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An XML Data Placement Strategy for distributed XML Storage and parallel Query

An XML Data Placement Strategy for Distributed XML Storage a...

引用

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

作者： Jing Zhang Bo Lang Yawei Duan State Key Laboratory of Software Development Environment Beihang University Beijing China

Since there has been significant amount of XML documents generated in various application domains, efficient XML management has become an important problem. distributed XML storage and parallel query based on Map Reduce can be an effective solution to this problem. As XML data placement strategy is a key factor of parallel system performance, in this paper we present an XML placement strategy, which is Query Workload Estimation based XML Placement strategy (QWEXP) for efficient distributed XML storage and parallel query. To achieve query workload balance, it partitions XML based on query workload estimation which is calculated by XML structure without knowing of user queries, considering that in common application scenarios user queries are unknown in advance. the partitioned XML segments are around an XML storage unit W0, to support scalability of parallel XML database. Finally segments are distributed to each processing node evenly to ensure workload balance on parallel query execution. Experimental results have shown that QWEXP promotes the speedup and scale up properties of parallel XML system greatly.

关键词： XML Estimation Partitioning algorithms distributed databases Scalability Benchmark testing

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P2G: A framework for distributed real-time processing of multimedia data

P2G: A framework for distributed real-time processing of mul...

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2011 international conference on parallel Processing Workshops, ICPPW 2011

作者： Espeland, Håvard Beskow, Paul B. Stensland, Håkon K. Olsen, Preben N. Kristoffersen, Ståle Griwodz, Carsten Halvorsen, Pål Department of Informatics University of Oslo Norway Simula Research Laboratory Norway

ISBN: (纸本)9780769545110

the computational demands of multimedia data processing are steadily increasing as consumers call for progressively more complex and intelligent multimedia services. New multi-core hardware architectures provide the required resources, but writing parallel, distributed applications remains a labor-intensive task compared to their sequential counter-part. For this reason, Google and Microsoft implemented their respective processing frameworks MapReduce [10] and Dryad [19], as they allow the developer to think sequentially, yet benefit from parallel and distributed execution. An inherent limitation in the design of these batch processing frameworks is their inability to express arbitrarily complex workloads. the dependency graphs of the frameworks are often limited to directed acyclic graphs, or even pre-determined stages. this is particularly problematic for video encoding and other algorithms that depend on iterative execution. With the Nornir runtime system for parallel programs [39], which is a Kahn Process Network implementation, we addressed and solved several of these limitations. However, it is more difficult to use than other frameworks due to its complex programming model. In this paper, we build on the knowledge gained from Nornir and present a new framework, called P2G, designed specifically for developing and processing distributed real-time multimedia data. P2G supports arbitrarily complex dependency graphs with cycles, branches and deadlines, and provides both data- and task-parallelism. the framework is implemented to scale transparently with available (heterogeneous) resources, a concept familiar from the cloud computing paradigm. We have implemented an (interchangeable) P2G kernel language to ease development. In this paper, we present a proof of concept implementation of a P2G execution node and some experimental examples using complex workloads like Motion JPEG and Kmeans clustering. the results show that the P2G system is a feasible approach to mu

关键词： MapReduce

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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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Dynamically Accessing Packet Header Fields at High-Speed

Dynamically Accessing Packet Header Fields at High-Speed

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

作者： Ralph Duncan Peder Jungck Kenneth Ross CloudShield Technologies SAIC company Sunnyvale CA USA

A significant part of packet processing consists of detecting whether certain standard protocol headers are present, where they are located and whether they include optional information. Packet processing programs are on tight time budgets, especially to handle speeds in the gigabits per second (gbps) range. thus, high-speed mechanisms for finding and accessing headers are critical. Our approach lets users define headers as C-style structures in a high-level language, packetC [1], and specify header locations in terms of offsets from the start of the current packet, which is treated as an array of unsigned bytes. these offsets can be expressed in terms of network layer offsets, constant values, runtime-calculated variables and combinations of all of these. this paper focuses on the principal forms these offset expressions can take and on how our FPGAs (Field Programmable Gate Arrays), compiler and interpreter collectively handle them at runtime. For simple and complex header offset scenarios we provide users with intuitive, high-level ways to describe offsets and provide effective runtime mechanisms to access header fields.

关键词： Protocols Runtime Payloads parallel processing Field programmable gate arrays Arrays Registers

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Dacoop: Accelerating Data-Iterative applications on Map/Reduce Cluster

Dacoop: Accelerating Data-Iterative Applications on Map/Redu...

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

作者： Yi Liang Guangrui Li Lei Wang Yanpeng Hu Department of Computer Science Beijing University of Technology Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Hwellzen Software Center Shanghai China

Map/reduce is a popular parallel processing framework for massive-scale data-intensive computing. the data-iterative application is composed of a serials of map/reduce jobs and need to repeatedly process some data files among these jobs. the existing implementation of map/reduce framework focus on perform data processing in a single pass with one map/reduce job and do not directly support the data-iterative applications, particularly in term of the explicit specification of the repeatedly processed data among jobs. In this paper, we propose an extended version of Hadoop map/reduce framework called Dacoop. Dacoop extends Map/Reduce programming interface to specify the repeatedly processed data, introduces the shared memory-based data cache mechanism to cache the data since its first access, and adopts the caching-aware task scheduling so that the cached data can be shared among the map/reduce jobs of data-iterative applications. We evaluate Dacoop on two typical data-iterative applications: k-means clustering and the domain rule reasoning in sementic web, with real and synthetic datasets. Experimental results show that the data-iterative applications can gain better performance on Dacoop than that on Hadoop. the turnaround time of a data-iterative application can be reduced by the maximum of 15.1%.

关键词： Processor scheduling Data models Programming Scheduling distributed databases Computer architecture Load modeling

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Trusted Block as a Service: Towards Sensitive applications on the Cloud

Trusted Block as a Service: Towards Sensitive Applications o...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Jianan Hao Wentong Cai Parallel and Distributed Computing Centre School of Computer Engineering Nanyang Technological University Singapore

Cloud computing grows rapidly as today's advanced information technology. However, by allowing outsourcing computation on the Cloud, users risk of disclosing privacy and obtaining forged results. these potential threats block sensitive applications to join the Cloud. In this paper, we characterize sensitive applications on the Cloud (SAND) problem and define two critical security requirements: confidentiality and verifiability. the former refers to the protection of sensitive programs/data from disclosing to other users or even the Cloud administrators. the latter concerns with user's capability to verify whether computing results are faithfully calculated. To address SAND, we propose a new Cloud model, Trusted Block as a Service (TBaaS), to provide a confidential and verifiable environment for each sensitive application. TBaaS limits Cloud provider's access of sensitive applications while granting user the ability to verify whether the computation is faithfully carried out. Moreover, it offers high flexibility and low performance overhead.

关键词： Virtual machine monitors Encryption Protocols Hardware Software

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A distributed Task Migration Scheme for Mesh-Based Chip-Multiprocessors

A Distributed Task Migration Scheme for Mesh-Based Chip-Mult...

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

作者： Hossein Yaghoubi Mehdi Modarresi Hamid Sarbazi-Azad Department of Computer Engineering Sharif University of Technology Tehran Iran School of Computer Science IPM Tehran Iran Department of Electrical and Computer Engineering University of Tehran Tehran Iran

A task migration scheme for homogeneous chip multiprocessors (CMP) is presented in this paper. the proposed migration mechanism focuses on the communication sub-system and aims to reduce the total power consumption and latency of the network-on-chip (NoC). In this work, starting from an initial mapping, the tasks migrate to new cores in such a way that the distance between the end-point nodes of high-volume communication flows is reduced. Finding the new place for a task is done in a distributed manner by applying an iterative local search that relies on the local information of each task about its communication demand. the task migration procedure also includes a pre-migration step that aims to produce a high quality (i.e. closer to the optimum point) starting point for the main distributed algorithm. the experimental results under some synthetic and realistic CMP workloads show that this method can effectively adapt the mapping of the tasks to the on-chip communication pattern and improve the power consumption and performance of the on-chip networks.

关键词： Program processors Benchmark testing Partitioning algorithms Algorithm design and analysis Power demand System-on-a-chip Heuristic algorithms

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Application of Ontologies in Peer-to-Peer Database with Routing Queries Based on Ant Colony Algorithm

Application of Ontologies in Peer-to-Peer Database with Rout...

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

作者： Carlos Roberto Valêncio Leandro Rincon Costa Carlos Henrique El Hetti Laurenti Adriano Mauro Cansian Depto. de Ciências de Computação e Estatística Universidade Estadual Paulista-Unesp São José do Rio Preto Brazil

A significant set of information stored in different databases around the world, can be shared through peer-to-peer databases. With that, is obtained a large base of knowledge, without the need for large investments because they are used existing databases, as well as the infrastructure in place. However, the structural characteristics of peer-to-peer, makes complex the process of finding such information. On the other side, these databases are often heterogeneous in their schemas, but semantically similar in their content. A good peer-to-peer databases systems should allow the user access information from databases scattered across the network and receive only the information really relate to your topic of interest. this paper proposes to use ontologies in peer-to-peer database queries to represent the semantics inherent to the data. the main contribution of this work is enable integration between heterogeneous databases, improve the performance of such queries and use the algorithm of optimization Ant Colony to solve the problem of locating information on peer-to-peer networks, which presents an improve of 18% in results.

关键词： Ontologies Peer to peer computing Semantics Routing distributed databases Cancer

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

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

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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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