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A Hierarchical Memory Service Mechanism in Server Consolidation Environment

A Hierarchical Memory Service Mechanism in Server Consolidat...

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international Conference on parallel and distributed systems (ICPADS)

作者： Liufeng Wang Huaimin Wang Lu Cai Rui Chu Pengfei Zhang Lanzheng Liu National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China School of Economics and Management Xi'an University Xi'an China

Increasing Internet business and computing footprint motivate server consolidation in data centers. Through virtualization technology, server consolidation can reduce physical hosts and provide scalable services. However, the ineffective memory usage among multiple virtual machines (VMs) becomes the bottleneck in server consolidation environment. Because of inaccurate memory usage estimate and the lack of memory resource managements, there is much service performance degradation in data centers, even though they have occupied a large amount of memory. In order to improve this scenario, we first introduce VM's memory division view and VM's free memory division view. Based on them, we propose a hierarchal memory service mechanism. We have designed and implemented the corresponding memory scheduling algorithm to enhance memory efficiency and achieve service level agreement. The benchmark test results show that our implementation can save 30% physical memory with 1% to 5% performance degradation. Based on Xen virtualization platform and balloon driver technology, our works actually bring dramatic benefits to commercial cloud computing center which is providing more than 2,000 VMs' services to cloud computing users.

关键词： Memory management resource management Servers scheduling algorithm Benchmark testing Cloud computing System performance

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scheduling Mixed Real-Time and Non-real-Time Applications in MapReduce Environment

Scheduling Mixed Real-Time and Non-real-Time Applications in...

引用

international Conference on parallel and distributed systems (ICPADS)

作者： Xicheng Dong Ying Wang Huaming Liao Institute of Computing Technology Chinese Academy of Sciences China

MapReduce scheduling is becoming a hot topic as MapReduce attracts more and more attention from both industry and academia. In this paper, we focus on the scheduling of mixed real-time and non-real-time applications in MapReduce environment, which is a challenging problem but receives only limited attention. To solve this problem, we present a two-level MapReduce scheduler built on previous techniques and make two key contributions. First, to meet the performance goal of real-time applications, we propose a deadline scheduler which adopts (1) a sampling based approach-Tasks Forward scheduling (TFS) to predict map/reduce task execution time(unlike prior work that requires users to input an estimated value). (2) a resource allocation model-Approximately Uniform Minimum Degree of parallelism (AUMD) to dynamically control each realtime job to execute with minimum tasks assignment in any time so as to maximize the number of concurrent real-time jobs. Second, through integrating this deadline scheduler into existing MapReduce scheduler, we develop a two-level scheduler with resource preemption supported, and it could schedule mixed real-time and non-real-time jobs according to their respective performance demands. We implement our scheduler in Hadoop system and experiments running on a real, small-scale cluster demonstrate that it could schedule mixed real-time and nonreal-time jobs to meet their different quality-of-service (QoS) demands.

关键词： Real time systems Schedules Processor scheduling Manganese Quality of service resource management

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Efficient Energy management Using Adaptive Reinforcement Learning-Based scheduling in Large-Scale distributed systems

Efficient Energy Management Using Adaptive Reinforcement Lea...

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international Conference on parallel Processing (ICPP)

作者： Masnida Hussin Young Choon Lee Albert Y. Zomaya Centre of Distributed and High Performance Computing School of Information Technologies University of Sydney NSW Australia

Energy consumption in large-scale distributed systems, such as computational grids and clouds gains a lot of attention recently due to its significant performance, environmental and economic implications. These systems consume a massive amount of energy not only for powering them, but also cooling them. More importantly, the explosive increase in energy consumption is not linear to resource utilization as only a marginal percentage of energy is consumed for actual computational works. This energy problem becomes more challenging with uncertainty and variability of workloads and heterogeneous resources in those systems. This paper presents a dynamic scheduling algorithm incorporating reinforcement learning for good performance and energy efficiency. This incorporation helps the scheduler observe and adapt to various processing requirements (tasks) and different processing capacities (resources). The learning process of our scheduling algorithm develops an association between the best action (schedule) and the current state of the environment (parallel system). We have also devised a task-grouping technique to help the decision-making process of our algorithm. The grouping technique is adaptive in nature since it incorporates current workload and energy consumption for the best action. Results from our extensive simulations with varying processing capacities and a diverse set of tasks demonstrate the effectiveness of this learning approach.

关键词： Program processors Dynamic scheduling Power demand Processor scheduling Energy efficiency Energy consumption

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An Adaptable In-advance and Fairshare Meta-scheduling Architecture to Improve Grid QoS

An Adaptable In-advance and Fairshare Meta-scheduling Archit...

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international workshop on Grid Computing

作者： Luis Tom´s Per-Olov Östberg Blanca Caminero Carmen Carrion Erik Elmroth Dept. of Computing Science Umeå University Umeå Sweden Dept. of Computing Systems University of Castilla-La Mancha Campus Universitario Albacete Spain

Grids are highly variable heterogeneous systems where resources may span multiple administrative domains and utilize heterogeneous schedulers, which complicates enforcement of end-user resource utilization quotas. This work focuses on enhancement of resource utilization quality of service through combination of two systems. A predictive meta-scheduling framework and a distributed fairs hare job prioritization system. The first, SA-Layer, is a system designed to provide scheduling of jobs in advance by ensuring resource availability for future job executions. The second, FS Grid, provides an efficient mechanism for fairs hare-based job prioritization. The integrated architecture presented in this work combines the strengths of both systems and improves perceived end-user quality of service by providing reliable resource allocations adhering to usage allocation policies.

关键词： resource management Quality of service scheduling Computer architecture Convergence Schedules Organizations

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Fair and Efficient Online Adaptive scheduling for Multiple Sets of parallel Applications

Fair and Efficient Online Adaptive Scheduling for Multiple S...

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international Conference on parallel and distributed systems (ICPADS)

作者： Hongyang Sun Yangjie Cao Wen-Jing Hsu School of Computer Engineering Nanyang Technological University Singapore School of Electronic and Information Engineering Xian Jiaotong University China

Both fairness and efficiency are crucial measures for the performance of parallel applications on multiprocessor systems. In this paper, we study online adaptive scheduling for multiple sets of such applications, where each set may contain one or more jobs with time-varying parallelism profile. This scenario arises naturally when dealing with several applications submitted simultaneously by different users in a large parallel system, where both user-level fairness and system-wide efficiency are important concerns. To achieve fairness, we use the equipartitioning algorithm, which evenly splits the available processors among the active job sets at any time. For efficiency, we apply a feedback-driven adaptive scheduler, which periodically adjusts the processor allocations within each set by consciously exploiting the jobs' execution history. We show that our algorithm is competitive for the objective of minimizing the set response time. For sufficiently large jobs, this theoretical result improves upon an existing algorithm that provides only fairness but lacks efficiency. Furthermore, we conduct simulations to empirically evaluate our algorithm, and the results confirm its improved performance using malleable workloads consisting of a wide range of parallelism variation structures.

关键词： Program processors Time factors resource management Processor scheduling Algorithm design and analysis Schedules

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Supporting Deadline Constrained distributed Computations on Grids

Supporting Deadline Constrained Distributed Computations on ...

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international workshop on Grid Computing

作者： Xinghui Zhao Nadeem Jamali Department of Computer Science University of Saskatchewan Saskatoon Canada

The growing popularity of grid and cloud computing has led to a renewed interest in resource control and coordination. The Actor model, which encapsulates objects along with threads of execution, offers a convenient way for scheduling computations' access to resources by way of scheduling of the actor threads. However, efficient Actor implementations do not use a thread for each actor, making implementation of fine-grained resource scheduling decisions difficult. This paper presents our work on integrating mechanisms for deadline assurance into an optimized implementation of Actors. We achieve this by using deadline-driven adaptive scheduling, which prioritizes individual message deliveries and method executions involved in a distributed computation, based on the calculated deadlines by which each must be completed. These deadlines can be efficiently calculated at run-time for an important class of computations which use pipeline interaction style. Additionally, a tuner dynamically balances -- manually or automatically -- the overhead of the control mechanisms against the extent of control exercised. Experimental evaluation shows that the approach offers effective support for timeliness requirements (for multimedia QoS, for example) at the cost of a relatively modest and adjustable overhead.

关键词： Cognition Foundries Message systems Processor scheduling resource management Schedules Quality of service

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Combining Multiple Metrics to Control BSP Process Rescheduling in Response to resource and Application Dynamics

Combining Multiple Metrics to Control BSP Process Rescheduli...

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international Conference on parallel and distributed systems (ICPADS)

作者： Rodrigo da Rosa Righi Lucas Graebin Rafael Bohrer Avila Philippe Olivier Alexadre Navaux Laercio Lima Pilla Programa Interdisciplinar de Pós-Graduao em Computação Aplicada Universidade do Vale do Rio dos Sinos Sao Leopoldo Rio Grande do Sul Brazil Programa de Pós-Graduação em Computação UFRGS Porto Alegre Brazil

This article discusses MigBSP: a rescheduling model that acts on Bulk Synchronous parallel applications running over computational Grids. It combines the metrics Computation, Communication and Memory to make migration decisions. MigBSP also offers efficient adaptations to reduce its overhead. Additionally, MigBSP is infrastructure and application independent and tries to handle dynamicity on both levels. MigBSP's results show application performance improvements of up to 16% on dynamic environments while maintaining a small overhead when migrations do not take place.

关键词： Measurement Program processors Processor scheduling Heuristic algorithms Load management Algorithm design and analysis Computational modeling

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Reducing Fragmentation on Torus-Connected Supercomputers

Reducing Fragmentation on Torus-Connected Supercomputers

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international Symposium on parallel and distributed Processing (IPDPS)

作者： Wei Tang Zhiling Lan Narayan Desai Daniel Buettner Yongen Yu Department of Computer Science Illinois Institute of Technology Chicago IL USA Mathematics and Computer Science Division Argonne National Laboratory Argonne IL USA Argonne Leadership Computing Facility Argonne National Laboratory Argonne IL USA

Torus-based networks are prevalent on leadership-class petascale systems, providing a good balance between network cost and performance. The major disadvantage of this network architecture is its susceptibility to fragmentation. Many studies have attempted to reduce resource fragmentation in this architecture. Although the approaches suggested can make good allocation decisions reducing fragmentation at job start time, none of them considers a job's wall time, which can cause resource fragmentation when neighboring jobs do not complete closely. In this paper, we propose a wall time-aware job allocation strategy, which adjacently packs jobs that finish around the same time, in order to minimize resource fragmentation caused by job length, discrepancy. Event-driven simulations using real job traces from a production Blue Gene/P system at Argonne National Laboratory demonstrate that our wall time-aware strategy can effectively reduce system fragmentation and improve overall system performance.

关键词： resource management Cobalt scheduling Partitioning algorithms Laboratories Three dimensional displays Runtime

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A Highly Scalable Decentralized Scheduler of Tasks with Deadlines

A Highly Scalable Decentralized Scheduler of Tasks with Dead...

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international workshop on Grid Computing

作者： Javier Celaya Unai Arronategui Department of Computer Science and Systems Engineering Universidad de Zaragoza Zaragoza Spain

scheduling of tasks in distributed environments, like cloud and grid computing platforms, using deadlines to provide quality of service is a challenging problem. The few existing proposals suffer from scalability limitations, because they try to manage full knowledge of the system state. To our knowledge, there is no implementation yet that reaches scales of a hundred thousand nodes. In this paper, we present a fully decentralized scheduler, that aggregates information about the availability of the execution nodes throughout the network and uses it to allocate tasks to those nodes that are able to finish them in time. Through simulation, we show that our scheduler is able to operate on different scenarios, from many-task applications in cloud computing sites to volunteer computing projects. Simulations on networks of up to a hundred thousand nodes show very competitive performance, reaching allocation times of under a second and very low overhead in low latency gigabit networks.

关键词： Availability Routing resource management Bandwidth Processor scheduling Accuracy Clustering algorithms

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Autonomic management of workflows on hybrid Grid-Cloud infrastructure 11

Autonomic management of workflows on hybrid Grid-Cloud infra...

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international Conference on Network and Service management

作者： Giuseppe Papuzzo Giandomenico Spezzano Institute for High Performance Computing and Networking (ICAR) CNR-National Research Council of Italy Rende Italy

ISBN: (纸本)9783901882449

Grids can be considered as dominant platforms for large-scale parallel/distributed computing in science and engineering. Clouds allow users to acquire and release resources on-demand. Next generation computing environments will benefit from the combination of Grid and Cloud paradigms providing frameworks that integrate traditional Grid services with on-demand Cloud services. Nowadays, workflows are the preferred means for the combination of services into added value service chains representing functional business processes or complex scientific experiments. A promising way to manage effectively services composition in a dynamic and heterogenous environment is to make the workflow management framework able to self-adapt at runtime to changes in its environment and provide an uniform resource access mechanism over Grid and Cloud infrastructures. Autonomic workflow management systems can support the runtime modification of workflows with the aim of improving their performance and recover from faults determining and provisioning the appropriate mix of Grid/Cloud services with requested QoS. This paper describes Sunflower an innovative P2P agent-based framework for configuring, enacting, managing and adapting workflows on hybrid Grid-Cloud infrastructures. To orchestrate Grid and Cloud services, Sunflower uses a bio-inspired autonomic choreography model and integrates the scheduling algorithm with a provisioning component that can dynamically launch virtual machines in a Cloud infrastructure to provide on-demand services in peak-load situations.

关键词： Quality of service Web services Virtual machining scheduling algorithm Engines Adaptation models Runtime

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