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Improving Batch scheduling on Blue Gene/Q by Relaxing 5D Torus Network Allocation Constraints

Improving Batch Scheduling on Blue Gene/Q by Relaxing 5D Tor...

引用

international Symposium on parallel and distributed Processing (IPDPS)

作者： Zhou Zhou Xu Yang Zhiling Lan Paul Rich Wei Tang Vitali Morozov Narayan Desai Department of Computer Science Illinois Institute of Technology Chicago IL USA Argonne Leadership Computing Facility Mathematics and Computer Science Division Argonne National Laboratory Argonne IL USA Ericsson Inc. San Jose CA USA

As systems scale toward exactable, many resources will become increasingly constrained. While some of these resources have historically been explicitly allocated, many -- such as network bandwidth, I/O bandwidth, or power -- have not. As systems continue to evolve, we expect many such resources to become explicitly managed. This change will pose critical challenges to resource management and job scheduling. In this paper, we explore the potentiality of relaxing network allocation constraints for Blue Gene systems. Our objectives to improve the batch scheduling performance, where the partition-based interconnect architecture provides a unique opportunity to explicitly allocate network resources to jobs. This paper makes three major contributions. The first is substantial benchmarking of parallel applications, focusing on assessing application sensitivity to communication bandwidth at large scale. The second is two new scheduling schemes using relaxed network allocation and targeted at balancing individual job performance with overall system performance. The third is a comparative study of our scheduling schemes versus the existing one under different workloads, using job traces collected from the 48-rack Mira, an IBM Blue Gene/Q system at Argonne National Laboratory.

关键词： Runtime Bandwidth Benchmark testing resource management Network topology Wiring scheduling

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resource and Deadline-Aware Job scheduling in Dynamic Hadoop Clusters

Resource and Deadline-Aware Job Scheduling in Dynamic Hadoop...

引用

international Symposium on parallel and distributed Processing (IPDPS)

作者： Dazhao Cheng Jia Rao Changjun Jiang Xiaobo Zhou University of Colorado Boulder Boulder CO US University of Colorado Colorado Springs USA Tongji University Shanghai China

ISBN: (纸本)9781479986507

As Hadoop is becoming increasingly popular in large-scale data analysis, there is a growing need for providing predictable services to users who have strict requirements on job completion times. While earliest deadline first scheduling (EDF) like algorithms are popular in guaranteeing job deadlines in real-time systems, they are not effective in a dynamic Hadoop environment, i.e., a Hadoop cluster with dynamically available resources. As there is a growing number of Hadoop clusters deployed on hybrid systems, e.g., infrastructure powered by mix of traditional and renewable energy, and cloud platforms hosting heterogeneous workloads, variable resource availability becomes common when running Hadoop jobs. In this paper, we propose, RDS, a resource and Deadline-aware Hadoop job Scheduler that takes future resource availability into consideration when minimizing job deadline misses. We formulate the job scheduling problem as an online optimization problem and solve it using an efficient receding horizon control algorithm. To aid the control, we design a self-learning model to estimate job completion times and use a simple but effective model to predict future resource availability. We have implemented RDS in the open source Hadoop implementation and performed evaluations with various benchmark workloads. Experimental results show that RDS substantially reduces the penalty of deadline misses by at least 36% and 10% compared with Fair Scheduler and EDF scheduler, respectively.

关键词： Dynamic scheduling resource management Predictive models Optimization Heuristic algorithms Job shop scheduling

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Controlled Contention: Balancing Contention and Reservation in Multicore Application scheduling

Controlled Contention: Balancing Contention and Reservation ...

引用

international Symposium on parallel and distributed Processing (IPDPS)

作者： Jingjing Wang Nael Abu-Ghazaleh Dmitry Ponomarev Computer Science Department Binghamton University CSE and ECE Depts University of California Riverside

ISBN: (纸本)9781479986507

One of the benefits of multiprogramming in conventional systems is to allow effective use of resources. For example, when one application blocks for I/O, another can use the available CPU time, improving throughput and performance. In a multithreaded environment, contention for resources can lead to substantial interference between applications: an application with dependencies can suffer if a thread holding critical dependency is not scheduled in time. In the HPC community, this problem is often addressed by reservation-based schedulers such as Gang scheduling. However, such schedulers cannot reap the benefits of resource multiplexing leading to underutilization of the resources and lower overall throughput of the system. In this paper, we explore the trade off between contention and reservation in multithreaded application scheduling on multicourse systems. We show that neither approach is optimal under all conditions. We propose Controlled Contention (CC) -- a scheduling algorithm that allows controlled contention for resources, allowing the benefits of contention while supporting limited reservation to reduce interference. CC provides around 25% improvement in relative speedup over the Completely Fair Scheduler (CFS). We also show that CC can significantly benefit from application-level interference management while providing fairness that is not possible to achieve with application-level adaptation alone. The combined approach (CC with application-level adaptation)provides an average of 21% improvement in throughput, 35% improvement in relative speedup and 36% reduction in energy for the application mixes we consider.

关键词： Interference Throughput Multicore processing Processor scheduling Radiation detectors Instruction sets resource management

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resource Provision for Batch and Interactive Workloads in Data Centers

Resource Provision for Batch and Interactive Workloads in Da...

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international Conference on parallel and distributed systems (ICPADS)

作者： Ting-Wei Chang Ching-Chi Lin Pangfeng Liu Jan-Jan Wu Chia-Chun Shih Chao-Wen Huang Department of Computer Science and Information Engineering National Taiwan University Institute of Information Science Academia Sinica The Graduate Institute of Networking and Multimedia National Taiwan University Chunghwa Telecom Laboratories

ISBN: (纸本)9781467386692

In this paper we describe a scheduling framework that allocates resources to both batch jobs and interactive jobs simultaneously in a private cloud with a static amount of resources. In the system, every job has an individual service level agreement (SLA), and violating the SLA incurs penalty. We propose a model to formally quantify the SLA violation penalty of both batch and interactive jobs. The analysis on the interactive jobs focuses on queuing analysis and response time. The analysis on batch jobs focuses on the non-preemptive job scheduling for multiple processing units. Based on this model we also propose algorithms to estimate the penalty for both batch jobs and interactive jobs, and algorithms that reduce the total SLA violation penalty. Our experiment results suggest that our system effectively reduces the total penalty by allocating the right amount of resources to heterogeneous jobs in a private cloud system.

关键词： Cloud computing Schedules Time factors Algorithm design and analysis resource management Heuristic algorithms scheduling

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

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

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international Symposium on parallel and distributed Processing (IPDPS)

作者： Suraj Prabhakaran Marcel Neumann Sebastian Rinke Felix Wolf Abhishek Gupta Laxmikant V. Kale German Research School for Simulation Sciences Aachen Germany Technische Universitat Darmstadt Darmstadt Germany Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL USA

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 favourable 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 exactable 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： Torque resource management Runtime Throughput Dynamic scheduling Servers

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Interference-Aware Component scheduling for Reducing Tail Latency in Cloud Interactive Services

Interference-Aware Component Scheduling for Reducing Tail La...

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international Conference on distributed Computing systems

作者： Rui Han Junwei Wang Siguang Huang Chenrong Shao Shulin Zhan Jianfeng Zhan Jose Luis Vazquez-Poletti Institute Of Computing Technology Chinese Academy of Sciences Beijing China Facultad de Informatica Universidad Complutense de Madrid Madrid Spain

Large-scale interactive services usually divide requests into multiple sub-requests and distribute them to a large number of server components for parallel execution. Hence the tail latency (i.e. The slowest component's latency) of these components determines the overall service latency. On a cloud platform, each component shares and competes node resources such as caches and I/O bandwidths with its co-located jobs, hence inevitably suffering from their performance interference. In this paper, we study the short-running jobs in a 12k-node Google cluster to illustrate the dynamic resource demands of these jobs, resulting in both individual components' latency variability over time and across different nodes and hence posing a major challenge to maintain low tail latency. Given this motivation, this paper introduces a dynamic and interference-aware scheduler for large-scale, parallel cloud services. At each scheduling interval, it collects workload and resource contention information of a running service, and predicts both the component latency on different nodes and the overall service performance. Based on the predicted performance, the scheduler identifies straggling components and conducts near-optimal component-node allocations to adapt to the changing workloads and performance interferences. We demonstrate that, using realistic workloads, the proposed approach achieves significant reductions in tail latency compared to the basic approach without scheduling.

关键词： Google Interference Monitoring Bandwidth resource management Servers Dynamic scheduling

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JellyFish: Online Performance Tuning with Adaptive Configuration and Elastic Container in Hadoop Yarn

JellyFish: Online Performance Tuning with Adaptive Configura...

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international Conference on parallel and distributed systems (ICPADS)

作者： Xiaoan Ding Yi Liu Depei Qian Sino-German Joint Software Institute Beihang University Beijing China

MapReduce is a popular computing framework for large-scale data processing. Practical experience shows that inappropriate configurations can result in poor performance of MapReduce jobs, however, it is challenging to pick out a suitable configuration in a short time. Also, current central resource scheduler may cause low resource utilization, and degrade the performance of the cluster. This paper proposes an online performance tuning system, JellyFish, to improve performance of MapReduce jobs and increase resource utilization in Hadoop YARN. JellyFish continually collects real-time statistics to optimize configuration and resource allocation dynamically during execution of a job. During performance tuning process, JellyFish firstly tunes configuration parameters by reducing the dimensionality of search space with a divide-and-conquer approach and using a model-based hill climbing algorithm to improve tuning efficiency; secondly, JellyFish re-schedules resources in nodes by using a novel elastic container that can expand and shrink dynamically according to resource usage, and a resource re-scheduling strategy to make full use of cluster resources. Experimental results show that JellyFish can improve performance of MapReduce jobs by an average of 24% for jobs run for the first time, and by an average of 65% for jobs run multiple times compared to default YARN.

关键词： Containers Tuning Yarn resource management Monitoring Real-time systems Heuristic algorithms

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Millipedes: distributed and Set-Based Sub-Task Scheduler of Computing Engines Running on Yarn Cluster

Millipedes: Distributed and Set-Based Sub-Task Scheduler of ...

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IEEE international Conference on High Performance Computing and Communications (HPCC)

作者： Kebing Wang Zhaojuan Bian Qian Chen Software and Service Group Intel Corporation Shanghai China

ISBN: (纸本)9781479989386

Hadoop YARN is evolving to become the de-facto standard that allows multiple data processing engines such as interactive SQL, real-time streaming, data science and batch processing to handle data stored in a single platform. And, there are lots of researches about efficiently managing cluster resources and scheduling parallel jobs over YARN clusters. However, the scheduling of sub-tasks derived from one application doesn't receive enough attention, which still managed by independent data processing engines on YARN. In this paper, we analyze the limitation of sub-task scheduling algorithms of popular data processing engines running on YARN, including MapReduce 2.0, Spark Then, we propose Millipedes: a distributed & set-based sub-task scheduling framework. In Millipedes, YARN's application master dispatches a set of sub tasks to data nodes, and then the local task scheduler of each data node schedules the execution of these sub tasks according to real-time dynamic resource usage.

关键词： Engines Containers Yarn Software scheduling Sparks resource management

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Performance analysis of C/U split hybrid satellite terrestrial network for 5G systems

Performance analysis of C/U split hybrid satellite terrestri...

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IEEE international workshop on Computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Jiaxin Zhang Barry Evans Muhammad Ali Imran Xing Zhang Wenbo Wang Wireless Signal Processing & Networks Lab (WSPN) Key Laboratory of Universal Wireless Communication Beijing University of Posts and Telecommunications (BUPT) Beijing China Institute for Communication Systems (ICS) University of Surrey Guildford UK

Over the last decade, the explosive increase in demand of high-data-rate video services and massive access machine type communication (MTC) requests have become the main challenges for the future 5G wireless network. The hybrid satellite terrestrial network based on the control and user plane (C/U) separation concept is expected to support flexible and customized resource scheduling and management toward global ubiquitous networking and unified service architecture. In this paper, centralized and distributed resource management strategies (CRMS and DRMS) are proposed and compared com- prehensively in terms of throughput, power consumption, spectral and energy efficiency (SE and EE) and coverage probability, utilizing the mature stochastic geometry. Numerical results show that, compared with DRMS strategy, the U-plane cooperation between satellite and terrestrial network under CRMS strategy could improve the throughput and EE by nearly 136% and 60% respectively in ultra-sparse networks and greatly enhance the U-plane coverage probability (approximately 77%). Efficient resource management mechanism is suggested for the hybrid network according to the network deployment for the future 5G wireless network.

关键词： Satellite broadcasting Low earth orbit satellites resource management Customer relationship management Throughput Power demand

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BMA: Bandwidth allocation management for distributed systems under cloud gaming

BMA: Bandwidth allocation management for distributed systems...

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international Conference on Communication Software and Networks, ICCSN

作者： Runze Wang Mochi Xue Kai Chen Zhanqiang Li Tao Dong Youkai Sun Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781479919857

Cloud gaming is becoming a popular IT business among cloud services. In order to host a variety of network services, modern clouds provide great computing power by equipping with multi-core CPUs and parallel-core GPUs. In our previous work, we propose VGASA [1], a virtualized GPU resource adaptive scheduling policy in cloud gaming, solving the poor default mechanisms of GPU resource sharing. However, with the number of game player for cloud gaming rising, the user experience is inevitably limited by the network bandwidth and communication quality, due to the poor default mechanism of network resources management. To address this network limitation, we propose BAM, which combines distributed architecture and virtualized GPU resource adaptive policy in cloud gaming based on our prior work. The key module of BAM maintains a real-time monitoring of the virtual machine (VM) running status to manage the allocation of network resources, guaranteeing the better running of VMs.

关键词： Bandwidth Graphics processing units resource management Games Virtual machining Servers Computer architecture

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