proposed to precisely load the management context into the running daemon, which can significantly reduce downtime. Experiments show that, compared with the state-of-the-art, Sledge reduces 57% of total migration time, 55% of image migration time, and 70% downtime.

关键词： cloud computing Runtime Scalability Loading Quality of service Containers Maintenance engineering

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Precision monitoring of radial growth of trees and micro-climate at a Korean Fir (Abies koreana Wilson) forest at 10 minutes interval in 2016 on Mt. Hallasan National Park, Jeju Island, Korea

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Journal of Ecology and Environment 2019年第1期43卷 1页

作者： Kim, Eun-Shik Cho, Hong-Bum Heo, Daeyoung Kim, Nae-Soo Kim, Young-Sun Lee, Kyeseon Lee, Sung-Hoon Ryu, Jaehong Department of Forestry Environment and Systems Kookmin University Seoul 02707 South Korea Institute of Forest Science Kookmin University Seoul 02707 South Korea Grid and Cloud Computing Laboratory School of Computer Science Kookmin University Seoul 02707 South Korea Electronics and Telecommunications Research Institute 218 Gajeong Ro Yuseong Gu Daejeon 34129 South Korea

To understand the dynamics of radial growth of trees and micro-climate at a site of Korean fir (Abies koreana Wilson) forest on high-altitude area of Mt. Hallasan National Park, Jeju Island, Korea, high precision dendrometers were installed on the stems of Korean fir trees, and the sensors for measuring micro-climate of the forest at 10 minutes interval were also installed at the forest. Data from the sensors were sent to nodes, collected to a gateway wireless, and transmitted to a data server using mobile phone communication system. By analyzing the radial growth data for the trees during the growing season in 2016, we can estimate that the radial growth of Korean fir trees initiated in late April to early May and ceased in late August to early September, which indicates that period for the radial growth was about 4 months in 2016. It is interesting to observe that the daily ambient temperature and the daily soil temperature at the depth of 20 cm coincided with the values of about 10 °C when the radial growth of the trees initiated in 2016. When the radial growth ceased, the values of the ambient temperature went down below about 15 °C and 16 °C, respectively. While the ambient temperature and the soil temperature are evaluated to be the good indicators for the initiation and the cessation of radial growth, it becomes clear that radii of tree stems showed diurnal growth patterns affected by diurnal change of ambient temperature. In addition, the wetting and drying of the surface of the tree stems affected by precipitation became the additional factors that affect the expansion and shrinkage of the tree stems at the forest site. While it is interesting to note that the interrelationships among the micro-climatic factors at the forest site were well explained through this study, it should be recognized that the precision monitoring made possible with the application of high resolution sensors in the measurement of the radial increment combined with the observation of

关键词： Abies koreana Ecological platform Environmental factors Korean fir trees Micro-climate Precision dendrometer Radial growth Real-time monitoring

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A Processing Pipeline for Cassandra Datasets Based on Hadoop Streaming

A Processing Pipeline for Cassandra Datasets Based on Hadoop...

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IEEE International Conference on Services Economics (SE)

作者： E. Dede B. Sendir P. Kuzlu J. Weachock M. Govindaraju L. Ramakrishnan Grid and Cloud Computing Research Laboratory SUNY Binghamton New York USA Lawrence Berkeley National Lab Berkeley CA

The progressive transition in the nature of both scientific and industrial datasets has been the driving force behind the development and research interests in the NoSQL data model. Loosely structured data poses a challenge to traditional data store systems, and when working with the NoSQL model, these systems are often considered impractical and expensive. As the quantity of unstructured data grows, so does the demand for a processing pipeline that is capable of seamlessly combining the NoSQL storage model and a "Big Data" processing platform such as MapReduce. Although, MapReduce is the paradigm of choice for data-intensive computing, Java-based frameworks such as Hadoop require users to write MapReduce code in Java. Hadoop Streaming, on the other hand, allows users to define non-Java executables as map and reduce operations. Similarly, for legacy C/C++ applications and other non-Java executables, there is a need to allow NoSQL data stores access to the features of Hadoop Streaming. In this paper, we present approaches in solving the challenge of integrating NoSQL data stores with MapReduce for non-Java application scenarios, along with advantages and disadvantages of each approach. We compare Hadoop Streaming alongside our own streaming framework, MARISSA, to show performance implications of coupling NoSQL data stores like Cassandra with MapReduce frameworks that normally rely on file-system based data stores.

关键词： Servers Pipelines Databases Big data Data models Java

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An Evaluation of Cassandra for Hadoop

An Evaluation of Cassandra for Hadoop

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IEEE International Conference on cloud computing, cloud

作者： Elif Dede Bedri Sendir Pinar Kuzlu Jessica Hartog Madhusudhan Govindaraju SUNY Grid and Cloud Computing Research Laboratory Binghamton New York USA

In the last decade, the increased use and growth of social media, unconventional web technologies, and mobile applications, have all encouraged development of a new breed of database models. NoSQL data stores target the unstructured data, which by nature is dynamic and a key focus area for "Big Data" research. New generation data can prove costly and unpractical to administer with SQL databases due to lack of structure, high scalability, and elasticity needs. NoSQL data stores such as MongoDB and Cassandra provide a desirable platform for fast and efficient data queries. This leads to increased importance in areas such as cloud applications, e-commerce, social media, bioinformatics, and materials science. In an effort to combine the querying capabilities of conventional database systems and the processing power of the MapReduce model, this paper presents a thorough evaluation of the Cassandra NoSQL database when used in conjunction with the Hadoop MapReduce engine. We characterize the performance for a wide range of representative use cases, and then compare, contrast, and evaluate so that application developers can make informed decisions based upon data size, cluster size, replication factor, and partitioning strategy to meet their performance needs.

关键词： Peer-to-peer computing Data models Servers Writing Distributed databases Benchmark testing

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L2 cache performance analysis and optimizations for processing HDF5 data on multi-core nodes

L2 cache performance analysis and optimizations for processi...

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2012 10th IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2012

作者： Bhowmik, Rajdeep Govindaraju, Madhusudhan Grid and Cloud Computing Research Laboratory Department of Computer Science SUNY Binghamton P.O. Box 6000 Binghamton NY 13902-6000 United States

ISBN: (纸本)9780769547015

It is important to design and develop scientific middleware libraries to harness the opportunities presented by emerging multi-core processors that are available on grid and cloud environments. Scientific middleware libraries not adhering or adapting to this programming paradigm can suffer from severe performance limitations while executing on emerging multi-core processors. In this paper, we focus on the utilization of a critical shared resource on chip multiprocessors (CMPs), the L2 cache. The way in which an application schedules and assigns processing work to each thread determines the access pattern of the shared L2 cache, which may result in either enhancing or diminishing the effects of memory latency on a multi-core processor. Therefore, while processing scientific datasets such as HDF5, it is essential to conduct fine-grained analysis of cache utilization, to make informed processing and scheduling decisions in multi-threaded programming. In this paper, using the TAU toolkit for performance feedback from dual- and quad-core machines, we analyze and recommend methods for effective scheduling of threads on multi-core nodes to augment the performance of scientific applications processing HDF5 data. We discuss the benefits that can be achieved by using L2 Cache-Affinity and L2 Balanced-Set based scheduling algorithms for improving L2 cache performance and effectively the overall execution time. © 2012 IEEE.

关键词： Scheduling algorithms

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L2 Cache Performance Analysis and Optimizations for Processing HDF5 Data on Multi-core Nodes

L2 Cache Performance Analysis and Optimizations for Processi...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Rajdeep Bhowmik Madhusudhan Govindaraju Grid and Cloud Computing Research Laboratory Department of Computer Science SUNY at Binghamton Binghamton NY USA

关键词： Instruction sets Multicore processing Optimization Processor scheduling Hardware Libraries Middleware

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Scalable and distributed processing of scientific XML data

Scalable and distributed processing of scientific XML data

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12th IEEE/ACM International Conference on grid computing, grid 2011

作者： Dede, Elif Fadika, Zacharia Gupta, Chaitali Govindaraju, Madhusudhan Grid and Cloud Computing Research Laboratory Department of Computer Science SUNY Binghamton NY 13902 United States

A seamless and intuitive search capability for the vast amount of datasets generated by scientific experiments is critical to ensure effective use of such data by domain specific scientists. Currently, searches on enormous XML datasets is done manually via custom scripts or by using hard-to-customize queries developed by experts in complex and disparate XML query languages. Such approaches however do not provide acceptable performance for large-scale data since they are not based on a scalable distributed solution. Furthermore, it has been shown that databases are not optimized for queries on XML data generated by scientific experiments, as term kinship, range based queries, and constraints such as conjunction and negation need to be taken into account. There exists a critical need for an easy-to-use and scalable framework, specialized for scientific data, that provides natural-language-like syntax along with accurate results. As most existing search tools are designed for exact string matching, which is not adequate for scientific needs, we believe that such a framework will enhance the productivity and quality of scientific research by the data reduction capabilities it can provide. This paper presents how the MapReduce model should be used in XML metadata indexing for scientific datasets, specifically Teragrid Information Services and the NeXus datasets generated by the Spallation Neutron Source (SNS) scientists. We present an indexing structure that scales well for large-scale MapReduce processing. We present performance results using two MapReduce implementations, Apache Hadoop and LEMO-MR, to emphasize the flexibility and adaptability of our framework in different MapReduce environments. © 2011 IEEE.

关键词： MapReduce

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Scalable and Distributed Processing of Scientific XML Data

Scalable and Distributed Processing of Scientific XML Data

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International Workshop on grid computing

作者： Elif Dede Zacharia Fadika Chaitali Gupta Madhusudhan Govindaraju Grid and Cloud Computing Research Laboratory Department of Computer Science SUNY Binghamton NY USA

关键词： XML Indexing Semantics Web search Search engines Complexity theory

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Benchmarking MapReduce Implementations for Application Usage Scenarios

Benchmarking MapReduce Implementations for Application Usage...

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International Workshop on grid computing

作者： Zacharia Fadika Elif Dede Madhusudhan Govindaraju Lavanya Ramakrishnan Grid and Cloud Computing Research Laboratory Department of Computer Science State University of New York Binghamton USA

The MapReduce paradigm provides a scalable model for large scale data-intensive computing and associated fault-tolerance. With data production increasing daily due to ever growing application needs, scientific endeavors, and consumption, the MapReduce model and its implementations need to be further evaluated, improved, and strengthened. Several MapReduce frameworks with various degrees of conformance to the key tenets of the model are available today, each, optimized for specific features. HPC application and middleware developers must thus understand the complex dependencies between MapReduce features and their application. We present a standard benchmark suite for quantifying, comparing, and contrasting the performance of MapReduce platforms under a wide range of representative use cases. We report the performance of three different MapReduce implementations on the benchmarks, and draw conclusions about their current performance characteristics. The three platforms we chose for evaluation are the widely used Apache Hadoop implementation, Twister, which has been discussed in the literature, and LEMO-MR, our own implementation. The performance analysis we perform also throws light on the available design decisions for future implementations, and allows grid researchers to choose the MapReduce implementation that best suits their application's needs.

关键词： Fault tolerance Fault tolerant systems Random access memory Memory management Benchmark testing Data processing Linux

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