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SCALE: a scalable framework for efficiently clustering transactional data

Hua Yan, Keke Chen, Ling Liu, Zhang Yi
2009 Data mining and knowledge discovery  
Second, we develop the weighted coverage density measure based clustering algorithm, a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data.  ...  This paper presents SCALE, a fully automated transactional clustering framework. The SCALE design highlights three unique features.  ...  Acknowledgments The first author is partly supported by China Scholarship Council for her one year visit at Georgia Institute of Technology.  ... 
doi:10.1007/s10618-009-0134-5 fatcat:xr3iicgtovfw7frrjbnzdlgbvi

Computing at Massive Scale: Scalability and Dependability Challenges

Renyu Yang, Jie Xu
2016 2016 IEEE Symposium on Service-Oriented System Engineering (SOSE)  
Large-scale Cloud systems and big data analytics frameworks are now widely used for practical services and applications.  ...  We first introduce a data-driven analysis methodology for characterizing the resource and workload patterns and tracing performance bottlenecks in a massive-scale distributed computing environment.  ...  Peter Garraghan from University of Leeds and Jin Ouyang from Alibaba Cloud Inc. for discussions.  ... 
doi:10.1109/sose.2016.73 dblp:conf/sose/YangX16 fatcat:bsbdpnfzpnf5jbl2d3hobd7adu

Scale-Out vs. Scale-Up Techniques for Cloud Performance and Productivity

Kai Hwang, Yue Shi, Xiaoying Bai
2014 2014 IEEE 6th International Conference on Cloud Computing Technology and Science  
Scale-out to a larger cluster of small nodes demonstrated high scalability. (2).  ...  Scaling up and mixed scaling have high performance in using smaller clusters with a few powerful machine instances. (3).  ...  The scale-up cluster shows very high efficiency for WordCount.  ... 
doi:10.1109/cloudcom.2014.66 dblp:conf/cloudcom/HwangSB14 fatcat:fnmnjmen2ngw5c5eushwyrs2jy

Scaling Reliably

Phil Trinder, Olivier Boudeville, Francesco Cesarini, Maurizio Di Stefano, Sverker Eriksson, Viktória fördős, Amir Ghaffari, Aggelos Giantsios, Rickard Green, Csaba Hoch, David Klaftenegger, Natalia Chechina (+13 others)
2017 ACM Transactions on Programming Languages and Systems  
While we report measurements on a range of NUMA and cluster architectures, the key scalability experiments are conducted on the Athos cluster with 256 hosts (6144 cores).  ...  We exceed the established scalability limits of distributed Erlang, and do not reach the limits of SD Erlang for these benchmarks at this scale (256 hosts, 6144 cores).  ...  This work has been supported by the European Union grant RII3-CT-2005-026133 "SCIEnce: Symbolic Computing Infrastructure in Europe", IST-2011-287510 "RELEASE: A High-Level Paradigm for Reliable Large-scale  ... 
doi:10.1145/3107937 fatcat:3l3v5solvvfvdj4y47et3gba3y

Towards Scaling Blockchain Systems via Sharding [article]

Hung Dang, Tien Tuan Anh Dinh, Dumitrel Loghin, Ee-Chien Chang, Qian Lin, Beng Chin Ooi
2019 arXiv   pre-print
In this work, we take a principled approach to apply sharding, which is a well-studied and proven technique to scale out databases, to blockchain systems in order to improve their transaction throughput  ...  Finally, we conduct an extensive evaluation of our design both on a local cluster and on Google Cloud Platform.  ...  This protocol relies on a trusted randomness beacon implemented inside a TEE for efficiency.  ... 
arXiv:1804.00399v4 fatcat:cyulzlarwfgyris4l2bty6yczi

Architecture Of Large-Scale Systems

Arne Koschel, Irina Astrova, Elena Deutschkämer, Jacob Ester, Johannes Feldmann
2013 Zenodo  
Finally, examples of large-scale systems are presented.  ...  In this paper various techniques in relation to large-scale systems are presented. At first, explanation of large-scale systems and differences from traditional systems are given.  ...  The hardware requirements for a social network like Facebook compared to a large-scale-scientific-cluster for calculating weather data are different.  ... 
doi:10.5281/zenodo.1089019 fatcat:bvlz4bgpdzanhaxud7la4jxzeu

Large-scale e-learning recommender system based on Spark and Hadoop

Karim Dahdouh, Ahmed Dakkak, Lahcen Oughdir, Abdelali Ibriz
2019 Journal of Big Data  
In this article, we develop a distributed courses recommender system for the e-learning platform.  ...  Next, we deploy our recommender system using big data technologies and techniques. Especially, we implement parallel FP-growth algorithm provided by Spark Framework and Hadoop ecosystem.  ...  Funding No funding has been received for the conduct of this work and preparation of this manuscript.  ... 
doi:10.1186/s40537-019-0169-4 fatcat:odz2ffldxbb3neync6uy7uhs7e

Managing data retention policies at scale

Jun Li, Sharad Singhal, Ram Swaminathan, Alan H. Karp
2011 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops  
A prototype deployed in a 16-machine Linux cluster currently supports 56 MB/sec for encryption, 76 MB/sec for decryption, 31, 000 retention policies/sec read and 15,000 retention policies/sec write.  ...  A prototype deployed in a 16-machine Linux cluster currently supports 56 MB/sec for encryption, 76 MB/sec for decryption, 31,000 retention policies/sec read and 15,000 retention policies/sec write.  ...  Managing data policies at scale poses various challenges, including the following two that we believe are key challenges to managing data retention policies:  Scalable Policy Enforcement: A scalable engine  ... 
doi:10.1109/inm.2011.5990674 dblp:conf/im/LiSSK11 fatcat:w65v5lzp7jec5oebgvp5grrjd4

Effective Scaling of Blockchain Beyond Consensus Innovations and Moore's Law [article]

Yinqiu Liu, Kai Qian, Jianli Chen, Kun Wang, Lei He
2020 arXiv   pre-print
We achieve this by developing an open-source benchmarking tool, called Prism, for investigating the key factors causing low resource efficiency and then discuss various topology and hardware innovations  ...  This makes blockchain suffer from insufficient performance and poor scalability.  ...  TPC, Transactions per CPU; TPMS, Transactions per Memory Second; TPDIO, Transactions per I/O Disk; TPND, Transactions per Network Data. Data obtained by Prism.  ... 
arXiv:2001.01865v2 fatcat:yhjx2yxtdnaapgj7xleuyqjeri

Survey of Large-Scale Data Management Systems for Big Data Applications

Lengdong Wu, Liyan Yuan, Jiahuai You
2015 Journal of Computer Science and Technology  
high throughput of transaction processing, but lack the capacity of scale-out.  ...  The pursuit for tackling the challenges posed by the big data trend has given rise to a plethora of data management systems characterized by high scalability.  ...  To conclude, this work delves deeper to lay down a comprehensive taxonomy framework that, not only serves as a direction of analyz- ing the large-scale data management systems for the big data application  ... 
doi:10.1007/s11390-015-1511-8 fatcat:tzdpu6jdive6dooandmaepiko4

Large-scale circuit placement

Jason Cong, Joseph R. Shinnerl, Min Xie, Tim Kong, Xin Yuan
2005 ACM Transactions on Design Automation of Electronic Systems  
The second part of the tutorial highlights the recent progress on large-scale circuit placement, including techniques for wirelength minimization, routability optimization, and performance optimization  ...  The first part of this tutorial summarizes results from recent optimality and scalability studies of existing placement tools.  ...  INTRODUCTION The exponential growth of on-chip complexity has dramatically increased the demand for scalable optimization algorithms for large-scale physical design.  ... 
doi:10.1145/1059876.1059886 fatcat:ji7ameibx5cqrefa5fmewiojci

Efficient and Scalable Metadata Management in EB-Scale File Systems

Quanqing Xu, Rajesh Vellore Arumugam, Khai Leong Yong, Sridhar Mahadevan
2014 IEEE Transactions on Parallel and Distributed Systems  
Efficient and scalable distributed metadata management is critically important to overall system performance in large-scale distributed file systems, especially in the EB-scale era.  ...  By conducting performance evaluation through extensive trace-driven simulations and a prototype implementation, experimental results demonstrate the efficiency and scalability of DROP.  ...  This work is supported by A * STAR Thematic Strategic Research Programme (TSRP) Grant No. 1121720013.  ... 
doi:10.1109/tpds.2013.293 fatcat:sltd4blg2bdj5p6mqlgodin2gq

Software transactional memory for large scale clusters

Robert L. Bocchino, Vikram S. Adve, Bradford L. Chamberlain
2008 Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming - PPoPP '08  
In this work, we present Cluster-STM, an STM designed for high performance on large-scale commodity clusters.  ...  While there has been extensive work on the design of software transactional memory (STM) for cache coherent shared memory systems, there has been no work on the design of an STM system for very large scale  ...  We also wish to thank the National Center for Supercomputing Applications (NCSA) at the University of Illinois for granting time and support on the Tungsten cluster.  ... 
doi:10.1145/1345206.1345242 dblp:conf/ppopp/BocchinoAC08 fatcat:44dfhcxbqnem7fqou476gsdsm4

Mining Profitable and Concise Patterns in Large-Scale Internet of Things Environments

Jerry Chun-Wei Lin, Youcef Djenouri, Gautam Srivastava, Philippe Fournier-Viger, Xingsi Xue
2021 Wireless Communications and Mobile Computing  
First, a GA-based MapReduce model is presented in this work known as GMR-Miner for mining closed patterns with high utilization in large-scale databases.  ...  A genetic algorithm (GA) is utilized in the developed MapReduce framework that can be used to explore the potential and possible candidates in a limited time.  ...  Acknowledgments Western Norway University of Applied Sciences, Norway, provides partial funding support for the work carried out in this paper.  ... 
doi:10.1155/2021/6653816 fatcat:wc7yzldruffohjkf3b4qedgi6y

A Scalable Framework for Provisioning Large-Scale IoT Deployments

Michael Vögler, Johannes M. Schleicher, Christian Inzinger, Schahram Dustdar
2016 ACM Transactions on Internet Technology  
To improve scalability and reduce generated network traffic between cloud and edge infrastructure, we present a distributed provisioning approach that deploys LEONORE local nodes within the deployment  ...  This is especially important in the context of large-scale IoT systems, such as in the smart city domain.  ...  The IoT gateway has the following components: (i) a container, A Scalable Framework for Provisioning Large-scale IoT Deployments A:5 hosting application packages, (ii) a profiler, monitoring the current  ... 
doi:10.1145/2850416 fatcat:qydaemcvrzhb3p4edy5denaymi
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