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Continuously Improving the resource Utilization of Iterative parallel Dataflows

Continuously Improving the Resource Utilization of Iterative...

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international Conference on distributed Computing systems workshop

作者： Lauritz Thamsen Thomas Renner Odej Kao Technische Universität Berlin Germany

parallel dataflow systems like Apache Flink allow analysis of large datasets with iterative programs. However, allocating a cost-effective set of resources for such jobs is a difficult task as the resource utilization depends on many factors such as dataset size, key value distributions, computational complexity of programs, and the underlying hardware. What's more, some of these factors are not well known before the execution. There are, for example, often no data statistics such as key value distributions available beforehand. For this reason, we propose to improve the resource utilization at runtime using the repetitive nature of iterative dataflow programs. Based on runtime statistics gathered in previous iterations, the resource allocation is adapted dynamically at the synchronization barriers between iterations. This approach has two advantages: First, at barriers detailed statistics can be available, even for parallelly executed task pipelines. Second, at barriers dataflows can be adapted without complex handling of intermediate task state. This paper presents a prototype integrated with Apache Flink and an evaluation on a cluster with 480 cores. One experiment shows a 57% reduction of the job runtime by allocating more resources for a shorter time, another experiment a release of up to 40% surplus resources without significantly extending the job runtime.

关键词： resource management Runtime Prototypes Hardware parallel processing Iterative methods distributed databases

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scheduling Matters: Area-Oriented Heuristic for resource management

Scheduling Matters: Area-Oriented Heuristic for Resource Man...

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international Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Jeremy Benson Trilce Estrada Arnold L. Rosenberg Michela Taufer University of New Mexico Albuquerque NM USA Northeastern University Boston MA USA University of Delaware Newark DE USA

ISBN: (纸本)9781509061099

parallel and distributed systems that provide compute resources on demand are convenient, cost-effective, and becoming increasingly common. Boosting workload performance in such environments through scheduling has been of great interest, as users and providers aim to increase parallelism and reduce execution times. For modern data centers, leaving a smaller carbon footprint while maintaining high performance and low cost is becoming the next big challenge. With this in mind, we analyze the relative impacts on resource utilization of three well-motivated platform-oblivious scheduling heuristics. We simulate over 50,000 DAG workflow executions and measure performance, cost, and resource utilization under the three scheduling heuristics. Our results provide insights to better enable high-performance execution of workflows and advanced capacity planning while increasing resource utilization and reducing costs.

关键词： Schedules Processor scheduling resource management Optimization scheduling Context Capacity planning

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Analysis, Modeling, and Simulation of Hadoop YARN MapReduce

Analysis, Modeling, and Simulation of Hadoop YARN MapReduce

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international Conference on parallel and distributed systems (ICPADS)

作者： Thomas C. Bressoud Qiuyi Tang Department of Mathematics and Computer Science Denison University Granville OH United States of America Builder Tools Amazon Web Service Seattle WA United States of America

ISBN: (纸本)9781509053827

Despite much work in simulation of MapReduce applications and in optimizing scheduling to minimize workload execution time, we still need models and tools to understand the performance of the individual tasks of a MapReduce application. Armed with this knowledge, we can characterize applications and model task operation to determine their performance in a given cluster setting. We have developed and analyzed a model of MapReduce tasks that abstracts their operation at a balanced level, avoiding too much detail that slows down analysis and requires knowledge of the inner workings of the cluster system, but at sufficient detail to be able to generalize to multiple application classes and cluster systems. The model, and an accompanying simulator, are evaluated against execution of real MapReduce applications, and show very promising results.

关键词： Analytical models Computational modeling Yarn Containers Mathematical model resource management Fault tolerance

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Towards a Green, QoS-Enabled Heterogeneous Cloud Infrastructure

Towards a Green, QoS-Enabled Heterogeneous Cloud Infrastruct...

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IEEE international Symposium on parallel and distributed Processing workshops and Phd Forum (IPDPSW)

作者： Julio Proaño Carmen Carrión M. Blanca Caminero Computing Systems Department University of Castilla-La Mancha Albacete Spain

Efficient heterogeneous resource management is one of the many challenges Cloud systems exhibit, because it can lead to increasing the profit obtained by Cloud providers by serving more client requests while fulfilling their requirements with the minimum set of resources. In this context, heterogeneous architectures composed by accelerators, and Field Programmable Gate Arrays (FPGAs) in particular, can enhance the fulfillment of client requirements with less power consumption, as long as they are managed in a smart way. Thus, the HEterogeneous Cloud COmputing (HECCO) framework developed in this work enables the integration and efficient management of FPGAs as co-processing resources within a Cloud Computing paradigm. This framework uses allocation and scheduling algorithms based on classification and prediction models in order to choose the most suitable combination of resources according to the applications and SLA parameters. A proof-of-concept implementation of the HECCO framework over an image processing service case-study is evaluated on a small testbed. Results show how the smart use of FPGAs integrated with conventional computational resources leads to a higher percentage of clients serviced with their QoS fulfilled and even supporting SLAs with more stringent deadlines. Additionally, energy savings exist, which can contribute to reduce the energy footprint of data-centers.

关键词： Cloud computing Field programmable gate arrays Quality of service Computer architecture resource management Companies Proposals

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Evaluating the Performance of A4SDN on Various Network Topologies

Evaluating the Performance of A4SDN on Various Network Topol...

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IEEE international Symposium on parallel and distributed Processing workshops and Phd Forum (IPDPSW)

作者： Giovanni Cammarata Antonella Di Stefano Giovanni Morana Daniele Zito DIEEI University of Catania Catania Italy DIEEI Univ. of Catania Catania Sicilia IT

ISBN: (纸本)9781509036837

Load balancing is one of the main goals of Software-Defined Networking (SDN). Distributing the traffic among the possible paths of a network according to specific rules allows SDN systems to increase their global performance index in term of resource usage and scalability. In a previous paper, the authors introduced Adaptive Alienated Ant Algorithm for Software-Defined Networking (A4SDN), a distributed, adaptive, load-balancing algorithm for traffic engineering on SDN showing its ability to optimise the network performance in terms of throughput, communication delay and packet loss rate. In this paper, the authors analyse how the performance of A4SDN are influenced by the underlying topology. For each considered topology, the performance of A4SDN are successfully with those of Extended Dijkstra algorithm. The tests show that the performance of A4SDN are the best when the average number of available shortest paths for each couple of nodes rises.

关键词： Switches Routing Network topology Load management Topology Ant colony optimization Dynamic scheduling

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Shortest Processing Time First Algorithm for Hadoop

Shortest Processing Time First Algorithm for Hadoop

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IEEE international Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Laurent Bobelin Patrick Martineau Di Zhao Haiwu He LI Université François Rabelais Tours France Computer Network Information Center Chinese Academy of Sciences China

ISBN: (纸本)9781509009473

Big data has revealed itself as a powerful tool for many sectors ranging from science to business. distributed data-parallel computing is then common nowadays: using a large number of computing and storage resources makes possible data processing of a yet unknown scale. But to develop large-scale distributed big data processing, we have to tackle many challenges. One of the most complex is scheduling. As it is known to be an optimal online scheduling policy when it comes to minimize the average flowtime, Shortest Processing Time First (SPT) is a classic scheduling policy used in many systems. We then decided to integrate this policy into Hadoop, a framework for big data processing, and realize an implementation prototype. This paper describes this integration, as well as tests results obtained on our testbed.

关键词： Containers Computer architecture Big data scheduling resource management Monitoring Yarn

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Priority Operators for Fairshare scheduling 1

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18th international workshop on Job scheduling Strategies for parallel Processing (JSSPP)

作者： Rodrigo, Gonzalo P. Ostberg, Per-Olov Elmroth, Erik Umea Univ Dept Comp Sci SE-90187 Umea Sweden

ISBN: (数字)9783319157894

ISBN: (纸本)9783319157887;9783319157894

Collaborative resource sharing in distributed computing requires scalable mechanisms for allocation and control of user quotas. Decentralized fairshare prioritization is a technique for enforcement of user quotas that can be realized without centralized control. The technique is based on influencing the job scheduling order of local resource management systems using an algorithm that establishes a semantic for prioritization of jobs based on the individual distances between user's quota allocations and user's historical resource usage (i.e. intended and current system state). This work addresses the design and evaluation of priority operators, mathematical functions to quantify fairshare distances, and identify a set of desirable characteristics for fairshare priority operators. In addition, this work also proposes a set of operators for fairshare prioritization, establishes a methodology for verification and evaluation of operator characteristics, and evaluates the proposed operator set based on this mathematical framework. Limitations in the numerical representation of scheduling factor values are identified as a key challenge in priority operator formulation, and it is demonstrated that the contributed priority operators (the Sigmoid operator family) behave robustly even in the presence of severe resolution limitations.

关键词： scheduling

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Analyzing the Effects of Reordering Work List Items for Selected Control Flow Patterns 19

Analyzing the Effects of Reordering Work List Items for Sele...

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IEEE 19th international Enterprise distributed Object Computing (EDOCW)

作者： Pflug, Johannes Rinderle-Ma, Stefanie Univ Vienna Res Grp Workflow Syst & Technol Vienna Austria

ISBN: (纸本)9781467393317

Efficient resource management is an important requirement for many process-oriented applications. Typically, work items are assigned to resources through their work lists. There are many reasons for reordering work items in a resource's work list. For process scheduling, for example, swapping process instances constitutes a mean to keep due times. At the same time, reducing the throughput time of the global process is typically not the primary goal. For process optimization, in turn, the implications of reordering work items on the overall temporal performance of the process might be crucial. In this paper, we investigate how reordering work items affects performance parameters that are typically associated with a first-in-first-out processing mechanism at resources. The analysis is conducted for single process tasks and for typical control flow patterns such as sequence as well as parallel and alternative branchings. It is shown that the implications on the global throughput time are less than expected, while the effects on instance-based parameters strongly depend on the control-flow pattern in which the reordering mechanism is implemented. The results are supported by means of a simulation.

关键词： information systems resource allocation scheduling alternative branchings control flow patterns first-in-first-out processing mechanism instance based parameters parallel branchings process optimization process scheduling resource management work list items reordering mechanism Delays Electronic mail Engines Optimization Process control Throughput Yttrium Business Process Analysis Business Process management Business Process Optimization Work List management management of resources FLOW PATTERNS Process optimization Business process management electronic mail throughput time Engines Process control Rare earths yttrium Throughput Computer based information systems resource utilization delay

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Adaptive resource and Job management for limited power consumption 29

Adaptive Resource and Job Management for limited power consu...

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29th IEEE international parallel and distributed Processing Symposium (IPDPS)

作者： Georgiou, Yiannis Glesser, David Trystram, Denis Bull SA Les Clayes Sous Bois France Univ Grenoble Alpes Grenoble France Inst Univ France Grenoble France

ISBN: (纸本)9781467376846

The last decades have been characterized by an ever growing requirement in terms of computing and storage resources. This tendency has recently put the pressure on the ability to efficiently manage the power required to operate the huge amount of electrical components associated with state-of-the-art high performance computing systems. The power consumption of a supercomputer needs to be adjusted based on varying power budget or electricity availabilities. As a consequence, resource and Job management systems have to be adequately adapted in order to efficiently schedule jobs with optimized performance while limiting power usage whenever needed. We introduce in this paper a new scheduling strategy that can adapt the executed workload to a limited power budget. The originality of this approach relies upon a combination of speed scaling and node shutdown techniques for power reductions. It is implemented into the widely used resource and job management system SLURM. Finally, it is validated through large scale emulations using real production workload traces of the supercomputer Curie.

关键词： resource management powercap power-aware scheduling

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A Batch System with Efficient Adaptive scheduling for Malleable and Evolving Applications 29

A Batch System with Efficient Adaptive Scheduling for Mallea...

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29th IEEE international parallel and distributed Processing Symposium (IPDPS)

作者： Prabhakaran, Suraj Neumann, Marcel Rinke, Sebastian Wolf, Felix Gupta, Abhishek Kale, Laxmikant V. German Res Sch Simulat Sci Aachen Germany Tech Univ Darmstadt Darmstadt Germany Univ Illinois Dept Comp Sci Urbana IL USA

ISBN: (纸本)9781479986484

The throughput of supercomputers depends not only on efficient job scheduling but also on the type of jobs that form the workload. Malleable jobs are most favorable for a cluster as they can dynamically adapt to a changing allocation of resources. The batch system can expand or shrink a running malleable job to improve system utilization, throughput, and response times. In the past, however, the rigid nature of commonly used programming models like MPI made writing malleable applications a daunting task, which is why it remained largely unrealized. This is now changing. To improve fault tolerance, load imbalance, and energy efficiency in emerging exascale systems, more adaptive programming paradigms such as Charm++ enter the scene. Although they offer better support for malleability, current batch systems still lack management facilities for malleable jobs and are therefore incapable of leveraging their potential. In this paper, we present an extension of the Torque/Maui batch system for malleability. We propose a novel malleable job scheduling strategy and show the first batch system capable of efficiently managing rigid, malleable, and evolving jobs together. We demonstrate that our strategy achieves consistently superior performance in comparison to every other state-of-the-art malleable job scheduling strategy under varying dynamics of the workload.

关键词： malleable jobs evolving jobs adaptive scheduling adaptive resource management batch systems

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