检索结果-内蒙古大学图书馆

15th IEEE international Symposium on parallel and distributed Processing with Applications (ISPA) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Li, Songyuan Huang, Jiwei Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9781538637906

With the growing popularity of the Internet of Things (IoT), energy efficiency has been a critical concern during the design and development of IoT service systems. Meanwhile, edge computing has drawn significant attention as a burgeoning computing paradigm. This paper studies the energy efficiency issue of IoT systems by proposing a joint scheme of resource allocation and task scheduling under the edge computing paradigm. Specifically, dynamic processes of the IoT services and system are formulated by generalized queueing network models, based on which quantitative analyses of performance and energy consumption are conducted. The resource management and task scheduling are formulated by Markov Decision Process (MDP), which can balance the tradeoff between energy costs and QoS requirements. To attack the challenge of MDP search space explosion due to the large scale of IoT systems, Ordinal Optimization (OO) techniques are applied to the MDP algorithms, which are able to significantly narrow the search of MDP by slightly softening the optimization objective to a good enough subset. Finally, we conduct simulation experiments based on real-world IoT data. Evaluations and comparisons demonstrate that our approach is effective and efficient in practice.

关键词： Internet of Things (IoT) resource management task scheduling energy efficiency Markov Decision Process Ordinal Optimization

来源：评论

学校读者我要写书评

暂无评论

Load balancing algorithms in heterogeneous web cluster

Load balancing algorithms in heterogeneous web cluster

引用

international Interdisciplinary PhD workshop (IIPhDW)

作者： Fatma Mbarek Volodymyr Mosorov Institute of Applied Computer Science Lodz University of Technology Lodz Poland

ISBN: (纸本)9781538661444

The load balancing is dedicated for distributed and parallel systems to manage the web cluster. It distributes the load among web servers using several scheduling algorithms. This paper investigates a detailed comparison of some HAProxy algorithms in heterogeneous web cluster as well as the meta-heuristic methods that inspired by the insects' colonies behavior such as Ant Colony Optimization algorithm and Honey Bee algorithm. The basic idea of meta-heuristic methods is to solve combinatorial optimization problems.

关键词： Load management Clustering algorithms Heuristic algorithms Ant colony optimization Optimization Computer science Servers

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Job scheduling in Computational Grid

引用

international JOURNAL OF distributed systems AND TECHNOLOGIES 2018年第2期9卷 1-15页

作者： Ghosh, Tarun Kumar Das, Sanjoy Haldia Inst Technol Dept Comp Sci & Engn Haldia India Kalyani Univ Dept Engn & Technol Studies Kalyani W Bengal India

scheduling jobs in computational Grids is considered as NP-complete problem owing to the heterogeneity of shared resources. The resources belong to many distributed administrative domains that enforce various management policies. Therefore, the use of meta-heuristics are more appropriate option in obtaining optimal results. In this article, a novel hybrid population-based global optimization algorithm, called the Hybrid Firefly Algorithm and the Differential Evolution (HFA-DE), is proposed by combining the merits of both the Firefly Algorithm and Differential Evolution. The Firefly Algorithm and the Differential Evolution are executed in parallel to support information sharing amongst the population and thus enhance searching efficiency. The proposed HFA-DE algorithm reduces the schedule makespan, processing cost, and improves resource utilization. The HFA-DE is compared with the standard Firefly Algorithm, the Differential Evolution and the Particle Swarm Optimization algorithms on all these parameters. The comparison results exhibit that the proposed algorithm outperforms the other three algorithms.

关键词： Computational Grid Differential Evolution Firefly Algorithm Job scheduling Makespan Particle Swarm Optimization resource Utilization

来源：评论

学校读者我要写书评

暂无评论

A Periodic Task-Oriented scheduling Architecture in Cloud Computing

A Periodic Task-Oriented Scheduling Architecture in Cloud Co...

引用

IEEE international Conference on Big Data and Cloud Computing (BdCloud)

作者： Peng Zhang Yan Li Hailun Lin Jianwu Wang Chuang Zhang Institute of Information Engineering Chinese Academy of Sciences Beijing China National Computer Network Emergency Response and Coordination Center Beijing China Department of Information Systems University of Maryland Baltimore County USA

ISBN: (纸本)9781728111421;9781728111414

With the rapid development of cloud computing, more and more applications are deployed in the cloud. This trend brings cloud infrastructure providers to build distributed data centers to provide physical resources for multi-domain-deployed jobs of these applications. However, the heterogeneity of resources and the different execution periodicity of tasks in jobs pose great challenges to the efficiency of large scale job executions. To solve this problem, we propose a periodic task-oriented scheduling architecture in cloud computing. This architecture provides resource domains for the scheduler to shield the details of the underlying heterogeneous resources. Moreover, it implements job-level scheduling and task-level scheduling by our proposed PTS algorithm and RMDU4T algorithm to improve the execution efficiency and resource utilization. Experimental results show the proposed architecture can reduce the scheduling times to less than 10% and reduce the average job completion time by 10%~40% under different distribution of tasks and resources, and the CPU resource utilization is improved by about 15%.

关键词： Task analysis scheduling Cloud computing Processor scheduling Data centers resource management Computer architecture

来源：评论

学校读者我要写书评

暂无评论

Virtual Job management and scheduling for Media Services 22

Virtual Job Management and Scheduling for Media Services

引用

IEEE 22nd international workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

作者： Tzolas, Ioannis Tselios, Christos Univ Patras Comp Sci Dept Patras Greece Univ Patras Wireless Telecommun Lab Patras Greece

ISBN: (纸本)9781509063024

The necessity for distributed parallel multimedia processing capacity in the multimedia-aware cloud derives from the fact that it must provide both data storage and computational resources to the whole number of interconnected users. When an application or a service becomes popular, the underlying system needs to scale up efficiently thus introducing the distributed factor into the overall scheme. Coordination, job scheduling and efficient task management have always been an area of great importance in the design of distributed computing clusters and will continue to be in the 5G networking era, due to the extensive hardware softwarization it introduces. This paper presents some of the limitations that render efficient job and task management problematic in a vastly distributed system such as a Media-Aware cloud, the most important scientific models that have been developed for describing those limitations, along with the dominant set of software implementations that provide a certain degree of solution to these thorny issues. It becomes clear that the design and implementation phases of a distributed virtual resource management module does not need to take place from scratch, since there are several relatively mature software packages which seem to provide the necessary degree of functionality needed in production-ready deployments.

关键词： Cloud Computing Media Services Virtualisation Schedulers

来源：评论

学校读者我要写书评

暂无评论

An Approach to Forecast Queue Time in Adaptive scheduling: How to Mediate System Efficiency and Users Satisfaction

引用

international JOURNAL OF parallel PROGRAMMING 2017年第5期45卷 1164-1193页

作者： Barone, G. B. Boccia, V. Bottalico, D. Campagna, R. Carracciuolo, L. Laccetti, G. Lapegna, M. Univ Naples Federico II Naples Italy Italian Natl Inst Nucl Phys Naples Italy Italian Natl Res Council Naples Italy

The minimisation of the total cost of ownership is hard to be faced by the owners of large scale computing systems, without affecting negatively the quality of service for the users. Modern datacenters, often included in distributed environments, appear to be "elastic", i.e., they are able to shrink or enlarge the number of local physical or virtual resources, also by recruiting them from private/public clouds. This increases the degree of dynamicity, making the infrastructure management more and more complex. Here, we report some advances in the realisation of an adaptive scheduling controller (ASC) which, by interacting with the datacenter resource manager, allows an effective and an efficient usage of resources. In particular, we focus on the mathematical formalisation of the ASC's kernel that allows to dynamically configure, in a suitable way, the datacenter resources manager. The described formalisation is based on a probabilistic approach that, starting from both a hystorical resources usage and on the actual users request of the datacenter resources, identifies a suitable probability distribution for queue time with the aim to perform a short term forecasting. The case study is the SCoPE datacenter at the University of Naples Federico II.

关键词： Adaptive scheduling resources management Large scale and distributed systems Queue time forecasting

来源：评论

学校读者我要写书评

暂无评论

Modeling of applications and hardware to explore task mapping and scheduling strategies on a heterogeneous micro-server system 31

Modeling of applications and hardware to explore task mappin...

引用

31st IEEE international parallel and distributed Processing Symposium workshops (IPDPS)

作者： Zaourar, Lilia Aba, Massinissa Ait Briand, David Philippe, Jean-Marc CEA LIST Comp & Design Environm Lab F-91191 Gif Sur Yvette France

ISBN: (纸本)9780769561493

Many of todays important applications of our everyday lives, e.g. weather forecast, design of plane and car shapes, medical analysis or even search engine queries depend on massively-parallel computer programs that are executed in data centers hosting thousands of computers. A large amount of electrical energy is used to power them, and it is of primary importance to compute more efficiently to sustain the increasing demand of computing power while keeping energy consumption reasonable. One promising research path in this domain is heterogeneous systems. The rationale for that is that at least parts of applications execute more efficiently depending on the computing resource (processors, accelerators, etc.). Nevertheless, the exploitation of these heterogeneous platforms raises new challenges in terms of application management optimization on available computing resources. The aim of our work is to determine effective algorithms to exploit these heterogeneous platforms by finding the best mapping and scheduling of an application to optimize the execution time and energy consumption with respect to various constraints. To achieve this goal, there is a need of a detailed modeling of the applications and the underlying hardware to be able to find realistic solutions. In this paper, we propose such as model, provide two implementations with state-of-the-art tools and preliminary mapping and scheduling numerical results.

关键词： heterogeneous computing resource management mapping scheduling energy consumption

来源：评论

学校读者我要写书评

暂无评论

Towards Energy-Efficient scheduling with Batch Processing for Instance-Intensive Cloud Workflows

Towards Energy-Efficient Scheduling with Batch Processing fo...

引用

IEEE international Conference on Big Data and Cloud Computing (BdCloud)

作者： Zhibin Wang Yiping Wen Jinjun Chen Buqing Cao Feiran Wang Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science & Technology China School of Computer Science and Engineering Hunan University of Science & Technology China

ISBN: (纸本)9781728111421;9781728111414

Instance-intensive workflow applications are widely used in the areas of electronic commerce, advanced manufacture, etc. However, existing studies normally do not consider the problem of reducing their energy consumption by utilizing the advantages of batch processing strategy and the characters of instance-intensive workflow applications. This paper presents an Energy-efficient Instance-intensive Cloud workflows scheduling method with Batch processing, named EICB. Technically, the method is promoted with strategies to merge several activity instances and balance resource utilization of physical machines (PMs) to improve energy efficiency for instance-intensive cloud workflows. The effectiveness and efficiency of the proposed method are validated by extensive experiments.

关键词： Batch production systems Energy consumption Cloud computing resource management Energy efficiency Job shop scheduling Monitoring

来源：评论

学校读者我要写书评

暂无评论

Argo NodeOS: Toward Unified resource management for Exascale 31

Argo NodeOS: Toward Unified Resource Management for Exascale

引用

31st IEEE international parallel and distributed Processing Symposium (IPDPS)

作者： Perarnau, Swann Zounmevo, Judicael A. Dreher, Matthieu Van Essen, Brian C. Gioiosa, Roberto Iskra, Kamil Gokhale, Maya B. Yoshii, Kazutomo Beckman, Pete Argonne Natl Lab Argonne IL 60439 USA Pacific Northwest Natl Lab Richland WA 99352 USA Lawrence Livermore Natl Lab Livermore CA USA

ISBN: (纸本)9781538639146

Exascale systems are expected to feature hundreds of thousands of compute nodes with hundreds of hardware threads and complex memory hierarchies with a mix of on-package and persistent memory modules. In this context, the Argo project is developing a new operating system for exascale machines. Targeting production workloads using workflows or coupled codes, we improve the Linux kernel on several fronts. We extend the memory management of Linux to be able to subdivide NUMA memory nodes, allowing better resource partitioning among processes running on the same node. We also add support for memory-mapped access to node-local, PCIe-attached NVRAM devices and introduce a new scheduling class targeted at parallel runtimes supporting user-level load balancing. These features are unified into compute containers, a containerization approach focused on providing modern HPC applications with dynamic control over a wide range of kernel interfaces. To keep our approach compatible with industrial containerization products, we also identify contentions points for the adoption of containers in HPC settings. Each NodeOS feature is evaluated by using a set of parallel benchmarks, miniapps, and coupled applications consisting of simulation and data analysis components, running on a modern NUMA platform. We observe out-of-the-box performance improvements easily matching, and often exceeding, those observed with expert-optimized configurations on standard OS kernels. Our lightweight approach to resource management retains the many benefits of a full OS kernel that application programmers have learned to depend on, at the same time providing a set of extensions that can be freely mixed and matched to best benefit particular application components.

关键词： containers exascale operating systems resource management

来源：评论

学校读者我要写书评

暂无评论

Efficient Task Assignment and scheduling on MPSoC with STT-RAM based Hybrid SPMs Considering Data Allocation 15

Efficient Task Assignment and Scheduling on MPSoC with STT-R...

引用

15th IEEE international Symposium on parallel and distributed Processing with Applications (ISPA) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Feng, Weiwei Gu, Shouzhen Yang, Yanqin Zhuge, Qingfeng Sha, Edwin H-M East China Normal Univ Shanghai Key Lab Trustworthy Comp Shanghai Peoples R China Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9781538637906

Be aware enough of the fact that performance gap between CPU and memory, employing novel memory techniques in embedded systems is a feasible way to reduce the performance gap. For MPSoC which is equipped with SRAM and STT-RAM based hybrid SPMs, data can be effectively parallel accessed. This paper explores data allocation, task assignment and scheduling on MPSoC with SRAM and STT-RAM based hybrid SPMs. We proposed a mixed integer quadratically constrained program(MIQCP) formulations and a heuristic method (HA) to generates optimal and near optimal data allocation, task assignment, and scheduling solution. Experimental results show that MIQCP and HA can reduce 32.6% and 20.1% schedule length on average.

关键词： Task analysis Random access memory resource management Schedules System-on-chip Computer architecture Clocks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：