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检索条件"任意字段=Proceedings - 24th International Conference on Distributed Computing Systems Workshops"

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End-to-End Production Process Orchestration for Smart Printing Factories: An Application in Industry 24

End-to-End Production Process Orchestration for Smart Printi...

引用

24th IEEE international Enterprise distributed Object computing conference (IEEE EDOC)

作者： Traganos, Konstantinos Vanderfeesten, Irene Grefen, Paul Erasmus, Jonnro Gerrits, Ton Verhofstad, Wim Eindhoven Univ Tehcnol Sch Ind Engn Eindhoven Netherlands Open Univ Netherlands Dept Informat Sci Heerlen Netherlands Canon Prod Printing Netherlands BV Venlo Netherlands

ISBN: (数字)9781728164731

ISBN: (纸本)9781728164731

Media production printing is following the Industry 4.0 era developments, towards the concept of smart printing factory. there, printers, post-processing machinery, media and product handling robots, automated guided vehicles and human operators shall be integrated in a seamless way for achieving higher production rates in a more efficient way, in a safe and ergonomic working environment. this integration of the various actors and the orchestration of their activities can be enabled by a process management system. this paper presents such a Printing Process Management System (PPMS), based on Business Process Management (BPM) technologies, that models and enacts end-to-end activities in a print shop. PPMS is a core information system of a cyber-physical system (CPS) for direct control of devices and humans in a smart production printing environment. It was implemented and demonstrated in the European EIT OEDIPUS1 project for a major international printing technology provider, proving feasibility and giving insights into how BPM concepts and tools can be applied in smart printing factories.

关键词： Printing factory 4.0 Smart manufacturing Industry 4.0 Production printing process orchestration Business Process Management Cyber-Physical systems EIT OEDIPUS project

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Concept of Energy Efficient ESP32 Chip for Industrial Wireless Sensor Network 24

Concept of Energy Efficient ESP32 Chip for Industrial Wirele...

引用

24th IEEE international conference on Intelligent Engineering systems (INES)

作者： Gatial, Emil Balogh, Zoltan Hluchy, Ladislav Slovak Acad Sci Dept Parallel & Distributed Comp Inst Informat Bratislava Slovakia

ISBN: (纸本)9781728110592

this paper proposes a concept of low-cost industrial wireless sensor network integrating the power consumption efficient ESP32 chip. the main focus is given on best practices of various sensors integration into Real-Time Operating System (RTOS) of ESP32 devices. the work proposes an architecture for data collection, security and possibility of data analysis. Energy effectiveness is estimated for battery usage in different modes of the chip. the last part of this work presents the conclusions and guidelines for future work. Road map of a distributed modeling

关键词： Industrial Wireless Sensor Network power efficiency Message Queue Transport and Telemetry

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ConeSSD: A Novel Policy to Optimize the Performance of HDFS Heterogeneous Storage

ConeSSD: A Novel Policy to Optimize the Performance of HDFS ...

引用

IEEE international conference on High Performance computing and Communications (HPCC)

作者： Xiao Zhang Liang Wang Zhijie Huang Huiru Xie Yuchen Zhang Michael Ngulube School of Computer Science Northwestern Polytechnical University Xi'an China

HDFS(Hadoop distributed file system) is the core storage service of Hadoop, which stores and processes large datasets efficiently. therefore, the performance of storage services significantly impacts big data processing applications. Using SSD to replace HDD devices can obtain better performance, but due to the high unit price, it is too expensive to replace all HDD with SSD in HDFS. therefore, heterogeneous storage has been widely used in HDFS. However, the existing hierarchical strategy of HDFS is simple and cannot obtain better cost performance. this paper presents a novel heterogeneous storage policy named Cone_SSD, which fully uses SSD media on each node to get the IO performance to near SSD. Experimental results show that the proposed policy improves read and write performance by 48.11 % and 47.45%, respectively, and the fluctuation of reading performance decreases by 54.92 % compared with other policies under the same price-performance ratio.

关键词： Performance evaluation Fluctuations Costs File systems Media Big Data

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Efficient. Scalable and Robust Data Shuffle Service for distributed MapReduce computing on Cloud

Efficient. Scalable and Robust Data Shuffle Service for Dist...

引用

IEEE international conference on High Performance computing and Communications (HPCC)

作者： Rong Gu Xu Huang Haipeng Dai Xiaoyu Geng Xiaofei Chen Yihua Huang Fu Xiao Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Nanjing University of Posts and Telecommunications Nanjing China

distributed Map Reduce computing frameworks, such as Hadoop, Spark, and Flink, are widely used in various domains which face big data challenges. Inside Map Reduce, Shuffle is a critical stage that bridges up the Map stage and Reduce stage through data partition and network transmission under the hood. With the rapidly growing scale of data and clusters, the all-to-all data transfer in Shuffle becomes inefficient and frequently fails, making Shuffle the bottleneck of many big data processing jobs. To improve the Shuffle efficiency, we propose a hybrid Shuffle model based on file pre-merging and partition reorganization mechanisms. To address the Shuf-fle stability issue, we design a novel data transmission model based on the replication mechanism. Finally, to support scalable Shuffle with high performance, we present a Shuffle parallelism degree tuning approach based on machine learning and searching techniques. We have implemented the proposed Shuffle models and strategies into the widely-used MapReduce-based big data platform Spark for evaluation. Experimental evaluation shows that, compared with the state-of-the-art systems, our proposed methods outperform them by 23% on average. As a practical application case, the proposed Shuffle service has been running in the real-world environment of ByteDance. Inc over one year with significant performance improvement.

关键词： Machine learning Parallel processing Big Data Data models Stability analysis Sparks Task analysis

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Energy Efficient Dynamic Load Balancing using Self-Organized Criticality in Grid computing

Energy Efficient Dynamic Load Balancing using Self-Organized...

引用

IEEE international conference on High Performance computing and Communications (HPCC)

作者： Vivek Kumar Chinmaya Kumar Swain Aryabartta Sahu Dept. of CSE IIT Guwahati India Dept. of CSE SRM University India

Grid computing refers to a collection of compute resources from different geographic locations to achieve a common objective. It involves tremendous amounts of computational task which require reliable resource sharing across the grid for efficiently executing the tasks. Load disparity is a major issue in grid computing. So effectively and efficiently utilize the potentials of huge distributed resources, load balancing algorithms are of primary importance. In this paper, we proposed an elegant approach using Bak-Tang-Wiesenfeld sandpile model for dynamically load-balancing of tasks across the processing sites in an energy efficient manner. the proposed approach reduces redundant migrations between processing sites by transferring tasks in a breadth first manner. In this approach avalanche propagation condition is stricter and depends on the average load of two adjacent layers of neighbours and use of signalling mechanism rather than actual transfer of tasks during an avalanche, until a destination comes in equilibrium. We experimentally validated by a number of numerical experiments using Google traces. the results of the experiments show the effectiveness of the proposed approach which reduces the number of migrations and improves the energy efficiency without much increasing the total flowtime and degrading the load balancing aspects as compared to state-of-the-art approaches.

关键词： Heuristic algorithms Computational modeling Load management Grid computing Energy efficiency Internet Resource management

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Joint Latency and Energy Optimization of Online Task Offloading Algorithms for Vehicular Edge computing

Joint Latency and Energy Optimization of Online Task Offload...

引用

IEEE international conference on High Performance computing and Communications (HPCC)

作者： Pingping Dong Cheng Nie Yajing Li Yuning Zuo Xiaojuan Lu Lianming Zhang College of Information Science and Engineering Hunan Normal University Changsha China

With the advantages of low latency and easy deployment, vehicle edge computing can effectively solve the task offloading problem for vehicles as well as surrounding pedestrians. However, due to hardware device limitations, vehicle edge nodes often have limited computing power and need to draw on the free resources on other surrounding vehicles. And the mobility of the vehicle also poses a challenge for task offloading. A common solution to this type of problem is Multi-Arm Bandit (MAB) theory, but existing algorithms based on MAB are not applicable to the dynamic vehicle environment. In this paper, we design an online learning-based vehicle task offloading algorithm (OLUCB), which performs task offloading in a distributed manner without frequent acquisition of vehicle state information, and adds three online capabilities of input-awareness, occurrence-awareness and speed-awareness to adapt to dynamic environments. the algorithm learns the task offloading performance of the surrounding vehicles and converges quickly to the optimal helper vehicle (HEV) with guaranteed performance. Extensive experiments in realistic highway scenario have been conducted, and results illustrate that the OLUCB achieves low latency and low energy performance. Compared to existing task offloading algorithms based on adaptive learning, the average latency, average energy consumption, average cost and average bit cost are reduced by 3%, 51%, 43% and 42% respectively.

关键词： Road transportation Energy consumption Costs Heuristic algorithms Resource management Task analysis Vehicle dynamics

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Hybrid Decision Based Multi-Agent Deep Reinforcement learning for Task Offloading in Collaborative Edge-Cloud computing

Hybrid Decision Based Multi-Agent Deep Reinforcement learnin...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Juan Chen Zongling Wu Lei Wu Yunni Xia Yang Wang Ling Xiong Canghong Shi School of Computer and Software Engineering Xihua University Chengdu China School of Information Science and Technology Southwest Jiaotong University Chengdu China School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China College of Computer Science Chongqing University Chongqing China

To support computing-intensive and latency-sensitive Internet-of-things (IoT) applications, we establish a three-layer collaborative edge-cloud computing network, in which multiple IoT devices, edge servers and cloud servers are allocated at IoT layer, edge layer and cloud layer, ***, it is still challenging to satisfy the service requests with inevitable resource competition among task offloading. To solve the joint computation offloading and resource allocation problem, we formulate it as a multi-agent Markov decision process (MAMDP), where action space of each agent contains discrete-continuous hybrid decision. Firstly, we relax the discrete action to a continuous set by developing a probabilistic method. then, by taking advantage of a continuous action based centralized training and distributed execution strategy, a cooperative multi-agent deep reinforcement learning (MADRL) based framework with each IoT device acts as an agent is established. the objective function is to minimize the total system cost in terms of the energy consumption of IoT device and the renting charge of edge/cloud servers. the simulation results demonstrate the advantageous of the proposed MADRL over the three state-of-the-art DRL based agents in reducing the system cost.

关键词： Costs Simulation Collaboration Reinforcement learning Probabilistic logic Internet of things Servers

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Decentralized Robust Servomechanism Problem for systems with distributed Time-Delay 24

Decentralized Robust Servomechanism Problem for Systems with...

引用

24th international conference on System theory, Control and computing (ICSTCC)

作者： Iftar, Altug Eskisehir Tech Univ Dept Elect & Elect Engn TR-26555 Eskisehir Turkey

ISBN: (纸本)9781728198095

the problem of desining stabilizing decentralized controllers to achieve robust asymtotic traking of certain references despite certain disturbances, which is called as the decentralized robust servomecanism problem, is consdered for lineer time-invarant (LTI) systems with disributed timedelay. the formulation, however, allows the reprsentation of discrete and distributed time-delays together. It is asumed that, the references and the distrbance satisfy a LTI delaydiferential equation with distrubuted and/or discrete timedelays. the case of references and the disturbance satisfying a LTI diferential equation can also be treated. the necesary and suficient condtions for the solvbility of this problem are derived. the strcture of the controler, when a solution exists, is also presented.

关键词： Time delay

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An Adaptive RPC Mechanism for Performance and Node Fault Tolerance Optimization in HDFS

An Adaptive RPC Mechanism for Performance and Node Fault Tol...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Jingyu Zhang Zhengwen Shu Lailong Luo School of Computer & Communication Engineering Changsha University of Science & Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China

With the rapid development and evolution of information technologies, the big data industry has been experiencing exponential explosive data growth. Since the huge business and research value behind the large-scale data, big data technology has become one of the hotest research fields in academia and industry. To the big data, storing and processing the data is core function of the file systems. Hadoop distributed File System (HDFS) is the most typical distributed big data architecture, which has the characteristics of high reliability, high fault tolerance, and low hardware cost. HDFS provides the efficient task implementation and fault tolerance among nodes by Remote Procedure Call (RPC) mechanism. However, the traditional RPC mechanism has remarkable defects with the fixed timing method, and cannot quickly establish the relationship between node downtime and network congestion. In this paper, we propose an improved adaptive RPC mechanism for node fault tolerance and performance. the proposed method classifies nodes by the data block access statistic, and dynamically adjusts the RPC interval time. this method can be used to reduce the networking traffic and processing pressure of NameNode nodes, and improve the I/O and task performance. through the node classification, an inrack method is used to achieve better performance on fault tolerance. Eventually, we design the extensive experiments to evaluate the proposed method, and experimental results show it improves the performance compared with state-of-the-art method.

关键词： Industries Fault tolerance Adaptive systems Heart beat File systems System performance Fault tolerant systems

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BTDFS: B+ Tree-Based distributed File Storing Mechanism for Improving Small File Storage

BTDFS: B+ Tree-Based Distributed File Storing Mechanism for ...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Jingyu Zhang Jiachen Tian Lailong Luo School of Computer & Communication Engineering Changsha University of Science & Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China

Hadoop is currently the most popular big data processing architecture, which provides a processing framework for managing and analyzing massive data. Hadoop distributed File System (HDFS) is the core component for storing data in the Hadoop systems. It performs well when storing and managing large-sized files. However, it shows extremely bad performance when dealing with a large number of small files, which is reflected in the consumption of a large amount of memory space of the NameNode node and the low efficiency of accessing small files. Archive file is proposed to solve this problem. It combines small files into larger merged files for storage, and uses index files to record the relevant information of small files in the merged files. However, archive files (e.g., Hadoop Archive, MapFile) all require additional processing and multiple I/O operations to obtain index information before actually accessing the file content, which reduces the efficiency of file access. this paper proposes a novel distributed file storing mechanism called B+ Tree-Based distributed File Storing mechanism (BTDFS), which combines a large number of small files into a merged file, records the index information of the small files in the merged file, and organizes the index information into a B+ tree for storage. Using BTDFS, while reducing the metadata consumption of the NameNode node, improves the access performance of a single small file with additional abilities, which provides convenience for analyzing a large number of small files. Finally, this paper designs corresponding experiments for the proposed BTDFS, and tests and analyzes the memory usage in N ameN ode and the access performance of small files. the experimental results show that the proposed BTDFS can effectively reduce the memory usage of the NameNode, and the efficiency of accessing small files is improved compared with state-of-the-art methods.

关键词： File systems Memory management distributed databases Big Data Metadata Indexing

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