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Intelligent distributed Computing VI: Proceedings of the 6th international Symposium on Intelligent distributed Computing - IDC 2012, Calabria, Italy, ... 2012

引用

2014年

作者： Giancarlo Fortino Costin Bădică Michele Malgeri Rainer Unland

ISBN: (纸本)9783642439346

This book represents the combined peer-reviewed proceedings of the Sixth international Symposium on Intelligent distributed Computing -- IDC~2012, of the international workshop on Agents for Cloud -- A4C~2012 and of the Fourth international workshop on Multi-Agent systems Technology and Semantics -- MASTS~2012. All the events were held in Calabria, Italy during September 24-26, *** 37 contributions published in this book address many topics related to theory and applications of intelligent distributed computing and multi-agent systems, including: adaptive and autonomousdistributed systems, agent programming, ambient assistedliving systems, business process modeling and verification, cloudcomputing, coalition formation, decision support systems, distributed optimization and constraint satisfaction, gesture recognition, intelligent energy management in WSNs, intelligent logistics, machine learning, mobile agents, parallel and distributed computational intelligence, parallel evolutionary computing, trust metrics and security, scheduling in distributed heterogenous computing environments, semantic Web service composition, social simulation, and software agents for WSNs.

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Reducing Energy Costs for IBM Blue Gene/P via Power-Aware Job scheduling 1

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17th international workshop on Job scheduling Strategies for parallel Processing (JSSPP)

作者： Zhou, Zhou Lan, Zhiling Tang, Wei Desai, Narayan Illinois Inst Technol Dept Comp Sci Chicago IL 60616 USA Argonne Natl Lab Math & Comp Sci Div Argonne IL USA

ISBN: (数字)9783662437797

ISBN: (纸本)9783662437797;9783662437780

Energy expense is becoming increasingly dominant in the operating costs of high-performance computing (HPC) systems. At the same time, electricity prices vary significantly at different times of the day. Furthermore, job power profiles also differ greatly, especially on HPC systems. In this paper, we propose a smart, power-aware job scheduling approach for HPC systems based on variable energy prices and job power profiles. In particular, we propose a 0-1 knapsack model and demonstrate its flexibility and effectiveness for scheduling jobs, with the goal of reducing energy cost and not degrading system utilization. We design scheduling strategies for Blue Gene/P, a typical partition-based system. Experiments with both synthetic data and real job traces from production systems show that our power-aware job scheduling approach can reduce the energy cost significantly, up to 25%, with only slight impact on system utilization.

关键词： Energy Power-aware job scheduling resource management Blue Gene HPC system

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Data Avenue: Remote Storage resource management in WS-PGRADE/gUSE

Data Avenue: Remote Storage Resource Management in WS-PGRADE...

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6th international workshop on Science Gateways (IWSG)

作者： Hajnal, Akos Farkas, Zoltan Kacsuk, Peter Hungarian Acad Sci Inst Comp Sci & Control Lab Parallel & Distributed Syst Budapest Hungary

ISBN: (纸本)9781479958191

State-of-the-art gateways are connected to several distributed computing infrastructures (DCIs) and are able to run jobs and workflows simultaneously in all those different DCIs. However, the flexibility of accessing data storages belonging to different DCIs is a missing feature of current gateways. SZTAKI (Institute for Computer Science and Control) has developed a Data Avenue Blacktop service and a Liferay-based Data Avenue portlet that open the door for integrating such features into science gateways. The paper explains the design considerations of the Data Avenue Blacktop service and its usage scenarios in science gateways through the Data Avenue portlet.

关键词： data storage systems data handling data transfer grid computing

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20th international Conference on parallel Processing, Euro-Par 2014

20th International Conference on Parallel Processing, Euro-P...

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20th international Conference on parallel Processing, Euro-Par 2014

ISBN: (纸本)9783319098722

The proceedings contain 68 papers. The special focus in this conference is on Support tools environments, Performance prediction and evaluation, scheduling and load balancing, High performance architectures and compilers, parallel and distributed data management, Grid, Cluster and cloud computing, Green high performance computing, distributed systems and algorithms, parallel and distributed programming, parallel numerical algorithms, Multicore and manycore programming, Theory and algorithms for parallel computation and High performance networks and communication. The topics include: MPI trace compression using event flow graphs;customized scalable tracing with in-situ data analysis;performance measurement and analysis of transactional memory and speculative execution on IBM blue Gene/Q;an open-source management framework for cloud applications;modeling and simulation of a dynamic task-based runtime system for heterogeneous multi-core architectures;modeling the impact of reduced memory bandwidth on HPC applications;finding the important basic blocks in multithreaded programs;optimization and trade-off analysis for time, energy and resource usage;performance prediction and evaluation of parallel applications in KVM, Xen, and VMware;per-task DRAM energy metering in multicore systems;characterizing the performance-energy tradeoff of small ARM cores in HPC computation;finding efficient queue setup using high-resolution simulations;a progressively pessimistic scheduler for software transactional memory;a queueing theory approach to pareto optimal bags-of-tasks scheduling on clouds;scheduling/placement approach for task-graphs on heterogeneous architecture;energy-aware multi-organization scheduling problem;energy efficient scheduling of mapreduce jobs and switchable scheduling for runtime adaptation of optimization.

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Utility Driven Dynamic resource management in an Oversubscribed Energy-Constrained Heterogeneous System 28

Utility Driven Dynamic Resource Management in an Oversubscri...

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28th IEEE international parallel & distributed Processing Symposium workshops (IPDPSW)

作者： Khemka, Bhavesh Friese, Ryan Pasricha, Sudeep Maciejewski, Anthony A. Siegel, Howard Jay Koenig, Gregory A. Powers, Sarah Hilton, Marcia Rambharos, Rajendra Poole, Steve Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA Dept Def Washington DC 20001 USA

ISBN: (纸本)9781479941162

In this paper, we address the problem of scheduling dynamically-arriving tasks to machines in an oversubscribed heterogeneous computing environment. Each task has a monotonically decreasing utility function associated with it that represents the utility (or value) based on the task's completion time. Our system model is designed based on the environments of interest to the Extreme Scale systems Center at Oak Ridge National Laboratory. The goal of our scheduler is to maximize the total utility earned from task completions while satisfying an energy constraint. We design an energy-aware heuristic and compare its performance to heuristics from the literature. We also design an energy filtering technique for this environment that is used in conjunction with the heuristics. The filtering technique adapts to the energy remaining in the system and estimates a fair-share of energy that a task's execution can consume. The filtering technique improves the performance of all the heuristics and distributes the consumption of energy throughout the day. Based on our analysis, we recommend the level of filtering to maximize the performance of scheduling techniques in an oversubscribed environment.

关键词： scheduling energy-constrained utility functions energy filtering

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Research on Job-shop scheduling Algorithm of Single Piece and Small Batch MES 8

Research on Job-shop Scheduling Algorithm of Single Piece an...

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8th international Conference on Future Generation Communication and Networking FGCN

作者： Huang Tao Tan Yanna Harbin Univ Sci & Technol Harbin Peoples R China China United Network Commun Corp Harbin Branch Harbin Peoples R China

ISBN: (纸本)9781479977802

Manufacturing execution system MES is a workshop for the production of management information systems, but also to achieve efficient production, saving companies the cost basis. MES job shop scheduling problem is the core module, and its essence is a kind of resource constraints, time constraints and process constraints such as combinatorial optimization problems, research and application of job shop scheduling problem for China's manufacturing sector improve management, productivity, and the implementation of advanced manufacturing strategies are important, therefore, job shop scheduling research has theoretical and practical significance. In this paper, the slow evolution of GA algorithm will occur, or premature, and the SA algorithm is introduced to the crossover and mutation, genetic simulated annealing algorithm is proposed - GASA algorithm, mathematical model is given algorithm, the algorithm model, the algorithm process. GA parallel sampling of the time optimization algorithm performance can be improved, and the control of SA binding guidelines to control the convergence of the algorithm to avoid prematurity. The classical scheduling problem FT06, GASA correctness of the algorithm is verified and compared with the traditional GA algorithm is verified GASA efficiency of the algorithm.

关键词： manufacturing execution system shop scheduling genetic algorithms simulated annealing algorithm

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Flux: A Next-Generation resource management Framework for Large HPC Centers 43

Flux: A Next-Generation Resource Management Framework for La...

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43rd international Conference on parallel Processing workshops, ICPPW 2014

作者： Ahn, Dong H. Garlick, Jim Grondona, Mark Lipari, Don Springmeyer, Becky Schulz, Martin Lawrence Livermore National Laboratory Computation Directorate LivermoreCA94550 United States

ISBN: (纸本)9781479956159

resource and job management software is crucial to High Performance Computing (HPC) for efficient application execution. However, current systems and approaches can no longer keep up with the challenges large HPC centers are facing due to ever-increasing system scales, resource and workload diversity, interplays between various resources (e.g., between compute clusters and a global file system), and complexity of resource constraints such as strict power budgeting. To address this gap, we propose Flux, an extensible job and resource management framework specifically designed to deal with the requirements of next-generation HPC centers. Flux targets an entire computing facility as one common pool of diverse sets of resources, enabling the facility to accommodate site-wide constraints (e.g., for power limits). Yet, its scalable and distributed design still offers scalable and effective scheduling strategies. This paper details the design of Flux and describes and evaluates our initial prototyping effort of the key run-time components. Our results show that our run-time prototype provides strong and predictable scalability. © 2014 IEEE.

关键词： Budget control

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MiCA: Real-time Mixed Compression Scheme for Large-Scale distributed Monitoring 43

MiCA: Real-time Mixed Compression Scheme for Large-Scale Dis...

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43rd Annual international Conference on parallel Processing (ICPP)

作者： Wang, Bo Song, Ying Sun, Yuzhong Liu, Jun Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing 100190 Peoples R China

ISBN: (纸本)9781479956180

Real-time monitoring, providing the real-time status information of servers, is indispensable for the management of distributed systems, e.g. failure detection and resource scheduling. The scalability of fine-grained monitoring faces more and more severe challenges with scaling up distributed systems. The real-time compression which suppresses remote information update to reduce continuous monitoring cost is a promising approach to address the scalability problem. In this paper, we present the Linear Compression Algorithm (LCA) which is the application of the linear filter to real-time monitoring. To our best knowledge, existing work and LCA only explores the correlations of values of each single metric at various times. We present a novel lightweight REal-time Compression Algorithm (ReCA) which employs discovery methods of the correlation among metrics to suppress remote information update in distributed monitoring. The compression algorithms mentioned above have limited compression power because they only explore either the correlations of values of each single metric at various times or that among metrics. Therefore, we propose the Mixed Compression Algorithm (MiCA) which explores both of the correlations to achieve higher compression ratio. We implement our algorithms and an existing compression algorithm denoted by CCA in a distributed monitoring system Ganglia and conduct extensive experiments. The experimental results show that LCA and ReCA have comparable compression ratios with CCA, that MiCA achieves up to 38.2%, 27% and 44.5% higher compression ratios than CCA, LCA and ReCA with negligible overhead, respectively, and that LCA, and ReCA can both increase the scalability of Ganglia about 1.5 times and MiCA can increase about 2.33 times under a mixed-load circumstance.

关键词： distributed system monitoring real-time data compression

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Next generation job management systems for extreme-scale ensemble computing 14

Next generation job management systems for extreme-scale ens...

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23rd ACM Symposium on High-Performance parallel and distributed Computing, HPDC 2014

作者： Wang, Ke Zhou, Xiaobing Chen, Hao Lang, Michael Raicu, Ioan Illinois Institute of Technology Chicago IL 60616 United States Los Alamos National Laboratory Los Alamos UT 87544 United States

ISBN: (纸本)9781450327480

With the exponential growth of supercomputers in parallelism, applications are growing more diverse, including traditional largescale HPC MPI jobs, and ensemble workloads such as finergrained many-task computing (MTC) applications. Delivering high throughput and low latency for both workloads requires developing a distributed job management system that is magnitudes more scalable than today's centralized ones. In this paper, we present a distributed job launch prototype, SLURM++, which is comprised of multiple controllers with each one managing a partition of SLURM daemons, while ZHT (a distributed key-value store) is used to store the job and resource metadata. We compared SLURM++ with SLURM using microbenchmarks of different job sizes up to 500 nodes, with excellent results showing 10X higher throughput. We also studied the potential of distributed scheduling through simulations up to millions of nodes. Copyright © 2014 ACM.

关键词： Supercomputers

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PARMA-DITAM 2014 - Proc., 5th workshop on parallel Programming and Run-Time Manage. Tech. for Many-Core Architect. and 3rd workshop on Design Tools and Architect. for Multicore Embedded Comput. Platforms

PARMA-DITAM 2014 - Proc., 5th Workshop on Parallel Programmi...

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5th workshop on parallel Programming and Run-Time management Techniques for Many-Core Architectures and 3rd workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms, PARMA-DITAM 2014

ISBN: (纸本)9781450326070

The proceedings contain 7 papers. The topics discussed include: an interactive tool based on Polly for detection and parallelization of loops;on expressing strategies for directive-driven multicore programming models;effective platform-level exploration for heterogeneous multicores exploiting simulation-induced slacks;a cycle-accurate synthesizable MIPS simulator in Simulink;extending a run-time resource management framework to support OpenCL and heterogeneous systems;exploiting performance counters for energy efficient co-scheduling of mixed workloads on multi-core platforms;and fine-grained link locking within power and latency transaction level modelling in wormhole switching non-preemptive networks on chip.

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