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15th IEEE International Conference on eScience, eScience 2019

作者： Begy, Volodimir Hermans, Joeri Barisits, Martin Lassnig, Mario Schikuta, Erich CERN Geneva Switzerland University of Liège Liège Belgium Faculty of Computer Science University of Vienna Vienna Austria

ISBN: (纸本)9781728124513

the data access patterns of applications running in computing grids are changing due to the recent proliferation of high-speed local and wide area networks. the data-intensive jobs are no longer strictly required to run at the computing sites, where the respective input data are located. Instead, jobs may access the data employing arbitrary combinations of data-placement, stage-in and remote data access. these data access profiles exhibit partially non-overlapping throughput bottlenecks. this fact can be exploited in order to minimize the time jobs spend waiting for input data. In this work we present a novel grid computing simulator, which puts a heavy emphasis on the various data access profiles. Its purpose is to enable reproducible performance studies on data access patterns. the fundamental assumptions underlying our simulator are justified by empirical experiments performed in the Worldwide LHC computing Grid (WLCG) at CERN. We demonstrate how to calibrate the simulator parameters in accordance with the true system using posterior inference with likelihood-free Markov Chain Monte Carlo. thereafter, we validate the simulator's output with respect to authentic production workloads from WLCG, demonstrating its remarkable accuracy. © 2019 IEEE.

关键词： Simulators

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面向数据处理与管理的云平台系统架构设计

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计算机技术与发展 2022年第7期32卷 122-127页

作者：汪朋姜红玉封雷中国电子科技集团公司第十五研究所北京100083

随着云原生技术在云计算领域的广泛应用,应用云原生技术更好地支撑业务能力成为了开发者们关注的重点。大数据环境下,基于海量数据和信息,针对数据处理与信息管理业务,对云平台系统进行了研究工作,梳理了系统的功能组成,部署形式,从体... 详细信息

随着云原生技术在云计算领域的广泛应用,应用云原生技术更好地支撑业务能力成为了开发者们关注的重点。大数据环境下,基于海量数据和信息,针对数据处理与信息管理业务,对云平台系统进行了研究工作,梳理了系统的功能组成,部署形式,从体系架构、功能结构、技术设计等方面设计了一种面向数据处理与信息管理的云平台,给出了框架、方法和技术,实现系统资源的统一管理、监控、共享和调度,实现应用的统一部署和高可用性。从整体角度分析设计了技术体系架构。通过应用容器云技术,该平台能够自动部署集群、快速扩容计算空间,用于应对大型数据处理面临的计算量巨大、优化困难等问题。该系统架构自动化程度更高、可用性更强,应用运行结果更具稳定性,能有效满足信息在存储、大规模计算、深度数据挖掘等方面的需求,从而达到通过云平台技术强力支撑数据处理与信息管理业务以及提升数据处理能力的目的。

关键词：云原生云平台容器技术云计算大数据

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SimdHT-Bench: Characterizing SIMD-Aware Hash Table Designs on Emerging CPU architectures∗ 15

SimdHT-Bench: Characterizing SIMD-Aware Hash Table Designs o...

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15th IEEE International symposium on Workload Characterization, IISWC 2019

作者： Shankar, DIpti Lu, Xiaoyi Panda, Dhabaleswar K. D. K. Ohio State University Department of Computer Science and Engineering United States

ISBN: (纸本)9781728140452

With the emergence of modern multi-core CPU architectures that support data parallelism via vectorization, several storage systems have been employing SIMD-based techniques to optimize data-parallel operations on in-memory structures like hash-tables. In this paper, we perform an in-depth characterization of the opportunities for incorporating AVX vectorization-based SIMD-aware designs for hash table lookups on emerging CPU architectures. We analyze the challenges and design dimensions involved in exploiting vectorization-based parallel key searching over cache-optimized non-SIMD hash tables. Based on this, we design a comprehensive micro-benchmark suite, SimdHT-Bench, that enables evaluating the performance and applicability of CPU SIMD-aware hash table designs for accelerating different read-intensive workloads. With SimdHT-Bench, we study five different use-case scenarios with varied workload patterns, on the latest Intel Skylake and Intel Cascade Lake multi-core CPU nodes. Further, to validate the applicability of SimdHT-Bench, we employ these performance studies to design a high-performance SIMD-aware RDMA-based in-memory key-value store to accelerate the Memcached 'Multi-Get' workload. We demonstrate that the SIMD-integrated designs can achieve up to 1.45x-2.04x improvement in server-side Get throughput and up to 34% improvement in end-to-end Multi-Get latencies over the state-of-the-art CPU-optimized non-SIMD MemC3 hash table design, on a high-performance compute cluster with Intel Skylake processors and InfiniBand EDR interconnects. © 2019 IEEE.

关键词： Table lookup

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Secure and flexible algorithm for outsourcing of bilinear pairings effectively resisting conspiracy

Secure and flexible algorithm for outsourcing of bilinear pa...

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Asia Joint Conference on Information Security (Asia JCIS)

作者： Chih-Hung Wang Guo-Cyuan Mao Department of Computer Science and Information Engineering National Chiayi University Chia-Yi Taiwan

ISBN: (数字)9781728199221

ISBN: (纸本)9781728199238

With the development of Internet of things (IoT) and 5G network, IoT devices become more and more important to our life. they contain smart speaker, web camera and smart home products. No doubt they bring a lot of convenience for our daily life and work affairs, but will cause some secure problems. the resource constrained IoT devices may not have enough computational ability and energy to run complicated cryptosystem like bilinear pairing. Currently, most of the architecture of distribution strategy for outsourcing bilinear pairing are fixed. the number of servers will be one or two without conspiracy. Nevertheless, in 5G and IoT environment, they support a large amount of assistant nodes to speed up calculation and data processing, such as edge computing. In this paper, we propose a flexible and secure architecture for outsourcing bilinear pairings with few assistant nodes which can be semi-trusted or untrusted. We can adjust our architecture to meet the different requirements according to the environment. Even if the most servers conspire each other, our proposed scheme also can detect the flaw of cheating with high probability.

关键词： Outsourcing computer architecture Servers Internet of things Cryptography 5G mobile communication

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A Low-Complexity Algorithm and Its Low-Power and high-throughput architecture for 3D-HEVC DMM-1 Encoding Tool

A Low-Complexity Algorithm and Its Low-Power and High-Throug...

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IEEE Latin American symposium on Circuits and Systems (LASCAS)

作者： Vinicius Borges Murilo Perleberg Vladimir Afonso Marcelo Porto Luciano Agostini Video Technology Research Group (ViTech) Federal University of Pelotas (UFPEL) Pelotas RS Brazil Postgraduate Program in Computing (PPGC) Video Technology Research Group (ViTech) Federal University of Pelotas (UFPEL) Pelotas RS Brazil Sul-rio-grandense Federal Institute (IFSul) Pelotas RS Brazil

ISBN: (数字)9781728134277

ISBN: (纸本)9781728134284

this paper presents a low-power and high-performance hardware design for the Depth Modeling Mode 1 (DMM-1) of the 3D-high Efficiency Video Coding (3D-HEVC). the designed architecture is based on a low-complexity algorithm developed to reduce the DMM-1 computational effort and to avoid the use of memory. the architecture was described in VHDL, and the ASIC synthesis was performed for the TSMC 40nm technology. the synthesis results showed a gate count of 1,283k gates and a power dissipation of 51.36 mW, when running at 82 MHz. the designed architecture is capable of processing 3D 1080p videos with up to 11 views at 30 frames per second. the reached results surpass the published works in terms of throughput and power dissipation.

关键词： Encoding computer architecture Hardware Calculators Tools Video coding three-dimensional displays

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Study on Replica Strategy of Big Data Storage based on Cloud Environment

Study on Replica Strategy of Big Data Storage based on Cloud...

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International Conference on computer Science & Education (ICCSE)

作者： Jun Qin Shengxi Liang Yanyan Song Ping Zong Communication University of China Nanjing Nanjing University of Posts and Telecommunications Nanjing China Nanjing University of Posts and Telecommunications Nanjing China Communication University of China Nanjing Nanjing China Nanjing University of Science and Technology Zijin College Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

the replica technology in cloud storage can not only maintain the high availability of the system, but also improve the overall performance of the system. this paper analyzes the limitations of the existing replica placement strategy in heterogeneous environment, and proposes a reasonable replica placement improvement strategy based on the comprehensive performance evaluation value of the node. the experimental results show that the improved replica placement strategy can make the replica distribution more reasonable and balanced on the premise of ensuring the overall availability of the system.

关键词： Cloud computing Writing performance evaluation Telecommunications Task analysis Tools Indexes

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Evaluation of pilot jobs for apache spark applications on hpc clusters 15

Evaluation of pilot jobs for apache spark applications on hp...

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15th IEEE International Conference on eScience, eScience 2019

作者： Hayot-Sasson, Valerie Glatard, Tristan Department of Computer Science and Software Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9781728124513

Big Data has become prominent throughout many scientific fields, and as a result, scientific communities have sought out Big Data frameworks to accelerate the processing of their increasingly data-intensive pipelines. However, while scientific communities typically rely on high-performance computing (HPC) clusters for the parallelization of their pipelines, many popular Big Data frameworks such as Hadoop and Apache Spark were primarily designed to be executed on dedicated commodity infrastructures. this paper evaluates the benefits of pilot jobs over traditional batch submission for Apache Spark on HPC clusters. Surprisingly, our results show that the speed-up provided by pilot jobs over batch scheduling is moderate to non-existent (0.98 on average) despite the presence of long queuing times. In addition, pilot jobs provide an extra layer of scheduling that complicates debugging and deployment. We conclude that traditional batch scheduling should remain the default strategy to deploy Apache Spark applications on HPC clusters. © 2019 IEEE.

关键词： Big data

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Proceedings - 29th International symposium on computer architecture and high performance computing, SBAC-PAD 2017

Proceedings - 29th International Symposium on Computer Archi...

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29th International symposium on computer architecture and high performance computing, SBAC-PAD 2017

ISBN: (纸本)9781509012336

the proceedings contain 23 papers. the topics discussed include: extending OmpSs for OpenCL kernel co-execution in heterogeneous systems;data coherence analysis and optimization for heterogeneous computing;exploring heterogeneous mobile architectures with a high-level programming model;scalability of CPU and GPU solutions of the prime elliptic curve discrete logarithm problem;overcoming memory-capacity constraints in the use of ILUPACK on graphics processors;exploiting data compression to mitigate aging in GPU register files;SEDEA: a sensible approach to account DRAM energy in multicore systems;a user-level scheduling framework for BoT applications on private clouds;GC-CR: a decentralized garbage collector component for checkpointing in clouds;towards a deterministic fine-grained task ordering using multi-versioned memory;FGSCM: a fine-grained approach to transactional lock elision;a machine learning approach for performance prediction and scheduling on heterogeneous CPUs;object placement for high bandwidth memory augmented with high capacity memory;accelerating graph analytics on CPU-FPGA heterogeneous platform;online multimedia similarity search with response time-aware parallelism and task granularity auto-tuning;a publish/subscribe system using causal broadcast over dynamically built spanning trees;global snapshot of a distributed system running on virtual machines;and resource-management study in HPC runtime-stacking context.

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Parallel high Average-Utility Itemset Mining Using Better Search Space Division Approach 15th

Parallel High Average-Utility Itemset Mining Using Better Se...

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15th International Conference on Distributed computing and Internet Technology (ICDCIT)

作者： Sethi, Krishan Kumar Ramesh, Dharavath Sreenu, M. Indian Inst Technol ISM Dhanbad Dhanbad 826004 Jharkhand India Ashoka Inst Engn & Technol Hyderabad 500082 Telangana India

ISBN: (纸本)9783030053666;9783030053659

Since the last decade, high Utility Itemset (HUI) mining has emerged as a popular pattern mining approach. HUI mining discovers a set of itemset with their profit more than a user defined profit threshold. high Average-Utility Itemset (HAUI) mining is an improvement over HUI mining that involves the length of items to refine the patterns and keep a fair mining process. In the era of big data, traditional HAUI mining algorithms are not suitable to process large transaction dataset on standalone system due to limitation of processing resources. therefore, several distributed frameworks have been developed to process big data on cluster of commodity hardwares. this paper presents a parallel version of the traditional HAUI-Miner algorithm and names it as Parallel high-Average Utility Itemset Miner (PHAUIM). PHAUIM is a Spark-based distributed algorithm which splits the dataset into multiple chunks and distributes on cluster nodes to process each data chunk in parallel. In addition, an improved approach for search space division is developed. Proposed search space division technique fairly assigns the workload to each node and upgrades the performance. Comprehensive experiments have been performed to measure the performance of PHAUIM in terms of speedup and data scalability. PHAUIM is also compared with traditional HAUIM.

关键词： high average-utility itemset mining Big data Apache-Spark Search space

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Learning Run-Time Compositions of Interacting Adaptations

Learning Run-Time Compositions of Interacting Adaptations

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SEAMS International Workshop on Software Engineering for Adaptive and Self-Managing Systems, ICSE

作者： Nicolás Cardozo Ivana Dusparic Systems and Computing Engineering Department Universidad de los Andes Colombia School of Computer Science and Statistics Trinity College Dublin Ireland

Self-adaptive systems continuously adapt to internal and external changes in their execution environment. In context-based self-adaptation, adaptations take place in response to the characteristics of the execution environment, captured as a context. However, in large-scale adaptive systems operating in dynamic environments, multiple contexts are often active at the same time, requiring simultaneous execution of multiple adaptations. Complex interactions between such adaptations might not have been foreseen or accounted for at design time. For example, adaptations can partially overlap, requiring only partial execution of each, or they can be conflicting, requiring some of the adaptations not to be executed at all, in order to preserve system execution. To ensure a correct composition of adaptations, we propose ComInA, a novel reinforcement learning based approach, which autonomously learns interactions between adaptations as well as the most appropriate adaptation composition for each combination of active contexts, as they arise. We present an initial evaluation of ComInA in an urban public transport network simulation, where multiple adaptations to buses, routes, and stations are required. Early results show that ComInA correctly identifies whether adaptations are compatible or conflicting and learns to execute adaptations which maximize system performance. However, further investigation is needed into how best to utilize such identified relationships to optimize a wider range of metrics and utilize more complex composition strategies.

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