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International Conference on Information Technology: Research and Education (ITRE)

作者： Kuan-Ching Li Chao-Tung Yang Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung Taiwan High Performance Computing Laboratory Department of Computer Science and Infnmation Engineering Tunghai University Taichung Taiwan

ISBN: (纸本)0780389328

The computing power provided by high performance and low cost PC-based clusters and grid computing platforms are attractive and they are equal or superior to supercomputers and mainframes. In parallel, discussions on how to obtain more computing power from these computing platforms become an interesting issue. The development of applications for these high-performance computing platforms is complicated for several reasons: the complexity of applications themselves, which combines aspects of super computing and distributed computing, and by the need to achieve higher performance. This paper describes the design rationale and implementation of a parallel programming Web-based toolkit, to ease the parallel programming learning process, with the use of Web-based interface. The toolkit has widely been used in MPI parallel programming courses (both in graduate and undergraduate levels) and industry trainings.

关键词： Parallel programming Concurrent computing Application software distributed computing Grid computing high performance computing Visualization Computer science Supercomputers Industrial training

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Blockchain oracle design patterns

arXiv

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arXiv 2021年

作者： Pasdar, Amirmohammad Dong, Zhongli Lee, Young Choon Aglive Lab 8 Hadenfeld Ave Macquarie ParkNSW2113 Australia Department of Computing Macquarie University SydneyNSW2109 Australia Center for Distributed and High Performance Computing School of Computer Science The University of Sydney NSW2006 Australia

Blockchain is a form of distributed ledger technology (DLT) where data is shared among users connected over the internet. Transactions are data state changes on the blockchain that are permanently recorded in a secure and transparent way without the need of a third party. Besides, the introduction of smart contracts to the blockchain has added programmability to the blockchain and revolutionized the software ecosystem leading toward decentralized applications (DApps) attracting businesses and organizations to employ this technology. Although promising, blockchains and smart contracts have no access to the external systems (i.e., off-chain) where real-world data and events resides;consequently, the usability of smart contracts in terms of performance and programmability would be limited to the on-chain data. Hence, blockchain oracles are introduced to mitigate the issue and are defined as trusted third-party services that send and verify the external information (i.e., feedback) and submit it to smart contracts for triggering state changes in the blockchain. In this paper, we will study and analyze blockchain oracles with regard to how they provide feedback to the blockchain and smart contracts. We classify the blockchain oracle techniques into two major groups such as voting-based strategies and reputation-based ones. The former mainly relies on participants' stakes for outcome finalization while the latter considers reputation in conjunction with authenticity proof mechanisms for data correctness and integrity. We then provide a structured description of patterns in detail for each classification and discuss research directions in the end. © 2021, CC BY.

关键词： Smart contract

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The anatomy of a course in cluster and grid computing

The anatomy of a course in cluster and grid computing

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International Conference on Information Technology: Research and Education (ITRE)

作者： Chao-Tung Yang Kuan-Ching Li High Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung Taiwan

ISBN: (纸本)0780389328

Cluster and grid computing is a relatively new interdisciplinary field, where computer science, engineering and computational biology are its core supporting disciplines. The rise of cluster and grid computing discipline brings to computer science faculty members new opportunities and challenges, both in education and in research. In this paper, we explore issues in teaching cluster and grid computing, the developing of curricula, and thoughts to foster student to work on research projects in cluster and grid computing.

关键词： Anatomy Grid computing distributed computing Concurrent computing Computer networks Brain modeling Predictive models Computer science Education Space technology

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Decision tree construction for data mining on grid computing environments

Decision tree construction for data mining on grid computing...

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Chao-Tung Yang Shu-Tzu Tsai Kuan-Ching Li High-Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung Taiwan

ISBN: (纸本)0769522491

In this paper, the authors presented the grid-based decision tree architecture, with the intention of applying it to both parallel and sequential algorithms. Also, it is shown that, based on the scope and model of data mining applied in the grid environment as well as user equivalent perspective, grid roles can be categorized into three types. It was aimed, through these definitions, to help software developers define clear system processes and differentiate the application scope for software applications. To fulfill the architecture, an existing parallel decision tree algorithm was first applied (the SPRINT algorithm) to the grid environment. The performance and differences in many other areas are compared using datasets of different sizes. The experimental results will be used for future reference and further development.

关键词： Decision trees Data mining Grid computing Computer architecture Clustering algorithms Concurrent computing distributed computing Computer science Application software Chaos

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Real-Time Stream Data Processing at Scale

Real-Time Stream Data Processing at Scale

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IEEE International Conference on Parallel and distributed computing, Applications and Technologies (PDCAT)

作者： M. Reza HoseinyFarahabady Ali Jannesari Wei Bao Zahir Tari Albert Y. Zomaya Center for Distributed & High Performance Computing School of Computer Science The University of Sydney NSW Australia Department of Computer Science Iowa State University IA USA School of Science RMIT University Melbourne VIC Australia

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

A typical scenario in a stream data-flow processing engine is that users submit continues queries in order to receive the computational result once a new stream of data arrives. The focus of the paper is to design a dynamic CPU cap controller for stream data-flow applications with real-time constraints, in which the result of computations must be available within a short time period, specified by the user, once a recent update in the input data occurs. It is common that the stream data-flow processing engine is deployed over a cluster of dedicated or virtualized server nodes, e.g., Cloud or Edge platform, to achieve a faster data processing. However, the attributes of incoming stream data-flow might fluctuate in an irregular way. To effectively cope with such unpredictable conditions, the underlying resource manager needs to be equipped with a dynamic resource provisioning mechanism to ensure the real-time requirements of different applications. The proposed solution uses control theory principals to achieve a good utilization of computing resources and a reduced average response time. The proposed algorithm dynamically adjusts the required quality of service (QoS) in an environment when multiple stream & data-flow processing applications concurrently run with unknown and volatile workloads. Our study confirms that such a unpredictable demand can negatively degrade the system performance, mainly due to adverse interference in the utilization of shared resources. Unlike prior research studies which assumes a static or zero correlation among the performance variability among consolidated applications, we presume the prevalence of shared-resource interference among collocated applications as a key performance-limiting parameter and confront it in scenarios where several applications have different QoS requirements with unpredictable workload demands. We design a low-overhead controller to achieve two natural optimization objectives of minimizing QoS violation amount an

关键词： Resource management Quality of service Data processing Microsoft Windows Adaptation models Computational modeling Real-time systems

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An Efficient Label Routing on high-Radix Interconnection Networks

An Efficient Label Routing on High-Radix Interconnection Net...

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International Conference on Parallel and distributed Systems (ICPADS)

作者： Fei Lei Dezun Dong Xiangke Liao College of Computer National University of Defense Technology China National Laboratory for Parallel and Distributed Processing National University of Defense Technology China Collaborative Innovation Center of High Performance Computing National University of Defense Technology China

ISBN: (纸本)9781538632086

Cost-effective adaptive routing has a significant impact on overall performance for high-radix hierarchical topologies, such as Dragonfly, which achieve a lower network diameter than traditional topologies, Torus and Fat tree, but exhibit a lower degree of adaptiveness for shortest-path rout- ing. Existing adaptive routing methods for those hierarchical topologies improve the adaptiveness by increasing path length, i.e. local or global adaptive routing, and thus suffer from complex and costly deadlock avoidance. This work aims to maximize the routing adaptiveness at the minimum cost of deadlock avoidance. We propose a label routing method for high-radix hierarchical networks. This label routing utilizes a co-design methodology and coordinates the two pipelines, input queue and routing computation, in the router microarchitec- ture. Packets in the input buffer are labeled by our routing algorithm depending on network states. We reorganize the input buffer and develop a label routing algorithm, named Green-Red Routing, GRR. GRR relaxes the requirement of using virtual channels to eliminate routing deadlock, and mitigates buffer resources dedicated to deadlock avoidance. GRR manages the buffer resources and balance its utilization elaborately, and achieve fully adaptive routing efficiently. We conduct extensive experiments to evaluate the performance of GRR on Dragonfly and compare it with state-of-the-art works. The results show that GRR achieves 10%-35% higher performance than existing routing algorithms under most traffic patterns.

关键词： Routing System recovery Network topology Topology Resource management Adaptive systems Organizations

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Dynamic Control of CPU Cap Allocations in Stream Processing and Data-Flow Platforms

Dynamic Control of CPU Cap Allocations in Stream Processing ...

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IEEE International Symposium on Network computing and Applications

作者： M. Reza HoseinyFarahabady Ali Jannesari Zahir Tari Javid Taheri Albert Y. Zomaya The University of Sydney Center for Distributed & High Performance Computing School of Computer Science New South Wales Australia Iowa State University IA USA RMIT University School of Science Melbourne VIC Australia Karlstad University Sweden

This paper focuses on Timely dataflow programming model for processing streams of data. We propose a technique to define CPU resource allocation (i.e., CPU capping) with the goal to improve response time latency in such type of applications with different quality of service (QoS) level, as they are concurrently running in a shared multi-core computing system with unknown and volatile demand. The proposed solution predicts the expected performance of the underlying platform using an online approach based on queuing theory and adjusts the corrections required in CPU allocation to achieve the most optimized performance. The experimental results confirms that measured performance of the proposed model is highly accurate while it takes into account the percentiles on the QoS metrics. The theoretical model used for elastic allocation of CPU share in the target platform takes advantage of design principals in model predictive control theory and dynamic programming to solve an optimization problem. While the prediction module in the proposed algorithm tries to predict the temporal changes in the arrival rate of each data flow, the optimization module uses a system model to estimate the interference among collocated applications by continuously monitoring the available CPU utilization in individual nodes along with the number of outstanding messages in every intermediate buffer of all TDF applications. The optimization module eventually performs a cost-benefit analysis to mitigate the total amount of QoS violation incidents by assigning the limited CPU shares among collocated applications. The proposed algorithm is robust (i.e., its worst-case output is guaranteed for arbitrarily volatile incoming demand coming from different data streams), and if the demand volatility is not large, the output is optimal, too. Its implementation is done using the TDF framework in Rust for distributed and shared memory architectures. The experimental results show that the proposed algorithm r

关键词： Quality of service Resource management Computational modeling Instruction sets Data models Programming Process control

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On implementation of a scalable wallet-size cluster computing system for multimedia applications

On implementation of a scalable wallet-size cluster computin...

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5th Pacific Rim Conference on Multimedia, PCM 2004

作者： Lee, Liang-Teh Li, Kuan-Ching Yang, Chao-Tung Tseng, Chia-Ying Liu, Kang-Yuan Hung, Chih-Hung Dept. of Computer Science and Engineering Tatung University Taipei104 Taiwan Parallel and Distributed Processing Center Dept. of Computer Science and Information Management Providence University Taichung433 Taiwan High Performance Computing Laboratory Dept. of Computer Science and Information Engineering Tunghai University Taichung407 Taiwan Networks and Multimedia Institute Institute for Information Industry Taipei106 Taiwan

ISBN: (纸本)9783540239857

In order to obtain a low-cost, low-power consumption and small sized digital home processing center, especially for multimedia applications, we propose a scalable wallet-size cluster computing system in this paper. Such system uses low-cost, low-power consumption and small sized microprocessors as computing nodes to form a cluster system. The main reason why we are interested to investigate and build such system is to increase the computing power and to accelerate the H.264 video processing. We analyzed the H.264 decoder scheme and distributed the most heavy-load computations in the decoder scheme to each computing node (or microprocessor) of the cluster system for the processing of signals, in parallel. To run multimedia applications in this cluster system, an efficient real-time video processing is implemented. © Springer-Verlag Berlin Heidelberg 2004.

关键词： computing power

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Q-Flink: A QoS-Aware Controller for Apache Flink

Q-Flink: A QoS-Aware Controller for Apache Flink

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IEEE/ACM International Symposium on Cluster computing and the Grid (CCGRID)

作者： M. Reza HoseinyFarahabady Ali Jannesari Javid Taheri Wei Bao Albert Y. Zomaya Zahir Tari School of Computer Science Center for Distributed and High Performance Computing The University of Sydney Department of Computer Science Iowa State University IA USA Department of Mathematics and Computer Science Karlstad University Sweden School of Science RMIT University Australia

ISBN: (数字)9781728160955

Modern stream-data processing platforms are required to execute processing pipelines over high-volume, yet high-velocity, datasets under tight latency constraints. Apache Flink has emerged as an important new technology of large-scale platform that can distribute processing over a large number of computing nodes in a cluster (i.e., scale-out processing). Flink allows application developers to design and execute queries over continuous raw-inputs to analyze a large amount of streaming data in a parallel and distributed fashion. To increase the throughput of computing resources in stream processing platforms, a service provider might be tempted to use a consolidation strategy to pack as many processing applications as possible on the working nodes, with the hope of increasing the total revenue by improving the overall resource utilization. However, there is a hidden trap for achieving such a higher throughput solely by relying on an interference-oblivious consolidation strategy. In practice, collocated applications in a shared platform can fiercely compete with each others for obtaining the capacity of shared resources (e.g., cache and memory bandwidth) which in turn can lead to a severe performance degradation for all consolidated *** paper addresses the shared resource contention problem associated with the auto-resource controlling mechanism of Apache Flink engine running across a distributed cluster. A controlling strategy is proposed to handle scenarios in which stream processing applications may have different quality of service (QoS) requirements while the resource interference is considered as the key performance-limiting parameter. The performance evaluation is carried out by comparing the proposed controller with the default Flink resource allocation strategy in a testbed cluster with total 32 Intel Xeon cores under different workload traffic with up to 4000 streaming applications chosen from various benchmarking tools. Experimental results demons

关键词： Quality of service Resource management Time factors Engines Data processing Interference Process control

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Semantically supported SLA negotiation

Semantically supported SLA negotiation

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2009 10th IEEE/ACM International Conference on Grid computing, Grid 2009

作者： Micsik, András Frutos, Henar Muñoz Kotsiopoulos, Ioannis Koller, Bastian Department of Distributed Systems MTA SZTAKI Lágymányosi utca 11 Budapest 1111 Hungary Telefónica Investigación y Desarrollo S.A C/ Emilio Vargas 6 28043 Madrid Spain School of Computer Science University of Manchester Kilburn Building Oxford Road Manchester M13 9PL United Kingdom High Performance Computing Center Stuttgart University of Stuttgart Nobelstrasse 19 70550 Stuttgart Germany

ISBN: (纸本)9781424451494

The evolution of services market raises the need for automatic support for negotiating service use criteria. In order to reach an agreement, the negotiating parties need to develop a common understanding of the Quality of Service (QoS) terms which are part of the Service Level Agreement (SLA). The use of semantic annotations together with reasoning can increase the level of flexibility and automation in SLA management. A framework is presented for SLA negotiation allowing the two parties to use their own terminologies. © 2009 IEEE.

关键词： Semantics

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