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On-Line Optimization of Publish/Subscribe Overlays

On-Line Optimization of Publish/Subscribe Overlays

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

作者： Eddy Caron Benjamin Depardon Ajoy K. Datta Lawrence L. Larmore LIP Laboratory-UMR CNRS ENS Lyon-INRIA-UCBL 5668 University of Lyon I France University of Nevada Las Vegas USA

Loosely coupled applications can take advantage of the publish/subscribe communication paradigm. In this latter, subscribers declare which events, or which range of events, they wish to monitor, and are asynchronously informed whenever a publishers throws an event. In such a system, when a publication occurs, all peers whose subscriptions contain the publication must be informed. In our approach, the subscriptions are represented by a DR-tree, which is an R-tree where each minimum bounding rectangle is supervised by a peer. Instead of attempting to statically optimize the DR-tree, we give an on-line algorithm, the work function algorithm, which continually changes the DR-tree in response to the sequence of publications, in attempt to dynamically optimize the structure. The competitiveness of this algorithm is computed to be at most 5 for any example where there are at most three subscriptions and the R-tree has height 2. The benefit of the on-line approach is that no prior knowledge of the distribution of publications in the attribute space is needed.

关键词： Subscriptions Peer to peer computing Heuristic algorithms Routing Algorithm design and analysis Extraterrestrial measurements

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Reducing Download Times in Peer-to-Peer File Sharing Systems with Stochastic Service Capacities

Reducing Download Times in Peer-to-Peer File Sharing Systems...

引用

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

作者： Keqin Li Department of Computer Science State University of New York New Paltz NY USA

The main problem for an individual user peer in a peer-to-peer network with heterogeneous source peers is the peer selection problem, namely, switching among source peers and finally settling on one, while keeping the total time of probing and downloading to a minimum. There has been little investigation on selecting source peers with stochastic service capacities. The main contribution of this paper is to address the problem of reducing download times in peer-to-peer file sharing systems with stochastic service capacities. A precise analysis of the expected download time is given when the service capacity of a source peer is a random variable. A chunk-based switching and peer selection algorithm using the method of probing high-capacity peers is proposed and the expected download time of the algorithm is analyzed. Two sub problems of the optimal choice of the threshold of high-capacity source peers and the optimal order of probing are also solved. The performance of the algorithm is compared with the random chunk-based switching method. It is shown that noticeable performance improvement can be obtained.

关键词： Algorithm design and analysis Switches Peer to peer computing Random variables Stochastic processes Probes Capacity planning

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Tuple Spaces in Hardware for Accelerated Implicit Routing

Tuple Spaces in Hardware for Accelerated Implicit Routing

引用

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

作者： Zachary K. Baker Justin L. Tripp Los Alamos National Laboratory Los Alamos NM USA

Organizing and optimizing data objects on networks with support for data migration and failing nodes is a complicated problem to handle as systems expand to hundreds of thousands of nodes. The goal of this work is to demonstrate that high levels of speedup can be achieved by moving responsibility for finding, fetching, and staging data into an FPGA-based network interface. We present a system for implicit routing of data via FPGA-based network cards. In this system, data structures are requested by name, and the network cooperatively finds the data and returns the information to the requester. This is achieved through successive examination of hardware hash tables implemented in the individual FPGA network cards. By avoiding the complex network software stacks between nodes, the data is quickly transferred entirely through FPGA-FPGA interaction. The performance of this system is approximately 26× faster vs. the software network on a per-node basis. This is due to the improved speed of the hash tables, higher levels of network abstraction and lowered latency between the network nodes.

关键词： Hardware Random access memory Field programmable gate arrays Peer to peer computing Graphics processing unit Routing Laboratories

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Predicting Node Failure in High Performance computing Systems from Failure and Usage Logs

Predicting Node Failure in High Performance Computing System...

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

作者： Nithin Nakka Ankit Agrawal Alok Choudhary Coordinated Science Laboratory University of Illinois Urbana-Champaign Urbana IL USA Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA

In this paper, we apply data mining classification schemes to predict failures in a high performance computer system. Failure and Usage data logs collected on supercomputing clusters at Los Alamos National Laboratory (LANL) were used to extract instances of failure information. For each failure instance, past and future failure information is accumulated -- time of usage, system idle time, time of unavailability, time since last failure, time to next failure. We performed two separate analyses, with and without classifying the failures based on their root cause. Based on this data, we applied some popular decision tree classifiers to predict if a failure would occur within 1 hour. Our experiments show that our prediction system predicts failures with a high-degree of precision up to 73% and recall of about 80%. We also observed that employing the usage data along with the failure data has improved the accuracy of prediction.

关键词： Databases Data mining Maintenance engineering Hardware Humans Program processors

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A History-Based Performance Prediction Model with Profile Data Classification for Automatic Task Allocation in Heterogeneous computing Systems

A History-Based Performance Prediction Model with Profile Da...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Katsuto Sato Kazuhiko Komatsu Hiroyuki Takizawa Hiroaki Kobayashi Graduate School of Information Sciences University of Tohoku Sendai Japan Cyberscience Center University of Tohoku Sendai Japan

In this paper, we propose a runtime performance prediction model for automatic selection of accelerators to execute kernels in OpenCL. The proposed method is a history-based approach that uses profile data for performance prediction. The profile data are classified into some groups, from each of which its own performance model is derived. As the execution time of a kernel depends on some runtime parameters such as kernel arguments, the proposed method first identifies parameters affecting the execution time by calculating the correlation between each parameter and the execution time. A parameter with weak correlation is used for the classification of the profile data and the selection of the performance prediction model. A parameter with strong correlation is used for building a linear model for the prediction of the kernel execution time by using only the classified profile data. Experimental results clearly indicate that the proposed method can achieve more accurate performance prediction than conventional history-based approaches because of the profile data classification.

关键词： Kernel Predictive models Runtime Correlation Accuracy Computational modeling Instruction sets

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distributed B&B: A Pure Peer-to-Peer Approach

Distributed B&B: A Pure Peer-to-Peer Approach

引用

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

作者： Mathieu Djamaï Bilel Derbel Nouredine Melab Laboratoire d'Informatique Fondamentale de Lille (LIFL) Université des Sciences et Technologies de Lille Lille France

The state-of-the-art large scale approach for solving NP-hard permutation-based problems using parallel Branch-and-Bound (B&B) techniques is based on a Master-Slave model which is known to be limited in terms of scalability. In this paper, we present a new Peer-to-Peer (P2P) approach that can handle a huge amount of computational resources in a fully distributed way, that is without the need of any centralized coordinator. To achieve that, we propose simple and efficient fully distributed algorithms dealing with major parallel B&B issues such as work sharing, dynamic load balancing and termination detection. We argue that our P2P approach has a scalability which is exponentially better in theory compared to the Master-Slave technique while having a negligible communication overhead in a worst case-scenario, namely poly-logarithmic. The approach has been implemented and experimented using the Grid'5000 nation-wide French grid. Through extensive simulations involving up to 150 000 peers, we show that, compared to the state-of-the-art Master-Slave technique, our P2P approach enables (i) to improve the parallel efficiency up to a ratio of 6 to 1, (ii) to significantly speed up the B&B search process, namely by up to 7 factor in terms of number of solutions explored in the search space and, (iii) to keep the communication overhead relatively low, namely by a factor of at most 11 without penalizing the search process.

关键词： Peer to peer computing Master-slave Scalability Space exploration Redundancy Algorithm design and analysis Computational modeling

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SC-OA: A Secure and Efficient Scheme for Origin Authentication of Interdomain Routing in Cloud computing Networks

SC-OA: A Secure and Efficient Scheme for Origin Authenticati...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Zhongjian Le Naixue Xiong Bo Yang Yuezhi Zhou School of Management Huazhong University of Science and Technology Wuhan China School of Information Technology Jiangxi University of Finance and Economics Nanchang China Department of Computer Science Georgia State University Atlanta GA USA Department of Computer Science and Technology Tsinghua University Beijing China

IP prefix hijacking is one of the top threats in the cloud computing Internets. Based on cryptography, many schemes for preventing prefix hijacks have been proposed. Securing binding between IP prefix and its owner underlies these schemes. We believe that a scheme for securing this binding should try to satisfy these seven critical requirements: no key escrow, no other secure channel, defending against Malicious Key Issuer (MKI) in the phase of prefix announcement, defending against MKI in the phase of key issuing, no certificate, in-band delegation attestation, and in-band public key witness. In this paper, we propose a new scheme, Origin Authentication based on Self-Certified public keys (SC-OA), using self-certified public keys to authenticate origin autonomous systems. To the best of our knowledge, it is the first work for securing prefix ownership using self-certified public keys to achieve an efficient and secure scheme that satisfies all seven requirements. The analyses show that SC-OA can defend against regular prefix, sub prefix, unassigned prefix, interception-based, and MKI hijacking, and improve performance in many aspects. It will be pushed ahead to practical deployment for preventing prefix hijacks.

关键词： Public key Protocols Organizations IP networks Authentication Computer architecture Educational institutions

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PHAST: Hardware-Accelerated Shortest Path Trees

PHAST: Hardware-Accelerated Shortest Path Trees

引用

international symposium on parallel and distributed Processing (IPDPS)

作者： Daniel Delling Andrew V. Goldberg Andreas Nowatzyk Renato F. Werneck Microsoft Research Silicon Valley CA USA

We present a novel algorithm to solve the nonnegative single-source shortest path problem on road networks and other graphs with low highway dimension. After a quick preprocessing phase, we can compute all distances from a given source in the graph with essentially a linear sweep over all vertices. Because this sweep is independent of the source, we are able to reorder vertices in advance to exploit locality. Moreover, our algorithm takes advantage of features of modern CPU architectures, such as SSE and multi-core. Compared to Dijkstra's algorithm, our method needs fewer operations, has better locality, and is better able to exploit parallelism at multi-core and instruction levels. We gain additional speedup when implementing our algorithm on a GPU, where our algorithm is up to three orders of magnitude faster than Dijkstra's algorithm on a high-end CPU. This makes applications based on all-pairs shortest-paths practical for continental-sized road networks. Several algorithms, such as computing the graph diameter, exact arc flags, or centrality measures (exact reaches or betweenness), can be greatly accelerated by our method.

关键词： Graphics processing unit Instruction sets Roads Registers parallel processing Random access memory Arrays

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Watershed: A High Performance distributed Stream Processing System

Watershed: A High Performance Distributed Stream Processing ...

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international symposium on Computer Architecture and High Performance computing (SBAC-PAD)

作者： Thatyene Louise Alves de Souza Ramos Rodrigo Silva Oliveira Ana Paula de Carvalho Renato Antonio Celso Ferreira Wagner Meira Jr. Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte Brazil

The task of extracting information from datasets that become larger at a daily basis, such as those collected from the web, is an increasing challenge, but also provides more interesting insights and analysis. Current analyses went beyond content and now focus on tracking and understanding users' relationships and interactions. Such computation is intensive both in terms of the processing demand imposed by the algorithms and also the sheer amount of data that has to handled. In this paper we introduce Watershed, a distributed computing framework designed to support the analysis of very large data streams online and in real-time. Data are obtained from streams by the system's processing components, transformed, and directed to other streams, creating large flows of information. The processing components are decoupled from each other and their connections are strictly data-driven. They can be dynamically inserted and removed, providing an environment in which it is feasible that different applications share intermediate results or cooperate to a global purpose. Our experiments demonstrate the flexibility in creating a set of data analysis algorithms and their composition into a powerful stream analysis environment.

关键词： parallel processing Libraries XML distributed databases Computer architecture Data analysis

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Automated, parallel Optimization Algorithms for Stochastic Functions

Automated, Parallel Optimization Algorithms for Stochastic F...

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

作者： Dheeraj Chahal Steven J. Stuart Sebastian Goasguen Colin J. Trout School of Computing Clemson University Clemson SC USA Department of Chemistry Clemson University Clemson SC USA Department of Chemistry Saint Francis University Loretto PA USA

We propose a hierarchical framework and new parallel algorithms for stochastic function optimization under conditions where the function to be optimized is subject to random noise, the variance of which decreases with sampling time. This is the situation expected for many real-world and simulation applications where results are obtained from sampling, and contain experimental error or random noise. Our new optimization algorithms are based on a downhill simplex algorithm, with extensions that alter the timing of simplex operations based on the level of noise in the function evaluations. Three proposed optimization methods, which we term maxnoise, point-to-point comparison, and a combination of these two, are tested on the Rosenbrock function and found to be better than previous stochastic optimization methods. The parallel framework implementing the optimization algorithms is also new, and is based on a master-worker architecture where each worker runs a massively parallel program. The parallel implementation allows the sampling to proceed independently on multiple processors, and is demonstrated to scale well up to over 100 vertices. It is highly suitable for clusters with an ever increasing number of cores per node. The new methods have been applied successfully to the reparameterization of the TIP4P water model, achieving thermodynamic and structural results for liquid water that are as good as or better than the original model, with the advantage of a fully automated parameterization process.

关键词： Noise Computational modeling Noise measurement Manganese Stochastic processes Optimization methods

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