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The p-index: Ranking Scientists Using Network Dynamics

Procedia Computer Science 2014年 29卷 465-477页

作者： U. Senanayake M. Piraveenan A.Y. Zomaya Center for Complex Systems Research The University of Sydney Australia Centre for Distributed and High Performance Computing The University of Sydney Australia

The indices currently used by scholarly databases, such as Google scholar, to rank scientists, do not attach weights to the citations. Neither is the underlying network structure of citations considered in computing these metrics. This results in scientists cited by well-recognized journals not being rewarded, and may lead to potential misuse if documents are created purely to cite others. In this paper we introduce a new ranking metric, the p-index (pagerank-index), which is computed from the underlying citation network of papers, and uses the pagerank algorithm in its computation. The index is a percentile score, and can potentially be implemented in public databases such as Google scholar, and can be applied at many levels of abstraction. We demonstrate that the metric aids in fairer ranking of scientists compared to h-index and its variants. We do this by simulating a realistic model of the evolution of citation and collaboration networks in a particular field, and comparing h-index and p-index of scientists under a number of scenarios. Our results show that the p-index is immune to author behaviors that can result in artificially bloated h-index values.

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WPress: An Application-Driven performance Benchmark for Cloud-Based Virtual Machines

WPress: An Application-Driven Performance Benchmark for Clou...

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International Conference on Enterprise distributed Object computing (EDOC)

作者： Amir Hossein Borhani Philipp Leitner Bu-Sung Lee Xiaorong Li Terence Hung Institute of High-Performance Computing (IHPC) Singapore s.e.a.l. software evolution & architecture lab University of Zurich Switzerland Parallel and Distributed Computing Center (PDCC) Nanyang Technological University Singapore

ISBN: (纸本)9781479954711

Approaching a comprehensive performance benchmark for on-line transaction processing (OLTP) applications in a cloud environment is a challenging task. Fundamental features of clouds, such as the pay-as-you-go pricing model and unknown underlying configuration of the system, are contrary to the basic assumptions of available benchmarks such as TPC-W or RUBiS. In this paper, we introduce a systematic performance benchmark approach for OLTP applications on public clouds that use virtual machines(VMs). We propose WPress benchmark, which is based on the widespread blogging software, WordPress, as a representative OLTP application and implement an open source workload generator. Furthermore, we utilize a CPU micro-benchmark to investigate CPU performance of cloud-based VMs in greater detail. Average response time and total VM cost are the performance metrics measured by WPress. We evaluate small and large instance types of three real-life cloud providers, Amazon EC2, Microsoft Azure and Rackspace cloud. Results imply that Rackspace cloud has better average response times and total VM cost on small instances. However, Microsoft Azure is preferable for large instance type.

关键词： Benchmark testing Cloud computing Time factors Servers Databases Blogs Publishing

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Local Resource Shaper for MapReduce

Local Resource Shaper for MapReduce

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IEEE International Conference on Cloud computing Technology and Science (CloudCom)

作者： Peng Lu Young Choon Lee Vincent Gramoli Luke M. Leslie Albert Y. Zomaya Center for Distributed and High Performance Computing The University of Sydney Australia School of Information Technologies NICTA and the University of Sydney Australia Department of Computer Science University of Illinois at Urbana-Champaign USA

Resource capacity is often over provisioned to primarily deal with short periods of peak load. Shaping these peaks by shifting them to low utilization periods (valleys) is referred to as "resource consumption shaping". While originally aimed at the data center level, the resource consumption shaping we consider focuses on local resources, like CPU or I/O as we have identified that individual jobs also incur load peaks and valleys on these resources. In this paper, we present Local Resource Shaper (LRS), which limits fairness in resource sharing between co-located MapReduce tasks. LRS enables Hadoop to maximize resource utilization and minimize resource contention independently of job type. Co-located MapReduce tasks are often prone to resource contention (i.e., Load peak) due to similar resource usage patterns particularly with traditional fair resource sharing. In essence, LRS differentiates co-located tasks through active and passive slots that serve as containers for interchangeable map or reduce tasks. LRS lets an active slot consume as much resources as possible, and a passive slot make use of any unused resources. LRS leverages such slot differentiation with its new scheduler, Interleave. Our results show that LRS always outperforms the best static slot configuration with three Hadoop schedulers in terms of both resource utilization and performance.

关键词： Resource management Scheduling Benchmark testing Educational institutions Heart beat Containers Linux

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A mathematical model for empowerment of Beowulf clusters for exascale computing

A mathematical model for empowerment of Beowulf clusters for...

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2013 11th International Conference on high performance computing and Simulation, HPCS 2013

作者： Mirtaheri, Seyedeh Leili Khaneghah, Ehsan Mousavi Grandinetti, Lucio Sharifi, Mohsen Center of High Performance Computing for Parallel and Distributed Processing University of Calabria Rende Italy School of Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9781479908363

high-performance computing (HPC) clusters are currently faced with two major challenges - namely, the dynamic nature of new generation of applications and the heterogeneity of platforms - if they are going to be useful for exascale computing. Processes running these applications may well demand unpredictable requirements and changes to system configuration and capabilities at runtime, thereby requiring fast system response without sacrificing the transparency and integrity of the reconfigured empowered system that is running on a heterogeneous platform. While a challenge in and of itself, platform heterogeneity is both useful and instrumental in the handling of unpredictable requests. The realization of such a dynamically reconfigurable and heterogeneous HPC cluster system for exascale computing requires a model to guide running processes to determine if they need empowerment of the current cluster, and if yes, by how much. To show the feasibility of empowerment of traditional HPC clusters for exascale computing, we have selected Beowulf as a noble candidate cluster and present a mathematical model for the empowerment of Beowulf clusters for exascale computing (EBEC). We have developed the model in line with Beowulf's cluster approach and by using vector space algebra. In contrast to traditional hardware-oriented approaches to improvise the performance of clusters, we use a software approach to the development of the proposed model by emphasizing processes, which act as the creators of the cluster and thus should decide on system (re)configuration, as the principal building blocks of the system. We have also adopted a new approach to heterogeneity by considering heterogeneity at different levels including hardware, system software, application software, and system functionality. In addition to support for heterogeneity and dynamic reconfiguration, the proposed model includes support for scalability that is crucial to exascale computing too. © 2013 IEEE.

关键词： Vector spaces

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A Dynamic Replication Mechanism to Reduce Response-Time of I/O Operations in high performance computing Clusters

A Dynamic Replication Mechanism to Reduce Response-Time of I...

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IEEE International Conference on Social computing (SocialCom)

作者： Ehsan Mousavi Khaneghah Seyedeh Leili Mirtaheri Lucio Grandinetti Amir Saman Memaripour Mohsen Sharifi Center of High Performance Computing for Parallel and Distributed Processing University of Calabria Rende Italy School of Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9781479915194

Extraordinary large datasets of high performance computing applications require improvement in existing storage and retrieval mechanisms. Moreover, enlargement of the gap between data processing and I/O operations' throughput will bound the system performance to storage and retrieval operations and remarkably reduce the overall performance of high performance computing clusters. File replication is a way to improve the performance of I/O operations and increase network utilization by storing several copies of every file. Furthermore, this will lead to a more reliable and fault-tolerant storage cluster. In order to improve the response time of I/O operations, we have proposed a mechanism that estimates the required number of replicas for each file based on its popularity. Besides that, the remaining space of storage cluster is considered in the evaluation of replication factors and the number of replicas is adapted to the storage state. We have implemented the proposed mechanism using HDFS and evaluated it using MapReduce framework. Evaluation results prove its capability to improve the response time of read operations and increase network utilization. Consequently, this mechanism reduces the overall response time of read operations by considering files' popularity in replication process and adapts the replication factor to the cluster state.

关键词： Time factors high performance computing Bandwidth Reliability Throughput System performance History

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A mathematical model for empowerment of Beowulf clusters for exascale computing

A mathematical model for empowerment of Beowulf clusters for...

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International Conference on high performance computing & Simulation (HPCS)

作者： Seyedeh Leili Mirtaheri Ehsan Mousavi Khaneghah Lucio Grandinetti Mohsen Sharifi Center of High Performance Computing for Parallel and Distributed Processing University of Calabria Rende Italy School of Computer Engineering Iran University of Science and Technology Tehran Iran

关键词： Vectors Mathematical model Hardware Runtime Computers Computational modeling

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Throughput Enhancement Through Selective Time Sharing and Dynamic Grouping

Throughput Enhancement Through Selective Time Sharing and Dy...

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IEEE International Parallel & distributed Processing Symposium

作者： Junliang Chen Bing Bing Zhou Chen Wang Peng Lu Penghao Wang Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Information Technologies The University of Sydney NSW 2006 Australia CSIRO ICT Center PO Box 76 Epping NSW 1710Australia Prince of Wales Clinical School The University of New South Wales NSW 2052 Australia

ISBN: (纸本)9781467360661

Space sharing approaches are widely used in job scheduling for HPC systems. The main drawback of these approaches is the blocking of short jobs, which results in low throughput. The research on gang scheduling has shown the potential of time sharing in improving throughput. However, traditional gang scheduling adds jobs for time sharing without selection, which may cause a higher performance degradation of existing running jobs than the performance gain of waiting jobs. Moreover, gang scheduling often adopts a contiguous buddy allocation scheme which has problems of fragmentation and low resource utilization. We design a selective time sharing technique that allows waiting jobs to be co-scheduled with existing running jobs only if the overall throughput can be improved. To alleviate the fragmentation problem, we present a dynamic grouping resource allocation mechanism that relaxes the contiguous allocation requirement imposed on gang scheduling. By integrating these techniques, our new job co-scheduling algorithm is able to simultaneously take system throughput and resource utilization into consideration. The experimental results demonstrate that our approach significantly outperforms both EASY backfilling and traditional gang scheduling in terms of both average turnaround time and bounded slowdown.

关键词： job scheduling time sharing resource allocation

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Workload Characteristic Oriented Scheduler for MapReduce

Workload Characteristic Oriented Scheduler for MapReduce

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International Conference on Parallel and distributed Systems (ICPADS)

作者： Peng Lu Young Choon Lee Chen Wang Bing Bing Zhou Junliang Chen Albert Y. Zomaya Center for Distributed and High Performance Computing The University of Sydney NSW Australia CSIRO ICT Center Epping NSW Australia

Applications in many areas are increasingly developed and ported using the Map Reduce framework (more specifically, Hadoop) to exploit (data) parallelism. The application scope of Map Reduce has been extended beyond the original design goal which was large-scale data processing. This extension inherently makes a need for scheduler to explicitly take into account characteristics of job for two main goals of efficient resource use and performance improvement. In this paper, we study Map Reduce scheduling strategies to effectively deal with different workload characteristics CPU intensive and I/O intensive. We present the Workload Characteristic Oriented Scheduler (WCO), which strives for co-locating tasks of possibly different Map Reduce jobs with complementing resource usage characteristics. WCO is characterized by its essentially dynamic and adaptive scheduling decisions using information obtained from its characteristic estimator. Workload characteristics of tasks are primarily estimated by sampling with the help of some static task selection strategies, e.g., Java byte code analysis. Results obtained from extensive experiments using 11 benchmarks in a 4-node local cluster and a 51-node Amazon EC2 cluster show 17% performance improvement on average in terms of throughput in the situation of co-existing diverse workloads.

关键词： Benchmark testing Heart beat Throughput Resource management Estimation Dynamic scheduling

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Network Load Analysis and Provisioning of MapReduce Applications

Network Load Analysis and Provisioning of MapReduce Applicat...

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IEEE International Conference on Parallel and distributed computing, Applications and Technologies (PDCAT)

作者： Nikzad Babaii Rizvandi Javid Taheri Reza Moraveji Albert Y. Zomaya The University of Sydney Sydney NSW AU Center for Distributed and High Performance Computing School of Information Technologies University of Sydney Sydney Australia

ISBN: (纸本)9781467357043

In this paper, we study the dependency between MapReduce configuration parameters and network load of fixed-size MapReduce jobs during the shuffle phase, then we propose an analytical method to model this dependency. Our approach consists of three key phases: profiling, modeling, and prediction. In the first stage, an application is run several times with different sets of MapReduce configuration parameters (here number of map tasks and number of reduce tasks) to profile the network load of an application in the shuffle phase on a given cluster. Then, the relation between these parameters and the network load is modeled by multivariate linear regression. For evaluation, three applications (Word Count, Exim Main log parsing, and TeraSort) are utilized to evaluate our technique on a 5-node MapReduce private cluster.

关键词： Load modeling Predictive models Accuracy Computational modeling Mathematical model distributed computing Data models

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A science gateway getting ready for serving the international molecular simulation community 2

A science gateway getting ready for serving the internationa...

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2012 EGI Community Forum / EMI 2nd Technical Conference, EGICF-EMITC 2012

作者： Gesing, Sandra Herres-Pawlis, Sonja Birkenheuer, Georg Brinkmann, André Grunzke, Richard Kacsuk, Peter Kohlbacher, Oliver Kozlovszky, Miklos Krüger, Jens Müller-Pfefferkorn, Ralph Schäfer, Patrick Steinke, Thomas Center for Bioinformatics Department of Computer Science University of Tübingen Sand 14 Tübingen72076 Germany Department of Chemistry Ludwig-Maximilians-University Munich Butenandtstr. 5-13 München81377 Germany Paderborn Center for Parallel Computing University of Paderborn Warburger Str. 100 Paderborn33089 Germany Johannes Gutenberg-University Mainz Mainz55099 Germany Center for Information Services and High Performance Computing Technische Universität Dresden Zellescher Weg 12-14 Germany Laboratory of Parallel and Distributed Systems MTA SZTAKI Kende Street 13-17 Budapest1111 Hungary Zuse Institute Berlin Takustraße 7 Berlin14195 Germany

The project MoSGrid (Molecular Simulation Grid) has been developing a web-based science gateway supporting the community with various services for quantum chemistry, molecular modeling, and docking. Users gain access to distributed computing infrastructures (DCIs) via intuitive user interfaces for sophisticated tools, specialized workflows, and distributed repositories. Currently, the MoSGrid community consists of about 120 users from a number of fields related to chemistry and bioinformatics located in Germany. However, the underlying security infrastructure is generally applicable and can be deployed in arbitrary projects. MoSGrid intends to address the international community by participating in the EU-projects SCI-BUS (Scientific gateway Based User Support) and ER-flow (Building an European Research Community through Interoperable Workflows and Data), and collaborating with the EU-project EDGI (European Desktop Grid Initiative). © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike Licence.

关键词： Quantum chemistry

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