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检索条件"主题词=distributed databases"

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Back cover

引用

Journal of Communications and Networks 2018年第3期20卷 c4-c4页

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ABC: A practicable sketch framework for non-uniform multisets

ABC: A practicable sketch framework for non-uniform multiset...

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IEEE International Conference on Big Data

作者： Junzhi Gong Tong Yang Yang Zhou Dongsheng Yang Shigang Chen Bin Cui Xiaoming Li Department of Computer Science Peking University China Collaborative Innovation Center of High Performance Computing NUDT China Department of Computer & Information of Science & Engineering University of Florida USA

Sketch is a data structure used to record frequencies of items in a multiset, which is widely used in data streams, data graph, distributed datasets processing, etc. It works with small memory usage and a high speed at the cost of a slight inaccuracy. In practice, frequencies of items in many datasets are non-uniformly distributed. Unfortunately, existing sketches can hardly work well on non-uniform datasets. To address this issue, we propose a new sketch framework, namely ABC framework, which can be applied to most existing sketches and can significantly improve the accuracy on non-uniform datasets. The key idea behind our framework is that when a counter overflows, it makes use of the space from the adjacent counters by operations of bits-borrowing and combination. Extensive experimental results show that our ABC framework improves the accuracy by 4.10 times and 4.49 times in average, respectively. A demo and all the related source codes are available on our homepage [1].

关键词： Frequency estimation Estimation Memory management Data structures distributed databases Probabilistic logic

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ELATS: Energy and locality aware aggregation tree for skip graph

ELATS: Energy and locality aware aggregation tree for skip g...

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International Black Sea Conference on Communications and Networking (BlackSeaCom)

作者： Yahya Hassanzadeh-Nazarabadi Öznur Özkasap Department of Computer Engineering Koç University İstanbul Turkey

ISBN: (纸本)9781509050505;9781509050499

As a distributed hash table (DHT), Skip Graph acts as the underlying routing infrastructure of peer-to-peer (P2P) storage systems, distributed online social networks, search engines, and other DHT-based applications. For many P2P applications, data aggregation is vital, however, it is a missing feature of Skip Graph. The traditional aggregation algorithms cost noticeable message overhead which degrades the energy efficiency while increasing the response time. Likewise, the aggregation trees proposed for other DHTs are either inapplicable to the Skip Graph or apply some sort of randomness in their construction. Randomized features of an aggregation tree result in higher aggregation latency as well as enforcing unbalanced load on nodes which negatively affect the energy efficiency. In this paper, we propose ELATS which is the first energy and locality aware aggregation tree for Skip Graph. We define the energy awareness as minimizing the average energy cost of an aggregation tree, and the locality awareness as minimizing the latency on the path between the root and leaves of the aggregation tree. Performance analysis results show that ELATS algorithm provides both energy and locality awareness, and improves the aggregation latency with the gain of about 8% in comparison to the best existing solutions for DHTs which are either locality aware or energy aware.

关键词： Peer-to-peer computing Data aggregation Routing Time factors distributed databases Mathematical model Social network services

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Towards an adaptive multi-agent architecture for association rule mining in distributed databases

Towards an adaptive multi-agent architecture for association...

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International Conference on Adaptive Science & Technology (ICAST)

作者： Adewale Opeoluwa Ogunde Olusegun Folorunso Adesina Simon Sodiya James Adedayo Oguntuase Redeemer's University (RUN) Ogun Nigeria University of Agriculture Abeokuta Ogun Nigeria

Association rule mining, which is a data mining technique, finds interesting association or correlation relationships among a large set of data items. Current association rule mining tasks can only be accomplished successfully only in a distributed setting, which will require integration of knowledge generated from the multiple data sites. Most existing architectures for mining in such circumstances require massive movement of data resulting in high communication overheads leading to slow response time. These challenges are heightened when we have extremely large data sizes in multiple heterogeneous sites. Moreover, most existing algorithms and architectures are only moderately suitable for real-world scenarios. There is therefore an urgent need for improved architectures that will explore the capabilities of software agents' paradigms in order to improve on the existing systems. This work therefore introduces an adaptive architectural framework that mines association rules across multiple data sites, and more importantly the architecture adapts to changes in the updated database giving special considerations to the incremental database with the X-Apriori algorithm. The results integration agent also adapts to changes in the results sites considering the size of the agents; size of intermediate results; bandwidth, and other computational resources at the data servers. The proposed system promises to reduce communication and interpretation costs, improve autonomy and efficiency of distributed association rule mining tasks.

关键词： Association rules distributed databases Computer architecture Mobile agents Servers

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DDB Expert: A Recommender for distributed databases Design

DDB Expert: A Recommender for Distributed Databases Design

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International Workshop on Database and Expert Systems Applications

作者： Rim Moussa UTIC Laboratory University of Tunis Carthage Tunisia CS Department University of Carthage Carthage Tunisia

This paper proposes a framework based on expert systems for distributed database design. The DB fragmentation advisor recommends table fragmentation or full/partial replication through DB schema analysis, DB workload analysis and DB statistics. The advisor is low cost, has a low memory footprint, and is very fast. The framework is flexible since it's open to workload changes with new business requirements or query/interaction's frequency changes as well as recommender enhancements with new rules.

关键词： distributed databases Benchmark testing Parallel processing Knowledge based systems Partitioning algorithms Production

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Privacy Aware Parallel Computation of Skyline Sets Queries from distributed databases

Privacy Aware Parallel Computation of Skyline Sets Queries f...

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International Conference on Networking and Computing (ICNC)

作者： Mohammad Shamsul Arefin Yasuhiko Morimoto Hiroshima University Higashihiroshima Japan

A skyline query finds the objects that are not dominated by another object from a given set of objects. Skyline queries help us to filter unnecessary information efficiently and provide us clues for various decision making tasks. However, we have to be aware about the privacy of individual's. In privacy aware environments, we have to hide individual records' values even though there is no ID information in the table. In such situations, it is not possible to use conventional skyline queries. To handle the privacy problem, we consider a skyline query for sets of objects in a database. Let s be the number of objects in each set and n be the number of objects in the database. The number of sets in the database is nCs. Skyline sets query returns all skyline s-sets from nCs sets. We consider an efficient algorithm for computing "convex" skyline of the nCs sets. In this paper, we propose a method for computing skyline sets queries in parallel fashion from distributed databases without disclosing individual records to others. The proposed method utilizes an agent-based parallel computing framework that solves the privacy problems of skyline queries in distributed environments. The computation of skyline sets is performed simultaneously in all databases that increases parallelism and reduces the computation time. We show through intensive experiments that our propose technique is almost independent of the number of servers involved in skyline sets queries, thus becoming an efficient and scalable solution for skyline sets queries in distributed environments.

关键词： Vectors IP networks Servers distributed databases Privacy Parallel processing

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Redundancy Adaptation for Multi-Path Intra-Flow Network Coding in Wireless Mesh Networks 84

Redundancy Adaptation for Multi-Path Intra-Flow Network Codi...

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84th IEEE Vehicular Technology Conference (VTC-Fall)

作者： Ageneau, Paul-Louis Wu, Chuchu Boukhatem, Nadia Gerla, Mario Telecom ParisTech LTCI Comp Sci & Network Dept Paris France Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA

ISBN: (纸本)9781509017010

Network coding can significantly enhance throughput and reliability in loss prone wireless networks. Adapting network coding redundancy is critical, since over-redundancy wastes network resources and hurts performance and under-estimated redundancy prevents decoding at destination. In this paper, we study the tradeoff between application tolerated loss rate and network overhead introduced by network coding redundancy. We first propose an analytic model that determines a simple redundancy bound to adapt network coding redundancy according to the measured packet loss rate and the targeted maximum application loss rate. Then, we propose a distributed algorithm with adaptive redundancy for reliable data delivery in wireless meshes. In the algorithm, each node opportunistically makes use of multiple paths available in the network to deliver data to the destination. We demonstrate the benefits of our schemes through simulation in various network scenarios.

关键词： Redundancy Network coding Encoding Throughput distributed databases Estimation Network coding redundancy distributed databases Throughput coding Wireless mesh networks destination multi-path

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Medusa: An Efficient Cloud Fault-Tolerant MapReduce 16

Medusa: An Efficient Cloud Fault-Tolerant MapReduce

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16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)

作者： Costa, Pedro A. R. S. Bai, Xiao Ramos, Fernando M. V. Correia, Miguel Univ Lisbon LaSIGE Fac Ciencias Lisbon Portugal Yahoo Labs Sunnyvale CA USA Univ Lisbon Inst Super Tecn INESC ID Lisbon Portugal

ISBN: (纸本)9781509024537

Applications such as web search and social networking have been moving from centralized to decentralized cloud architectures to improve their scalability. MapReduce, a programming framework for processing large amounts of data using thousands of machines in a single cloud, also needs to be scaled out to multiple clouds to adapt to this evolution. The challenge of building a multi-cloud distributed architecture is substantial. Notwithstanding, the ability to deal with the new types of faults introduced by such setting, such as the outage of a whole datacenter or an arbitrary fault caused by a malicious cloud insider, increases the endeavor considerably. In this paper we propose Medusa, a platform that allows MapReduce computations to scale out to multiple clouds and tolerate several types of faults. Our solution fulfills four objectives. First, it is transparent to the user, who writes her typical MapReduce application without modification. Second, it does not require any modification to the widely used Hadoop framework. Third, the proposed system goes well beyond the fault-tolerance offered by MapReduce to tolerate arbitrary faults, cloud outages, and even malicious faults caused by corrupt cloud insiders. Fourth, it achieves this increased level of fault tolerance at reasonable cost. We performed an extensive experimental evaluation in the ExoGENI testbed, demonstrating that our solution significantly reduces execution time when compared to traditional methods that achieve the same level of resilience.

关键词： Cloud computing Fault tolerance Fault tolerant systems distributed databases Servers Web search Computer architecture Fault tolerance Web search Fault tolerant systems distributed databases Computer Architecture Servers Clouds Cloud Computing CLOUDS faults medusa

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A hybrid simulation model for data grids 16

A hybrid simulation model for data grids

引用

16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)

作者： Barisits, Martin Kuehn, Eva Lassnig, Mario CERN CH-1211 Geneva 23 Switzerland Vienna Univ Technol Inst Comp Languages A-1040 Vienna Austria

ISBN: (纸本)9781509024537

Data grids are used in large scale scientific experiments to access and store nontrivial amounts of data by combining the storage resources from multiple data centers in one system. This enables users and automated services to use the storage resources in a common and efficient way. However, as data grids grow it becomes a hard problem for developers and operators to estimate how modifications in policy, hardware, and software affect the performance metrics of the data grid. In this paper we address the modeling of operational data grids. We first analyze the data grid middleware system of the ATLAS experiment at the Large Hadron Collider to identify components relevant to the data grid performance. We describe existing modeling approaches for pre-transfer, network, storage, and validation components, and build black-box models for these components. Consequently, we present a novel hybrid model, which unifies these separate component models, and we evaluate the model using an event simulator. The evaluation is based on historic workloads extracted from the ATLAS data grid. The median evaluation error of the hybrid model is at 22%.

关键词： Data models Predictive models Computational modeling Analytical models distributed databases Time factors Grid computing Grid computing distributed databases data models Time Factors Computational modeling ANALYSIS MODELS Prediction models Hybrid modes grids data center Performance metrics Grid

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Acacia-RDF: An X10-based Scalable distributed RDF Graph Database Engine 9

Acacia-RDF: An X10-based Scalable Distributed RDF Graph Data...

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9th IEEE International Conference on Cloud Computing (CLOUD)

作者： Dayarathna, Miyuru Herath, Isuru Dewmini, Yasima Mettananda, Gayan Nandasiri, Sameera Jayasena, Sanath Suzumura, Toyotaro WSO2 Inc Mountain View CA 94041 USA Univ Moratuwa Dept Comp Sci & Engn Moratuwa Sri Lanka JST CREST Tokyo Japan IBM TJ Watson Res Ctr Yorktown Hts NY USA Barcelona Supercomp Ctr Barcelona Spain Univ Tokyo Tokyo Japan

ISBN: (纸本)9781509026197

Linked data mining has become one of the key questions in High Performance graph mining in recent years. However, the existing Resource Description Framework (RDF) database engines are not scalable and are less reliable in heterogeneous clouds. In this paper we describe the design and implementation of Acacia-RDF which is a scalable distributed RDF graph database engine developed with X10 programming language to solve this issue. Acacia-RDF partitions the RDF data sets into subgraphs following vertex cut paradigm. The partitioned data sets are persisted on secondary storage across X10 places. We developed a scalable SPARQL (an RDF query language) processor for Acacia-RDF which operates on top of partitioned RDF data. Furthermore, we designed and implemented a replication based fault tolerance mechanism for Acacia-RDF. We present performance results gathered from Acacia with different scales of LUBM (Lehigh University Benchmark) RDF benchmark data sets. We make a comparison of AcaciaRDF's performance against Neo4j graph database server. From the scalability experiments conducted upto 16 X10 places, we observed that Acacia-RDF scales well with LUBM data sets. Acacia-RDF reported less than ten seconds elapsed times on 16 places for running the first and the third queries of the LUBM benchmark on LUBM 160 universities data set with 3.6 million vertices and 28.5 million edges which was 1.7GB in size. Through this work we describe and demonstrate the use of X10 language for development of scalable RDF graph data management systems.

关键词： RDF SPARQL Graph databases Database management distributed databases Graph theory Cloud computing

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