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14th International Conference on distributed Computing and Internet Technology (ICDCIT)

作者： Singh, Prakash Kumar Shanker, Udai MMM Univ Technol Dept Comp Sci & Engn Gorakhpur Uttar Pradesh India

ISBN: (纸本)9783319723440;9783319723433

Priority heuristic policies have been developed for centralized and distributed real time database systems where cohorts or sub transaction executed in sequential manner, however, these heuristics may not fit well for the mobile distributed real time database systems (MDRTDBS) where sub transactions are performing parallel execution and faces a lot of wireless challenges. In this paper, a MDRTDBS model has been introduced where sub-transaction executed parallel on different mobile sites and proposed a heuristic based on number of write locks. Proposed heuristic improves overall system performance by favoring sub transaction which demands lesser number of write locks. Further, a study has been done to evaluate impact of proposed heuristics with earliest deadline first and heuristic based on number of locks required using distributed high priority two phase locking protocol.

关键词： real time Transaction Priority heuristic Mobile database

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电网调控实时数据平台体系架构及关键技术研究与应用

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电力系统自动化 2019年第22期43卷 157-164页

作者：许洪强孙世明葛朝强屈刚孙云枫王兴志孙文彦国家电网有限公司国家电力调度控制中心北京市100031 南瑞集团有限公司(国网电力科学研究院有限公司) 江苏省南京市211106 国电南瑞科技股份有限公司江苏省南京市211106 智能电网保护和运行控制国家重点实验室江苏省南京市211106 国家电网有限公司华东分部上海市200120

一体化运行的互联大电网对现有调度自动化系统实时数据的全局性和实时性等提出了新的挑战,文中提出了电网调控实时数据平台体系架构和建设目标原则。在此基础上,研究了电网模型在线同步、分布式数据汇集与处理、大规模并行状态估计、实... 详细信息

一体化运行的互联大电网对现有调度自动化系统实时数据的全局性和实时性等提出了新的挑战,文中提出了电网调控实时数据平台体系架构和建设目标原则。在此基础上,研究了电网模型在线同步、分布式数据汇集与处理、大规模并行状态估计、实时数据统一服务等关键技术,实现了基于模型自动同步免维护下的全网实时数据单点采集、全网共享。研发的电网调控实时数据平台在国分调控中心试点建设,应用结果表明,所提技术方案提高了实时数据汇集的全局性、实时性和运维效率,能够为电网分析决策应用提供全面、实时、准确的全网实时数据和状态估计数据断面。

关键词：实时数据平台原始报文汇集电网模型在线同步分布式数据处理并行状态估计实时数据统一服务

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Research on distributed real time Data Space SCADA Cluster for Large Power Grid 5

Research on Distributed Real Time Data Space SCADA Cluster f...

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5th International Conference on systems and Informatics (ICSAI)

作者： Wang, Hao Jin, Fenlan Gong, Zeping Xia, Hui Chen, Wei Zhao, Lin NARI Grp Corp Beijing Beijing 100192 Peoples R China

ISBN: (纸本)9781728101200

In the field of industrial automation, the traditional master/slave real-lime data processing (SCADA) system has been unable to deal with the current massive data and diversified business demands in throughput, real-time and scalability. This paper presents a decentralized real-time data space, based decentralized management SCADA cluster solutions for the treatment of object partitioning, distributed real-time data processing, dynamic data migration and decentralized real-time data management are discussed. At last, the scheme is applied to develop the system an-d verify it.

关键词： distributed architecture data division parallel computing decentralized management

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Towards real-time Privacy Preservation: A Streaming Location Anonymous Method Based on distributed Framework

Towards Real-time Privacy Preservation: A Streaming Location...

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IEEE International Conference on Communications (ICC) / workshop on Integrating UAVs into 5G

作者： Hui, Tong Yang, Yahui Shen, Qingni Wu, Zhonghai Peking Univ Sch Software & Microelect Beijing Peoples R China

ISBN: (纸本)9781538631805

In order to better serve users, several location-based services rely on the real-time spatio-temporal information. Existing location privacy-preserving methods traverse the whole dataset to anonymize k locations together, and do not utilize parallel computing technology. The anonymization for big volume of location data may result in huge computing cost. We propose a new method called Never Wait for Long (NW4L), which protects the privacy of big-volume location data in parallel and real-time. Instead of linear structure, a k-d tree structure is adopted for the nearest location search to speed up computation. To further improve efficiency, locations are pre-classified in several groups, so that each group can be anonymized in parallel with support from the distributed stream computation framework. In this paper, we implemented NW4L based on Spark and used real-world dataset for performance evaluation. Experimental results show that 100,000 location samples can be processed in 2 minutes, which is feasible for real-time computation.

关键词： privacy-preserving location data real-time k-d tree distributed computation

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A cloud-agnostic queuing system to support the implementation of deadline-based application execution policies

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 101卷 99-111页

作者： Kiss, Tamas DesLauriers, James Gesmier, Gregoire Terstyanszky, Gabor Pierantoni, Gabriele Abu Oun, Osama Taylor, Simon J. E. Anagnostou, Anastasia Kovacs, Jozsef Univ Westminster Ctr Parallel Comp 115 New Cavemdish St London W1W 6UW England Univ Kent Sch Comp Cyber Secur Grp Canterbury Kent England Brunel Univ London Modelling & Simulat Grp Kingston Lane Uxbridge UB8 3PH Middx England MTA SZTAKI Budapest Hungary

There are many scientific and commercial applications that require the execution of a large number of independent jobs resulting in significant overall execution time. Therefore, such applications typically require distributed computing infrastructures and science gateways to run efficiently and to be easily accessible for end-users. Optimising the execution of such applications in a cloud computing environment by keeping resource utilisation at minimum but still completing the experiment by a set deadline has paramount importance. As container-based technologies are becoming more widespread, support for job-queuing and auto-scaling in such environments is becoming important. Current container management technologies, such as Docker Swarm or Kubernetes, while provide auto-scaling based on resource consumption, do not support job queuing and deadline-based execution policies directly. This paper presents JQueuer, a cloud-agnostic queuing system that supports the scheduling of a large number of jobs in containerised cloud environments. The paper also demonstrates how JQueuer, when integrated with a cloud application-level orchestrator and auto-scaling framework, called MiCADO, can be used to implement deadline-based execution policies. This novel technical solution provides an important step towards the cost-optimisation of batch processing and job submission applications. In order to test and prove the effectiveness of the solution, the paper presents experimental results when executing an agent-based simulation application using the open source REPAST simulation framework. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Cloud computing Container technologies Deadline-based auto-scaling JQueuer MiCADO Agent-based simulation

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Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges

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JOURNAL OF systems ARCHITECTURE 2018年 91卷 83-102页

作者： Garcia-Valls, Marisol Dubey, Abhishek Botti, Vicent Univ Carlos III Madrid Dept Telemat Engn Madrid Spain Univ Carlos III Madrid Distributed Real Time Syst Lab Madrid Spain Vanderbilt Univ Elect Engn & Comp Sci Nashville TN USA Vanderbilt Univ Smart Comp Lab Nashville TN USA Univ Politecn Valencia Comp Syst Valencia Spain

If last decade viewed computational services as a utility then surely this decade has transformed computation into a commodity. Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of "platform as a service" or "software as a service", both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers, dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing, analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions;and analyzes and discusses the relevant foundational softwar

关键词： Social dispersed computing IoT Fog Computing Cloud Computing Dispersed Computing Social Computing Edge Computing distributed computing Cyber physical systems real time Middleware Virtualization Containers Microservices distributed transactions Blockchain Multi agent systems distributed coordination Complex event processing Networking

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Adaptive Scheduling parallel Jobs with Dynamic Batching in Spark Streaming

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IEEE TRANSACTIONS ON parallel AND distributed systems 2018年第12期29卷 2672-2685页

作者： Cheng, Dazhao Zhou, Xiaobo Wang, Yu Jiang, Changjun Univ North Carolina Charlotte Dept Comp Sci Charlotte NC 28223 USA Univ Colorado Dept Comp Sci Colorado Springs CO 80918 USA Tongji Univ Key Lab Embedded Syst & Serv Comp Shanghai 200000 Peoples R China

Today enterprises have massive stream data that require to be processed in real time due to data explosion in recent years. Spark Streaming as an emerging system is developed to process real time stream data analytics by using micro-batch approach. The unified programming model of Spark Steaming leads to some unique benefits over other traditional streaming systems, such as fast recovery from failures, better load balancing and resource usage. It treats the continuous stream as a series of micro-batches of data and continuously process these micro-batch jobs. However, efficient scheduling of micro-batch jobs to achieve high throughput and low latency is very challenging due to the complex data dependency and dynamism inherent in streaming workloads. In this paper, we propose A-scheduler, an adaptive scheduling approach that dynamically schedules parallel micro-batch jobs in Spark Streaming and automatically adjusts scheduling parameters to improve performance and resource efficiency. Specifically, A-scheduler dynamically schedules multiple jobs concurrently using different policies based on their data dependencies and automatically adjusts the level of job parallelism and resource shares among jobs based on workload properties. Furthermore, we integrate dynamic batching technique with A-Scheduler to further improve the overall performance of the customized Spark Streaming system. It relies on an expert fuzzy control mechanism to dynamically adjust the length of each batch interval in response to time-varying streaming workload and system processing rate. We implemented A-scheduler and evaluated it with a real-time security event processing workload. Our experimental results show that A-scheduler with dynamic batching can reduce end-to-end latency by 38 percent and meanwhile improve workload throughput and energy efficiency by 23 and 15 percent, respectively, compared to the default Spark Streaming scheduler.

关键词： Adaptive scheduling spark streaming parallel job data dependency resource allocation dynamic batching

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Confluence: Speeding Up Iterative distributed Operations by Key-Dependency-Aware Partitioning

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IEEE TRANSACTIONS ON parallel AND distributed systems 2018年第2期29卷 351-364页

作者： Liang, Feng Lau, Francis C. M. Cui, Heming Wang, Cho-Li Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

A typical shuffle operation randomly partitions data on many computers, generating possibly a significant amount of network traffic which often dominates a job's completion time. This traffic is particularly pronounced in iterative distributed operations where each iteration invokes a shuffle operation. We observe that data of different iterations are related according to the transformation logic of distributed operations. If data generated by the current iteration are partitioned to the computers where they will be processed in the next iteration, unnecessary shuffle network traffic between the two iterations can be prevented. We model general iterative distributed operations as the transform-and-shuffle primitive and define a powerful notion named Confluence key dependency to precisely capture the data relations in the primitive. We further find that by binding key partitions between different iterations based on the Confluence key dependency, the shuffle network traffic can always be reduced by a predictable percentage. We implemented the Confluence system. Confluence provides a simple interface for programmers to express the Confluence key dependency, based on which Confluence automatically generates efficient key partitioning schemes. Evaluation results on diverse real-life applications show that Confluence greatly reduces the shuffle network traffic, resulting in as much as 23 percent job completion time reduction.

关键词： Spark shuffle key dependency iterative distributed operation partitioning

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Synthesization of High-Utility Patterns in parallel Computing

Synthesization of High-Utility Patterns in Parallel Computin...

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International workshop/Symposium on distributed Simulation and real-time Applications (DS-RT)

作者： Jerry Chun-Wei Lin Yuanfa Li Matin Pirouz Linlin Tang Miroslav Voznak Lukas Sevcik Western Norway University of Applied Sciences Bergen Norway Harbin Institute of Technology (Shenzhen) Shenzhen China California State University Fresno USA VSB-Technical University of Ostrava Ostrava Czech Republic

High utility pattern mining (HUPM) has become a key issue in knowledge discovery since it provides retailers and managers with useful information for making decisions efficiently. However, previous studies most focused on mining the high-utility patterns (HUPs) from a single database. In this paper, we present a framework to incorporate the weighted model for parallel synthesis of the discovered HUPs from various databases. The pre-large concept was also used as a buffer here in order to provide more prospective HUPs, thus providing higher accuracy of the synthesized patterns. From our experiments, the developed model exceeds existing works, in particular the designed model has increased precision and recall on knowledge synthesization compared to the previous works.

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Enabling Run-time Managed distributed Mobile Computing 18

Enabling Run-Time Managed Distributed Mobile Computing

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9th workshop on parallel Programming and Run-time Management Techniques for Many-Core Architectures (PARMA) / 7th workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (DITAM)

作者： Zanella, Michele Massari, Giuseppe Fornaciari, William Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9781450364447

The increasing pervasiveness of mobile devices combined with their replacement rate, led us to deal with the disposal of an increasing amount of still working electronic devices. This work proposes an approach to mitigate this problem by extending the mobile devices' lifetime, by integrating them as part of a distributed mobile computing system. Thanks also to the growing computational power of such devices, this paradigm opens up the opportunity to deploy mobile applications in a distributed manner. This, without forgetting the energy budget management as a paramount objective. In this work, we built a proof-of-concept, based on the extension of a run-time resource manager to support Android applications. We introduced a energy-aware device selection policy to dispatch the application workload according to both device capabilities and run-time status. Experimental results show that, as well as increasing the utilization of multiple mobile devices available to the single user, using an energy-efficiency and distributed approach can increase the battery duration between 12% and 36%.

关键词： Mobile Computing Portable devices distributed systems Resource Management Mobile Cloud Computing

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