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A Combined Dual-stage Framework for Robust scheduling of Scientific Applications in Heterogeneous Environments with Uncertain Availability

A Combined Dual-stage Framework for Robust Scheduling of Sci...

引用

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

作者： Florina M. Ciorba Timothy Hansen Srishti Srivastava Ioana Banicescu Anthony A. Maciejewski Howard Jay Siegel Center for Information Services and High Performance Computing Technische Universität Dresden Dresden Germany Department of Electrical and Computer Engineering Colorado State University Fort Collins CO USA Department of Computer Science and Engineering Mississippi State University Mississippi MS USA Department of Electrical and Computer Engineering Department of Computer Science Colorado State University Fort Collins CO USA

scheduling parallel applications on existing or emerging computing platforms is challenging, and, among other attributes, must be efficient and robust. A dual-stage framework is proposed in this paper to evaluate the robustness of efficient resource allocation and dynamic load balancing of scientific applications in heterogeneous computing environments with uncertain availability. The first stage employs robust resource allocation heuristics, while the second stage incorporates robust dynamic loop scheduling techniques. The combined dual-stage framework constitutes a comprehensive framework that enables and provides guarantees for the robust execution of scientific applications in computing systems where uncertainty is caused by various unpredictable perturbations. The paper reports on studies for determining the best techniques to be used for each stage that: (a) maximize the probability that the system make span satisfies a deadline, and (b) minimize the system make span for every given availability level in the system. The usefulness and benefits of the proposed framework are demonstrated via a small scale example.

关键词： Availability Robustness resource management Uncertainty Runtime Dynamic scheduling Program processors

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BASED ON CLOUD COMPUTING PROGRAMMING MODEL AND ADAPTIVE resource management RESEARCH

BASED ON CLOUD COMPUTING PROGRAMMING MODEL AND ADAPTIVE RESO...

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2012 IEEE 2nd international Conference on Cloud Computing and Intelligence systems

作者： Yanjun Cui Hong Gan Dan Pan Guangdong Pei Zheng college Guangzhou City Construction College

cloud computing through the basic idea is to construct a large-scale cluster system based data center, the cluster of resources (such as hardware, development platform for virtual form) to provide users with a resource pool. In this paper we in cloud computing and data processing techniques work, introduced emphatically based on cloud computing distributed parallel programming model, a distributed task scheduling and cloud computing platform adaptive resource scheduling and management, and the difficulties and challenges are to accept;at the same time, but also to the future potential breakthrough direction the prospect of.

关键词： cloud computing programming model adaptive

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DMSS: A Dynamic Memory scheduling System in Server Consolidation Environments

DMSS: A Dynamic Memory Scheduling System in Server Consolida...

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IEEE international Symposium on Object/Component/Service-Oriented Real-Time distributed Computing workshops (ISORCW)

作者： Lanzheng Liu Rui Chu Yongchun Zhu Pengfei Zhang Liufeng Wang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Systems and Technology Group IBM China Beijing China

As the foundation of cloud computing, Server consolidation allows multiple computer infrastructures running as virtual machines in a single physical node. It improves the utilization of most kinds of resource but memory under x86 architecture. Because of inaccurate memory usage estimate and the lack of memory resource management, there is much service performance degradation in data centers, even though they have occupied a large amount of memory. Furthermore, memory becomes insufficient for a physical server when a lot of virtual machines depend on it. In order to improve this, we present a dynamic memory scheduling system called DMSS, which can manage memory resources in server consolidation environments and allocate memory among virtual machines on demand. We have designed and implemented the corresponding memory scheduling policy based on Xen virtualization platform to enhance memory efficiency and achieve service level agreement. The benchmark shows that DMSS can make an accurate and rapid response to memory changes and save more than 30% physical memory with less than 5% performance degradation. DMSS actually brings in economic benefits to cloud service providers because more virtual machines can be accommodated at lower costs.

关键词： Memory management Virtual machine monitors Monitoring resource management Servers Dynamic scheduling Degradation

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Genetic approach for real-time scheduling on multiprocessor systems

Genetic approach for real-time scheduling on multiprocessor ...

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IEEE international Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Gheorghe Sebestyen Anca Hangan Computer Science Department Technical University of Cluj Napoca Romania

Real-time scheduling of concurrent tasks on multiprocessor systems is a complex job, which implies finding a feasible solution in a multi-dimensional space. In order to reduce the search time we propose a genetic approach for two important aspects of the scheduling problem: task allocation and deadline assignment. We combine a genetic search engine with a simulation tool in order to find a scheduling strategy that assures the fulfillment of all time restrictions. Our system model includes a wide range of multiprocessor systems, from parallel systems to network-based distributed ones and from independent task sets to chains of tasks organized as concurrent transactions. The paper gives details regarding the adaptation of genetic operators for the scheduling problem.

关键词： Sociology Statistics Genetic algorithms Processor scheduling resource management Real-time systems scheduling

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Automatic VM Allocation for Scientific Application

Automatic VM Allocation for Scientific Application

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international Conference on parallel and distributed systems (ICPADS)

作者： Sarunya Pumma Tiranee Achalakul Li Xiaorong Department of Computer Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Technology and Research Institute of High Performance Computing Agency for Science Singapore

Cloud has been the main technology utilized as a high performance computing (HPC) platform. The characteristics of cloud can satisfy a large scale processing required by scientific applications, which are mostly compute-intensive with big data. Cloud can also reduce the computing cost through sharing and virtualizing of resources. In the cloud, a large number of virtual machines (VM) can be generated on demands. In order to obtain the optimal cost and high efficiency in the task execution on the public cloud, the suitable amount of virtual machines should be properly determined prior to the start of the computation. Moreover, the application should be effectively partitioned and distributed onto the virtual machines. In this paper, we propose an automatic mechanism to allocate the optimal numbers of resources in the cloud. The novel resource estimation model and scheduling algorithm are presented. We select an analytic application with high level of computations in the field of epidemic forecast to demonstrate the use of the designed mechanism. Experimental studies have been conducted to examine the resource prediction accuracy and the scalability of running the application on the cloud.

关键词： Parameter estimation Predictive models Cloud computing Mathematical model resource management Virtual machining Computational modeling

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Autonomous and Energy-Aware management of Large-Scale Cloud Infrastructures

Autonomous and Energy-Aware Management of Large-Scale Cloud ...

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

作者： Eugen Feller Christine Morin INRIA Centre Rennes-Bretagne Atlantique Rennes France

ISBN: (纸本)9781467309745

With the advent of cloud computing and the need for increasing amount of computing power, cloud infrastructure providers are now facilitating the deployment of large-scale data centers. In order to efficiently manage such environments three important properties have to be fulfilled by their resource management frameworks: (1) scalability, (2) autonomy (i.e. self-organization and healing), (3) energy-awareness. However, existing open-source cloud management stacks (e.g. Eucalyptus, Nimbus, Open Nebula, Open Stack) have a high degree of centralization and limited power management support. In this context, this PhD thesis focuses on more scalable, autonomic, and energy-aware resource management frameworks for large-scale cloud infrastructures. Particularly, a novel virtual machine (VM) management system based on a self-organizing hierarchical architecture called Snooze is proposed. In order to conserve energy, Snooze automatically transitions idle servers into a low-power mode (e.g. suspend). To favor idle times the system integrates a nature-inspired VM consolidation algorithm based on the Ant Colony Optimization (ACO).

关键词： Cloud computing Heart beat Scalability Fault tolerance Fault tolerant systems scheduling Monitoring

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Page-Based Memory Allocation Policies of Local and Remote Memory in Cluster Computers

Page-Based Memory Allocation Policies of Local and Remote Me...

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international Conference on parallel and distributed systems (ICPADS)

作者： Mónica Serrano Salvador Petit Julio Sahuquillo Rafael Ubal Houcine Hassan José Duato Department of Computer Engineering (DISCA) Universitat Politècnica de València Valencia Spain

Main memory latencies have a strong impact on the overall execution time of the applications. The need of efficiently scheduling the costly DRAM memory resources in the different motherboards is a major concern in cluster computers. Most of these systems implement remote access capabilities which allow the OS to access to remote memory. In this context, efficient scheduling becomes even more critical since remote memory accesses may be several orders of magnitude higher than local accesses. These systems typically support interleaved memory at cache-block granularity. In contrast, in this paper we explore the impact on the system performance when allocating memory at OS page granularity. Experimental results show that simply supporting interleaved memory at OS page granularity is a feasible solution that does not impact on the performance of most of the benchmarks. Based on this observation we investigated the reasons of performance drops in those benchmarks showing unacceptable performance when working at page granularity. The results of this analysis lead us to propose two memory allocation policies, namely on-demand (OD) and Most-accessed in-local (Mail). The OD policy first places the requested pages in local memory, once this memory region is full, the subsequent memory pages are placed in remote memory. This policy shows good performance when the most accessed pages are requested and allocated before than the least accessed ones, which as proven in this work, is the most common case. This simple policy reaches performance improvements by 25% in some benchmarks with respect to a typical block interleaving memory system. Nevertheless, this strategy has poor performance when a noticeable amount of the least accessed pages are requested before than the most accessed ones. This performance drawback is solved by the Mail allocation policy by using profile information to guide the allocation of new pages. This scheme always outperforms the baseline block interleaving

关键词： Benchmark testing resource management Computers Memory management Degradation Bismuth Postal services

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A Sequential Cooperative Game Theoretic Approach to Storage-Aware scheduling of Multiple Large-Scale Workflow Applications in Grids

A Sequential Cooperative Game Theoretic Approach to Storage-...

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international workshop on Grid Computing

作者： Rubing Duan Radu Prodan Xiaorong Li Institute of High Performance Computing Agency for Science Technology and Research (ASTAR) Singapore Institute of Computer Science University of Innsbruck Innsbruck Austria

scheduling large-scale applications in heterogeneous Grid and Cloud systems is a fundamental NP-complete problem for obtaining good performance and execution costs. We address the problem of scheduling an important class of large-scale Grid applications inspired from real-world, characterised by a large number of homogeneous, concurrent, and computationally-intensive tasks that are the main sources of performance, cost, and storage bottlenecks. We propose a new formulation of this problem based on a cooperative distributed game theoretic method for makespan and cost optimisation of a multiple such applications while fulfilling important storage constraints. We present experimental results using simulation and real-world applications that demonstrate the effectiveness of our method in terms of the solution delivered, algorithm execution time, and fairness compared to other related approaches.

关键词： Games resource management Program processors Computational modeling Schedules scheduling algorithms

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Job scheduling with License Reservation: A Semantic Approach

Job Scheduling with License Reservation: A Semantic Approach

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19th international Euromicro Conference on parallel, distributed and Network-Based Processing (PDP)

作者： Ejarque, Jorge Micsik, Andras Sirvent, Raul Pallinger, Peter Kovacs, Laszlo Badia, Rosa M. Barcelona Supercomp Ctr Grid Comp & Clusters Grp Barcelona Spain Spanish Natl Res Council IIIA CSIC Arti Cial Intelligence Res Inst Barcelona Spain Univ Politecn Cataluna Comp Architecture Dept Barcelona Spain Hungarian Acad Sci MTA SZTAKI Comp & Automat Res Inst Distributed Syst Dept Budapest Hungary

ISBN: (纸本)9780769543284

The license management is one of the main concerns when Independent Software Vendors (ISV) try to distribute their software in computing platforms such as Clouds. They want to be sure that customers use their software according to their license terms. The work presented in this paper tries to solve part of this problem extending a semantic resource allocation approach for supporting the scheduling of job taking into account software licenses. This approach defines the licenses as another type of computational resource which is available in the system and must be allocated to the different jobs requested by the users. License terms are modeled as resource properties, which describe the license constraints. A resource ontology has been extended in order to model the relations between customers, providers, jobs, resources and licenses in detail and make them machine processable. The license scheduling has been introduced in a semantic resource allocation process by providing a set of rules, which evaluate the semantic license terms during the job scheduling.

关键词： multi-agent semantics scheduling resource allocation software licenses grid computing cloud computing distributed systems

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Co-management of Power and Performance in Virtualized distributed Environments

Co-management of Power and Performance in Virtualized Distri...

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6th international Conference on Grid and Pervasive Computing (GPC)/international workshop on Health and Well-Being Technologies and Services for Elderly (HWTS)/international workshop on Self-Managing Solutions for Smart Environments (S3E)

作者： Sharifi, Mohsen Najafzadeh, Mahsa Salimi, Hadi Iran Univ Sci & Technol Distributed Syst Lab Sch Comp Engn Tehran Iran

ISBN: (纸本)9783642207549

Rapid growth of large-scale applications and their widespread use in research and industry has led to dramatic increases in energy consumption in enterprise data centers and large-scale distributed systems such as Grids. Any attempt at reducing the energy consumption without concern for performance can be destructive and deteriorate the overall efficiency of data centers and large-scale distributed systems running such applications. In this paper, we present an optimization model for resource management in virtualized distributed systems to minimize power costs automatically while satisfying performance constraints. The objective of our model is to keep the utilization of servers near to an optimum point to prevent performance degradation. The model includes two objective functions, one for power costs and another for performance. Using the objective functions, we present a scheduling algorithm to place a set of virtual machines on a set of servers dynamically so that to integrate power management with performance management. We show experimentally that the proposed scheduler consumes approximately 24% less energy than static power management techniques while maintaining comparable performance.

关键词： power management performance virtualization technology consolidation

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