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Towards a Self-Adaptive Data Management System for Cloud Environments

Towards a Self-Adaptive Data Management System for Cloud Env...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Alexandra Carpen-Amarie INRIA/IRISA Rennes France

The emergence of Cloud computing brings forward many challenges that may limit the adoption rate of the Cloud paradigm. As data volumes processed by applications running on Clouds increase, the need for efficient and secure data management emerges as a crucial requirement. This work aims to enable BlobSeer, a large-scale data management system, as a Cloud data service, by addressing a series of self-management requirements. The first step towards an autonomic behavior was to equip the BlobSeer platform with introspection capabilities, which can serve as input data for a self-adaptive engine designed to address such goals as self-configuration, self-optimization or self-protection. We developed the self-protection direction within a generic security management framework allowing providers of Cloud data management systems to define and enforce complex security policies. In addition, we designed an expressive policy description language enabling system administrators to define a large array of security attacks and to enforce various types of restrictions upon the detected malicious clients. Finally, we are integrating our data-management system as a storage back end within the Nimbus Cloud environment.

关键词： Security Monitoring Cloud computing distributed databases Throughput Availability Clouds

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parallelizing BLAST and SOM Algorithms with MapReduce-MPI Library

Parallelizing BLAST and SOM Algorithms with MapReduce-MPI Li...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Seung-Jin Sul Andrey Tovchigrechko J. Craig Venter Institute Rockville MD USA

Most bioinformatics algorithms are developed in a serial form due to a fast pace of changes in the subject domain and the fact that many bioinformatics tasks can be parallelized as collections of serial jobs communicating at the file system level (High-Throughput Computing, HTC). Recently, a MapReduce-MPI library was made available by Sandia Lab to ease porting of a large class of serial applications to the High Performance Computing (HPC) architectures dominating large federated resources such as NSF Tera Grid. Using this library, we have created two open-source bioinformatics applications. The first one addresses a problem of adapting existing complex and highly optimized serial bioinformatics algorithm to HPC architecture in a minimally invasive way. We built a parallel BLAST implementation that calls the high-level methods of unmodified NCBI C++ Toolkit. We demonstrated scaling for up to 1000 cores on TACC Ranger cluster when processing the sufficiently large input datasets. Using unmodified NCBI Toolkit ensures that the results are compatible across the multitude of settings in the original serial algorithm, and that future versions of the upstream code can be easily integrated. The second application is a Self-Organizing Map (SOM) machine-learning algorithm, popular in bioinformatics applications such as metagenomic binning. The nature of the SOM requires a global synchronization step with a frequency that necessitates the use of an HPC environment. Our implementation of the "batch SOM" uses a mix of MapReduce-MPI and direct MPI calls and scales to 1000 cores as well. This allows easy processing of datasets with a size that is out of range of the serial SOM implementations. Both implementations are available in the open source at http://***/mgtaxa/.

关键词： Bioinformatics databases Libraries Clustering algorithms parallel processing Computer architecture File systems

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Very large pattern databases for heuristic search 10

Very large pattern databases for heuristic search

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proceedings of the 19th ACM international symposium on High Performance distributed Computing

作者： Reinefeld, Alexander Schütt, Thorsten Maier, Robert Zuse Institute Berlin Takustrasse 7 14195 Berlin Germany

ISBN: (纸本)9781605589428

Pattern databases (PDBs) store heuristic estimates that are used to improve the performance of heuristic search algorithms. They are key to the success of heuristic search in many application domains. While it is known [12] that the efiectiveness of PDBs critically depends on their size, current implementations use only small PDBs because they require random access to main memory. We present two MapReduce implementations that do not require random memory access and therefore enable larger PDBs than were previously possible. The first one, named MR-BFFS, is a parallel breadth-first frontier search. It is used for generating arbitrarily large PDBs out-of-core. The second one, MR-IDA*, uses out-of-core PDBs to perform a breadth-first iterative-deepening A* search. Both scale perfectly on massively parallel systems and they make use of all available resources like CPUs, distributed memories, and disks. We demonstrate the performance of our algorithms and provide, as a byproduct of this research, the first complete evaluation of dual additive PDBs for the 8-puzzle. We also provide data on larger problem spaces and discuss the efiectiveness of PDBs for improving the search. Copyright 2010 ACM.

关键词： MapReduce

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Exploiting constraints to build a flexible and extensible data stream processing middleware

Exploiting constraints to build a flexible and extensible da...

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2010 IEEE international symposium on parallel and distributed Processing, Workshops and Phd Forum, IPDPSW 2010

作者： Cipriani, Nazario Lübbe, Carlos Moosbrugger, Alexander Institute of Parallel and Distributed Systems Universität Stuttgart Universitätsstraße 38 70569 Stuttgart Germany

ISBN: (纸本)9781424465347

A wide range of real-time applications process stream-based data. To process this stream-based data in an application-independent manner, many stream processing systems have been built. However, none of them reached a huge domain of applications, such as databases did. This is due to the fact that they do not consider the specific needs of real-time applications. For instance, an application which visualizes stream-based data has stringent timing constraints, or may even need a specific hardware environment to smoothly process the data. Furthermore, users may even add additional constraints. E.g., for security reasons they may want to restrict the set of nodes that participates in processing. Thus, constraints naturally arise on different levels of query processing. In this work we classify constraints that occur on different levels of query processing. Furthermore we propose a scheme to classify the constraints and show how these can be integrated into the query processing of the distributed data stream middleware NexusDS. © 2010 IEEE.

关键词： Query processing

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proceedings of the 2010 IEEE international symposium on parallel and distributed Processing, Workshops and Phd Forum, IPDPSW 2010

Proceedings of the 2010 IEEE International Symposium on Para...

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2010 IEEE international symposium on parallel and distributed Processing, Workshops and Phd Forum, IPDPSW 2010

ISBN: (纸本)9781424465347

The proceedings contain 239 papers. The topics discussed include: characterizing heterogeneous computing environments using singular value decomposition;statistical predictors of computing power in heterogeneous clusters;a first step to the evaluation of SimGrid in the context of a real application;dynamic adaptation of DAGs with uncertain execution times in heterogeneous computing systems;robust resource allocation of DAGs in a heterogeneous multicore system;decentralized dynamic scheduling across heterogeneous multi-core desktop grids;a configurable-hardware document-similarity classifier to detect web attacks;a configurable high-throughput linear sorter system;hardware implementation for scalable lookahead regular expression detection;reducing grid energy consumption through choice of resource allocation method;and scheduling parallel tasks on multiprocessor computers with efficient power management.

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proceedings of the 2010 IEEE international symposium on parallel and distributed Processing, IPDPS 2010

Proceedings of the 2010 IEEE International Symposium on Para...

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24th IEEE international parallel and distributed Processing symposium, IPDPS 2010

ISBN: (纸本)9781424464432

The proceedings contain 128 papers. The topics discussed include: distributed advance network reservation with delay guarantees;a general algorithm for detecting faults under the comparison diagnosis model;broadcasting on large scale heterogeneous platforms under the bounded multi-port model;on the importance of bandwidth control mechanisms for scheduling on large scale heterogeneous platforms;scalable failure recovery for high-performance data aggregation;high performance comparison-based sorting algorithm on many-core GPUs;improving the performance of Uintah: a large-scale adaptive meshing computational framework;optimizing and tuning the fast multipole method for state-of-the-art multicore architectures;first experiences with congestion control in infiniband hardware;power-aware MPI task aggregation prediction for high-end computing systems;and a hybrid interest management mechanism for peer-to-peer networked virtual environments.

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Hypergraph-based task-bundle scheduling towards efficiency and fairness in heterogeneous distributed systems

Hypergraph-based task-bundle scheduling towards efficiency a...

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24th IEEE international parallel and distributed Processing symposium, IPDPS 2010

作者： Zhao, Han Liu, Xinxin Li, Xiaolin Scalable Software Systems Laboratory Department of Computer Science Oklahoma State University Stillwater OK 74078 United States

ISBN: (纸本)9781424464432

This paper investigates scheduling loosely coupled task-bundles in highly heterogeneous distributed systems. Two allocation quality metrics are used in pay-per-service distributed applications: efficiency in terms of social welfare, and fairness in terms of envy-freeness. The first contribution of this work is that we build a unified hypergraph scheduling model under which efficiency and fairness are compatible with each other. Second, in the scenario of budget-unawareness, we formulate a strategic algorithm design for distributed negotiations among autonomous self-interested computing peers and prove its convergence to complete local efficiency and envy-freeness. Third, we add budget limitation to the allocation problem and propose a class of hill-climbing heuristics in favor of different performance metrics. Finally we conduct extensive simulations to validate the performance of all the proposed algorithms. The results show that the decentralized hypergraph scheduling method is scalable, and yields desired allocation performance in various scenarios. © 2010 IEEE.

关键词： Scheduling

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Consistency in hindsight: A fully decentralized STM algorithm

Consistency in hindsight: A fully decentralized STM algorith...

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24th IEEE international parallel and distributed Processing symposium, IPDPS 2010

作者： Bieniusa, Annette Fuhrmann, Thomas Department of Computer Science University of Freiburg Germany Department of Computer Science Technical University of Munich Germany

ISBN: (纸本)9781424464432

Software transactional memory (STM) algorithms often rely on centralized components to achieve atomicity, isolation and consistency. In a distributed setting, centralized components are undesirable as they impair scalability. This paper presents Decent STM, a fully decentralized object-based STM algorithm. It relies on mostly immutable data structures, which are well-suited for replication and migration. It is the first decentralized STM implementing snapshot isolation semantics. A novel randomized consensus protocol guarantees consistency of the mutable parts. Transactions may proceed tentatively before consensus has been reached. Object versioning ensures consistency in hindsight. Thus, atomic code sections never block during execution. The evaluation of benchmarks shows that the guaranteed success of reads more than compensates for the higher conflict rate during commit. © 2010 IEEE.

关键词： distributed computer systems

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AuctionNet: Market oriented task scheduling in heterogeneous distributed environments

AuctionNet: Market oriented task scheduling in heterogeneous...

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2010 IEEE international symposium on parallel and distributed Processing, Workshops and Phd Forum, IPDPSW 2010

作者： Zhao, Han Li, Xiaolin Department of Computer Science Scalable Software Systems Laboratory Oklahoma State University Stillwater OK 74078 United States

ISBN: (纸本)9781424465347

We propose a suite of market-oriented task scheduling algorithms to build an AuctionNet for heterogeneous distributed environments. In heterogeneous distributed environments, computing nodes are autonomous and owned by different organizations, for example peer-to-peer systems, desktop grids/clouds. To address such diverse heterogeneity and dynamism in systems, applications, and local policies, efficient and fair task scheduling becomes a challenging issue. To cope with such complexity in a distributed and noncooperative environment, we propose to use market-oriented incentive mechanisms to regulate task scheduling in a distributed manner. Further, to accommodate multiple objectives and criteria, we adopt a combined approach leveraging the advantage of both hypergraph theory and incentive mechanisms. We first formulate a general framework of market-oriented task scheduling in distributed systems. We then present two algorithms for task-bundle scheduling. Preliminary results demonstrate the satisfactory performance of our proposed algorithms. The remaining work to complete the PhD dissertation is then presented. The proposed research carries significant intellectual merits and potential broader impacts in the following aspects. (1) We propose the notion of task-bundle for the first time in the literature. Product-bundle has been a common marketing strategy in our daily life for a long time. In the emerging commercial louds and desktop clouds, task-bundle could be a useful concept for computing and storage markets. (2) We propose efficient distributed mechanisms that are very suitable for such distributed systems. A novel algorithm combining hypergraph and incentive mechanisms achieves multi-objective optimization. (3) We conduct rigorous analytical study and prove that our algorithms ensure efficiency and fairness and in the meantime maximize social welfare. (4) Overall, this proposal lays a solid foundation and sheds light on future research and realworld application

关键词： Multiobjective optimization

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PV-EASY: A strict fairness guaranteed and prediction enabled scheduler in parallel job scheduling 10

PV-EASY: A strict fairness guaranteed and prediction enabled...

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proceedings of the 19th ACM international symposium on High Performance distributed Computing

作者： Yuan, Yulai Yang, Guangwen Wu, Yongwei Zheng, Weimin Department of Computer Science and Technology Tsinghua University Beijing 100084 China

ISBN: (纸本)9781605589428

As the most widely used parallel job scheduling strategy in production schedulers, EASY has achieved great success, not only because it can balance fairness and performance, but also because it is universally applicable to most HPC systems. However, unfairness still exists in EASY. For real workloads used in this work, our simulation shows that a blocked job can be delayed by later jobs for more than 90 hours. In addition, EASY cannot directly employ parallel job runtime prediction techniques, because this would lead to a serious situation called reservation violation. In this paper, we aim at guaranteeing strict fairness (no job is delayed by any jobs of lower priority) while achieving attractive performance, and employing prediction without causing reservation violation in parallel job scheduling. We propose two novel strategies, shadow load preemption (SLP) and venture backfilling (VB), which are together integrated into EASY to construct a preemptive venture EASY backfilling (PV-EASY) strategy. Experimental results on three workloads of real HPC systems demonstrate that: first, PV-EASY guarantees strict fairness, in addition to avoiding reservation violation when employing job runtime prediction techniques in scheduling;Second, PV-EASY achieves the same performance as EASY, and outperforms prediction employed EASY;Third, the preemption in PV-EASY is not resource costly and simple enough to be implemented in all HPC systems where EASY works. These advantages make PV-EASY more attractive than EASY in parallel job scheduling, both from academic and industry perspectives. Copyright 2010 ACM.

关键词： Forecasting

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