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Mobile Element Path Planning for Gathering Transit-Time Constrained Data

Mobile Element Path Planning for Gathering Transit-Time Cons...

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IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Khaled Almi'ani Anastasios Viglas Mohammed Aalsalem Faculty of Information Technology Al-Hussein Bin Talal University Maan Jordan School of Information Technologies University of Sydney NSW Australia Faculty of Computer and Information System Jazan University Saudi Arabia

In this paper we investigate the problem of gathering the data in wireless sensor network using a single Mobile Element. In particular we consider the case where the data are produced by measurements and they need to be delivered to a predefined sink within a given time interval from the time the measurement takes place. A mobile element travels the network in predefined paths, collect the data from the nodes, and deliver them to the sink by a single long-distance transmission. In this problem, the length of the mobile element path is bounded by pre-determined length. this path will visit a subset of the nodes. these selected nodes will work as caching points and will aggregate the other nodes' data. the caching point nodes are selected with the aim of reducing the energy expenditures due to multi-hop forwarding. We provide a heuristic-based solution for this problem. We evaluate the performance of our algorithm by comparing it to the optimal solution as well as the best well-known algorithms from the literature.

关键词： Mobile communication Routing Sensors Mobile computing Energy consumption Algorithm design and analysis Wireless sensor networks

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Enacting SLAs in Clouds Using Rules

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17th international Euro-Par conference on parallel Processing

作者： Maurer, Michael Brandic, Ivona Sakellariou, Rizos Vienna Univ Technol Distributed Syst Grp Argentinierstr 8 A-1040 Vienna Austria Univ Manchester Sch Comp Sci Manchester Lancs England

ISBN: (纸本)9783642234002

the emergence of Cloud computing raises the question of dynamically allocating resources of physical (PM) and virtual machines (VM) in an on-demand and autonomic way. Yet, using Cloud computing infrastructures efficiently requires fulfilling three partially contradicting goals: first, achieving low violation rates of Service Level Agreements (SLA) that define non-functional goals between the Cloud provider and the customer;second, achieving high resource utilization;and third achieving the first two issues by as few time- and energy consuming reallocation actions as possible. To achieve these goals we propose a novel approach with escalation levels to divide all possible actions into five levels. these levels range from changing the configuration of VMs over migrating them to other PMs to outsourcing applications to other Cloud providers. In this paper we focus on changing the resource configuration of VMs in terms of storage, memory, CPU power and bandwidth, and propose a knowledge management approach using rules with threat thresholds to tackle this problem. Simulation reveals major improvements as compared to recent related work considering SLA violations, resource utilization and action efficiency, as well as time performance.

关键词： Knowledge management

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Communication-Aware Task Partition and Voltage Scaling for Energy Minimization on Heterogeneous parallel Systems

Communication-Aware Task Partition and Voltage Scaling for E...

引用

IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Guibin Wang Wei Song National Laboratory for Parallel and Distributed Processing National University of Defense Technology China

Heterogeneous parallel systems have become popular in general purpose computing and even high performance computing fields. there are many studies focused on harnessing heterogeneous parallel processing for better performance. However the energy optimization for heterogeneous system has not been well studied. Owing to the differences in performance and energy consumption, the energy optimization technique for heterogeneous system is different from the existing methods designed for homogeneous system. Besides typical voltage scaling method, reasonable task partitioning is also an essential method for optimizing energy consumption on heterogeneous systems. through partitioning a data parallel task and mapping sub-tasks onto several processors, one could achieve better performance and reduced energy consumption. As the computation cost reduces with specific accelerators, the communication overhead becomes more prominent. therefore, the task partition optimization should holistically consider the computation improvement and communication overhead to achieve higher energy efficiency. Typically, task partition and voltage scaling are not orthogonal and influence the effect of each other in the energy optimization problem. In order to harness both two knobs efficiently, this paper proposes an integer linear programming (ILP) based energy-optimal solution designed for heterogeneous system. We present a case study of optimizing MGRID benchmark on a typical CPU-GPU heterogeneous system. the experimental results demonstrate that the proposed method could exploit the heterogeneity in different processors and achieve improved energy efficiency.

关键词： Graphics processing unit Energy consumption Power demand Optimization Kernel Schedules

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Optimization of Query Results in Peer-to-Peer Database Supported by Semantics

Optimization of Query Results in Peer-to-Peer Database Suppo...

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IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Carlos Roberto Valêncio Leandro Rincon Costa Juliano Augusto Carreira Mario Luiz Tronco Depto. de Ciências de Computação e Estatística Universidade Estadual Paulista – Unesp São José do Rio Preto Brazil Departamento de Ciências de Computação e Estatística Universidade Estadual Paulista Sao Jose do Rio Preto Brazil Departamento de Engenharia Mecânica Universidade de São Paulo – USP São Carlos Brazil

the development of new technologies that use peer-to-peer networks grows every day, with the object to supply the need of sharing information, resources and services of databases around the world. Among them are the peer-to-peer databases that take advantage of peer-to-peer networks to manage distributed knowledge bases, allowing the sharing of information semantically related but syntactically heterogeneous. However, it is a challenge to ensure the efficient search for information without compromising the autonomy of each node and network flexibility, given the structural characteristics of these networks. On the other hand, some studies propose the use of ontology semantics by assigning standardized categorization of information. the main original contribution of this work is the approach of this problem with a proposal for optimization of queries supported by the Ant Colony algorithm and classification though ontologies. the results show that this strategy enables the semantic support to the searches in peer-to-peer databases, aiming to expand the results without compromising network performance.

关键词： Peer to peer computing Ontologies Routing Semantics Optimization distributed databases Heuristic algorithms

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CHMasters: A Scalable and Speed-Efficient Metadata Service in distributed File System

CHMasters: A Scalable and Speed-Efficient Metadata Service i...

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IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Min Xu Junrui Zhou Wei Zhou Hong An Department of Computer Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Computer System and Architecture Chinese Academy of Sciences Beijing China

distributed file system (DFS) is playing important roles of supporting large distributed data-intensive applications to meet storage needs. Typically, the design of DFS, such as GFS in Google, DMS in Cisco and TFS in Alibaba, is driven by observations of specific application workloads, internal demands and technological environment. In such systems, the metadata service is a critical factor that can affect the file system performance and availability to a great degree. Five requirements have been summarized for the metadata service: location transparent file service, smart director, efficient speed, strong scalability and friendly collaborator. In this paper, we present metadata service module called CH Masters in our DFS. Consistent hashing protocol is used to relieve potential hot spots on name servers. Files' metadata and master nodes are mapped into the same hash space by consistent hash function. And then files' metadata are scattered to master nodes by clockwise "closest" principle. Chunk server acts as a client when report its chunks info. Only a small proportion of files' metadata will be rehashed when master nodes state change. A new scalable file mapping strategy is also proposed to map file sizes from few MB to several GB efficiently. After intensive experiments, it shows CH Masters is satisfying the above five requirements.

关键词： Protocols File systems Google Servers Computer architecture Clocks Testing

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IPDPS 2011 Wednesday 25th Year Panel: What's ahead?

IPDPS 2011 Wednesday 25th Year Panel: What's ahead?

引用

international Symposium on parallel and distributed Processing (IPDPS)

作者： Per Stenström Doug Burger Wen-mei Hwu Vipin Kumar Kunle Olukotun David Padua Burton Smith Chalmers University of Technology Sweden

Summary form only given, as follows. parallel computing has become ubiquitous and relates to challenging computational problems in science via business-driven computing to mobile computing. the scope has widened dramatically over the last decade. this panel will debate and speculate on how the parallel computing landscape is expected to change in the years to come. Areas of focus will include: (1) computing platforms: How will we be able to maintain the performance growth of the past and what will be the major challenges in the next 10 years and beyond that? What technical barriers are anticipated and what disruptive technologies are behind the corner? (2) Software: How will software infrastructures evolve to meet performance requirements in the next 10 years and beyond? How will we ever be able to hide parallelism obstacles for the masses and what is the road forward towards that? (3) Algorithms: What will be the major computational problems to tackle in the next 10 years and beyond? What are the most challenging algorithmic problems to solve? (4) applications: What will be the next wave of grand challenge problems to focus on in the next 10 years and beyond? What will be the major performance driving applications in the general and mobile computing domains? A record of the panel discussion was not made available for publication as part of the conference proceedings.

关键词： parallel processing Mobile computing distributed processing Disruptive technologies Software Roads Software algorithms

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Perspective of the Large Scale Data Facility (LSDF) Supporting Nuclear Fusion applications

Perspective of the Large Scale Data Facility (LSDF) Supporti...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Rainer Stotzka Volker Hartmann thomas Jejkal Michael Sutter Jos van Wezel Marcus Hardt Ariel García Rainer Kupsch Serguei Bourov Karlsruhe Institute of Technology (KIT) Institute for Data Processing and Electronics (IPE) Karlsruhe Germany Karlsruhe Institute of Technology (KIT) Steinbuch Centre for Computing (SCC) Karlsruhe Germany

To cope with the growing requirements of data intensive scientific experiments, models and simulations the Large Scale Data Facility(LSDF) at KIT aims to support many scientific disciplines. the LSDF is a distributed storage facility at Exabyte scale providing storage, archives, data bases and meta data repositories. Open interfaces and APIs support a variety of access methods to the highly available services for high throughput data applications. Tools for an easy and transparent access allow scientists to use the LSDF without bothering with the internal structures and technologies. In close cooperation with the scientific communities the LSDF provides assistance to efficiently organize data and metadata structures, and develops and deploys community specific software on the directly connected computing infrastructure.

关键词： Communities Software Protocols Standards organizations Organizations Microscopy distributed databases

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ePUMA embedded parallel DSP processor with Unique Memory Access

ePUMA embedded parallel DSP processor with Unique Memory Acc...

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international conference on Information, Communications and Signal Processing

作者： Dake Liu Andreas Karlsson Joar Sohl Jian Wang Magnus Petersson Wenbiao Zhou Department of Electrical Engineering Linköping University Sweden ASIP College of Information and electronics Beijing Institute of Technology China

ISBN: (纸本)9781457700293

computing unto 100GOPS without cooling is essential for high-end embedded systems and much required by markets. A novel master-slave multi-SIMD architecture and its kernel (template) based parallel programming flow is thus introduced as a parallel signal processing platform, ePUMA, embedded parallel DSP processor with Unique Memory Access. It is an on chip multi-DSP-processor (CMP) targeting to predictable signal processing for communications and multimedia. the essential technologies are to separate the processing of control stream from parallel computing, and to separate parallel data access from parallel arithmetic computing kernels. By separations, the computation and data access can be orthogonal both in hardware and in programs. Orthogonal operations can therefore be executed in parallel and the run time cost of data access can be minimized. Benchmark shows that the computing performance therefore reaches about 80% of the hardware limit. Less than 40% of the hardware limit can be reached by normal processors. the unique SIMD memory subsystem architecture offers programmable conflict free parallel data accesses. Programming flow and tools are also developed to support coding on the unique hardware architecture. A prototype on FPGA shows especially high performance over silicon cost.

关键词： Computer architecture Digital signal processing Kernel Multimedia communication Silicon Baseband Power demand

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CREST: Towards Fast Speculation of Straggler Tasks in MapReduce

CREST: Towards Fast Speculation of Straggler Tasks in MapRed...

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IEEE international conference on e-Business Engineering (ICEBE)

作者： Lei Lei Tianyu Wo Chunming Hu State Key Laboratory of Software Development Environment Beihang University Beijing China

Data-Intensive computing emerges as the fourth paradigm for modern scientific discoveries. MapReduce, a programming paradigm for large-scale data-parallel applications, is widely applied to web indexing, machine learning, and scientific simulations in industries as well as in academia. Recently, the virtualized "utility computing" environments, such as campus cloud, are becoming an important scenario to run MapReduce jobs. For a MapReduce job, the straggler tasks may dominate the response time and delay whole job. Various speculation schemes have been proposed to alleviate such problem, however, most of them implicitly assume that the time cost for data movement on launching speculative map tasks is trivial, which does not always hold for the virtualized Hadoop clusters in a campus cloud. In this paper, we propose a novel approach, CREST(Combination Re-Execution Scheduling Technology), which can achieve the optimal running time for speculative map tasks and decrease the response time of MapReduce jobs. the main idea is that re-executing a combination of tasks on a group of computing nodes may progress faster than directly speculating the straggler task on target node, due to data locality. the evaluation validates our approach and demonstrates that CREST can reduce the running time of a speculative map task by 70% with best cases and 50% on average, comparing with LATE.

关键词： Time factors Bandwidth Virtual machining Educational institutions Computational modeling distributed databases Programming

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Dynamic Resource Allocation via distributed Decisions in Cloud Environment

Dynamic Resource Allocation via Distributed Decisions in Clo...

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IEEE international conference on e-Business Engineering (ICEBE)

作者： Trieu C. Chieu Hoi Chan IBM Thomas J. Watson Research Center Hawthorne NY USA

the adaptation of virtualization technologies and the Cloud Compute model by Web service providers is accelerating. these technologies commonly known as Cloud Compute Model are built upon an efficient and reliable dynamic resource allocation system. Maintaining sufficient resources to meet peak workloads while minimizing cost determines to a large extend the profitability of a Cloud service provider. Traditional centralized approach of resource provisioning with global optimization and statistical strategies can be complex, difficult to scale, computational intensive and often non-traceable which adds to the cost and efficiency of Cloud operation, especially in industrial environments. As we have learned in real life, the most efficient economic system is the one that provides individuals with incentives for their own decisions. It is also true for computing systems. In this paper, we present an architecture for dynamic resource provisioning via distributed decisions. We will illustrate our approach with a Cloud based scenario, in which each physical resource makes its own utilization decision based on its own current system and workload characteristics, and a light-weight provisioning optimizer with a replaceable routing algorithm for resource provisioning and scaling. this approach enables resource provisioning system to be more scalable, reliable, traceable, and simple to manage. In an industrial setting, the importance of these characteristics often exceeds the goal of squeezing the absolute last CPU cycles of the underlying physical resources.

关键词： Resource management Indexes distributed decision making Cloud computing Computational modeling Computer architecture

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