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Proceedings of the 9th IEEE international Symposium on Industrial Embedded systems, SIES 2014

Proceedings of the 9th IEEE International Symposium on Indus...

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9th IEEE international Symposium on Industrial Embedded systems, SIES 2014

ISBN: (纸本)9781479940233

The proceedings contain 32 papers. The topics discussed include: periodic charging scheme for fixed-priority real-time systems with renewable energy;saving energy by means of dynamic load management in embedded multicore systems;towards a true energetically sustainable WSN: a case study with prediction-based data collection and a wake-up receiver;hard constant bandwidth server: comprehensive formulation and critical scenarios;on the scheduling of fork-join parallel/distributed real-time tasks;flexible spin-lock model for resource sharing in multiprocessor real-time systems;a networked haptic embedded controller;data fusion for relative localization of wireless mobile nodes;tracking limbs motion using a wireless network of inertial measurement units;and modeling and real-time control of an industrial air multi-compressor system.

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Analytical study of load scheduling algorithms in cloud computing

Analytical study of load scheduling algorithms in cloud comp...

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international Conference on parallel distributed and Grid Computing (PDGC)

作者： Divya Chaudhary Bijendra Kumar Department of Computer Engineering Netaji Subhas Institute of Technology New Delhi India

scheduling enables the cloud in balancing the large amount of load present in the system for faster computation. It plays a vital and significant part in the execution of the load in the various heterogeneous systems. The scheduling portrays a selection of resources for the tasks for better resource utilization. This paper differentiates the various load scheduling algorithms applied in the various heterogeneous systems in detail. It plays a key role in larger resource utilization and handling. This paper defines the basic cloud computing fundamentals and the concept of load balancing i.e., scheduling of load in cloud. The applied load scheduling algorithms are elaborated and surveyed extensively.

关键词： resource management scheduling algorithms Biological cells Cloud computing Genetic algorithms Load management

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Integration and Evaluation of Decentralized Fairshare Prioritization (Aequus)

Integration and Evaluation of Decentralized Fairshare Priori...

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

作者： Daniel Espling Per-Olov Östberg Erik Elmroth Department of Computing Science Umeå University Umeå Sweden

Fairshare is commonly one of the factors used by cluster resource management systems to prioritize jobs during scheduling. Despite the grid vision of a transparent and unified infrastructure, fairshare is normally calculated and enforced at the local cluster level rather than at a grid-wide scale. Aequus is a self-contained decentralized system for grid-wide fairshare job prioritization. Using Aequus, detailed global share policies can be combined with local cluster policies to offer a unified grid fairshare prioritization system where local administrations retain control over their clusters. This work shows how Aequus can be integrated with local resource management systems such as SLURM and Maui with minimal intrusion. Early results from production help assess the maturity of the system, and the system is further tested and evaluated for use at a nation-wide scale using workload modeling techniques. Statistical models are created based on historical national grid usage data, and synthetic traces based on these models are used to create a diverse input set used to exemplify system behavior. The system is shown to behave consistently despite great variations in job arrival patterns and partial participation of some of the collaborating installations.

关键词： Vectors resource management Data models Load modeling scheduling Processor scheduling

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An Experimental Analysis for Hardware resource management Using a New Strip Packing Problem

An Experimental Analysis for Hardware Resource Management Us...

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Euromicro Conference on parallel, distributed and Network-Based Processing

作者： Rika Ito Naoyuki Fujita Numerical Simulation Research Group Japan Aerospace Exploration Agency (JAXA)

The use of PC cluster systems composed of many PCs is largely spread in these days. If there are many waiting jobs even when there is free resource, it is a waste of resource. So the algorithm to execute jobs as many as possible has been researched for decades. However this optimization problem is difficult to solve. So we propose algorithms based on a strip packing problem to arrange jobs. We performed numerical experiments and we show these results.

关键词： Algorithm design and analysis Strips Prediction algorithms Finishing Sorting resource management Processor scheduling

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Flexible scheduling and Control of Bandwidth and In-transit Services for End-to-End Application Workflows

Flexible Scheduling and Control of Bandwidth and In-transit ...

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international workshop on Network-Aware Data management (NDM)

作者： Mehmet Fatih Aktas Georgiana Haldeman Manish Parashar Rutgers The State University of New Jersey New Brunswick NJ US

Emerging end-to-end scientific applications that integrate high-end experiments and instruments with large scale simulations and end-user displays, require complex couplings and data sharing between distributed components involving large data volumes and varying hard (in-time data delivery) and soft (in-transit processing) quality of service (QoS) requirements. As a result, efficient data transport is a key requirement of such workflows. In this paper, we leverage software-defined networking (SDN) to address issues of data transport service control and resource provisioning to meet varying QoS requirements from multiple coupled workflows sharing the same service medium. Specifically, we present a flexible control and a disciplined resource scheduling approach for data transport services of science networks. Furthermore, we emulate an SDN testbed on top of the FutureGrid virtualized testbed and use it to evaluate our approach for a realistic scientific workflow. Our results show that SDN-based control and resource scheduling based on simple intuitive models can meet the coupling requirement with high resource utilization.

关键词： Couplings Quality of service Data models resource management Plasmas Control systems Bandwidth

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Combine thread with memory scheduling for maximizing performance in multi-core systems

Combine thread with memory scheduling for maximizing perform...

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international Conference on parallel and distributed systems (ICPADS)

作者： Gangyong Jia Guangjie Han Liang Shi Jian Wan Dong Dai Department of Computer Science and Technology Hangzhou Dianzi University Hanazhou China Department of Computer Science Hohai University Changzhou China Department of Computer Science and Technology Chongqing University Chongqing China Department of Computer Science Texas Tech University Lubbock TX USA

The growing gap between microprocessor speed and DRAM speed is a major problem that computer designers are facing. In order to narrow the gap, it is necessary to improve DRAM's speed and throughput. Moreover, on multi-core platforms, DRAM memory shared by all cores usually suffers from the memory contention and interference problem, which can cause serious performance degradation and unfairness among parallel running threads. To address these problems, this paper proposes techniques to take both advantages of partitioning cores, threads and memory banks into groups to reduce interference among different groups and grouping the memory accesses of the same row together to reduce cache miss rate. A memory optimization framework combined thread scheduling with memory scheduling (CTMS) is proposed in this paper, which simultaneously minimizes memory access schedule length, memory access time and reduce interference to maximize performance for multi-core systems. Experimental results show CTMS is 12.6% shorter in memory access time, while improving 11.8% throughput on average. Moreover, CTMS also saves 5.8% of the energy consumption.

关键词： Instruction sets scheduling Interference Memory management Clocks Schedules resource management

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Improving utilization through dynamic VM resource allocation in hybrid cloud environment

Improving utilization through dynamic VM resource allocation...

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international Conference on parallel and distributed systems (ICPADS)

作者： Yuda Wang Renyu Yang Tianyu Wo Wenbo Jiang Chunming Hu School of Computer Science and Engineering Beihang University Beijing China

Virtualization is one of the most fascinating techniques because it can facilitate the infrastructure management and provide isolated execution for running workloads. Despite the benefits gained from virtualization and resource sharing, improved resource utilization is still far from settled due to the dynamic resource requirements and the widely-used over-provision strategy for guaranteed QoS. Additionally, with the emerging demands for big data analytic, how to effectively manage hybrid workloads such as traditional batch task and long-running virtual machine (VM) service needs to be dealt with. In this paper, we propose a system to combine long-running VM service with typical batch workload like MapReduce. The objectives are to improve the holistic cluster utilization through dynamic resource adjustment mechanism for VM without violating other batch workload executions. Furthermore, VM migration is utilized to ensure high availability and avoid potential performance degradation. The experimental results reveal that the dynamically allocated memory is close to the real usage with only 10% estimation margin, and the performance impact on VM and MapReduce jobs are both within 1%. Additionally, at most 50% increment of resource utilization could be achieved. We believe that these findings are in the right direction to solving workload consolidation issues in hybrid computing environments.

关键词： resource management Dynamic scheduling Containers Memory management Protocols Benchmark testing Yarn

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

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

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international Conference on parallel Processing workshops (ICPPW)

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

ISBN: (纸本)9781479956166

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.

关键词： resource management scheduling Computational modeling Software Synchronization Processor scheduling Prototypes

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Analysis of resource mapping strategies for fault tolerant scheduling on grid computing

Analysis of resource mapping strategies for fault tolerant s...

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international Conference on parallel distributed and Grid Computing (PDGC)

作者： Shafi Jindal R K Bansal Savina Bansal Deptt of Electronics and Communication Engineering Giani Zail Singh - Punjab Tech Univ Campus Bathinda Punjab India

Grid environment has significant challenges due to failures encountered during job execution. Grid infrastructure must have mechanisms to deal with faults while also providing efficient and reliable services to its end users. Therefore fault tolerance in such an environment could not be ignored. In this paper, two strategies such as Maximum Mapping Strategy (MMS) and Exact Mapping Strategy (EMS) are proposed for fault tolerant job scheduling in grid. Whenever the scheduler has jobs to schedule, MMS and EMS selects the resources based on the maximum suitable mapping and exact mapping of job requirement value with the resource capable value respectively. If failure occurs during execution, re-schedules the jobs using the last saved state according to the failure rate of the resources. EMS approach is effective in the sense that all resources are busy at the same time so that resources are utilized in better way and Make-span i.e. the time taken by all jobs to be finished processing is also reduced. Failure rate is reduces in EMS because failures due to speed and memory and bandwidth is less as load is distributed almost equally among resources compared to MMS.

关键词： Fault tolerance Fault tolerant systems resource management Grid computing Bandwidth Computers Schedules

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

Utility Driven Dynamic Resource Management in an Oversubscri...

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

作者： Bhavesh Khemka Ryan Friese Sudeep Pasricha Anthony A. Maciejewski Howard Jay Siegel Gregory A. Koenig Sarah Powers Marcia Hilton Rajendra Rambharos Steve Poole Department of Electrical and Computer Engineering Colorado State University CO Department of Computer Science Colorado State University CO Oak Ridge National Laboratory Oak Ridge TN Department of Defense Washington DC

ISBN: (纸本)9781479941155

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： resource management Computational modeling Energy consumption Aggregates Processor scheduling US Department of Defense System performance

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