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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

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An Elasticity Description Language for Task-parallel Cloud Applications 10

An Elasticity Description Language for Task-parallel Cloud A...

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10th international conference on Cloud computing and Services Science (CLOSER)

作者： Haussmann, Jens Blochinger, Wolfgang Kuechlin, Wolfgang Reutlingen Univ Parallel & Distributed Comp Grp Reutlingen Germany Univ Tubingen Symbol Computat Grp Tubingen Germany

ISBN: (纸本)9789897584244

In recent years, the cloud has become an attractive execution environment for parallel applications, which introduces novel opportunities for versatile optimizations. Particularly promising in this context is the elasticity characteristic of cloud environments. While elasticity is well established for client-server applications, it is a fundamentally new concept for parallel applications. However, existing elasticity mechanisms for client-server applications can be applied to parallel applications only to a limited extent. Efficient exploitation of elasticity for parallel applications requires novel mechanisms that take into account the particular runtime characteristics and resource requirements of this application type. To tackle this issue, we propose an elasticity description language. This language facilitates users to define elasticity policies, which specify the elasticity behavior at both cloud infrastructure level and application level. Elasticity at the application level is supported by an adequate programming and execution model, as well as abstractions that comply with the dynamic availability of resources. We present the underlying concepts and mechanisms, as well as the architecture and a prototypical implementation. Furthermore, we illustrate the capabilities of our approach through real-world scenarios.

关键词： Cloud computing High Performance computing parallel Application Elasticity

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Coded computing at Full Speed 40

Coded Computing at Full Speed

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40th IEEE international conference on distributed computing Systems (ICDCS)

作者： Tang, Bin Cao, Jiannong Cui, Runze Ye, Baoliu Li, Ye Lu, Sanglu Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Nantong Univ Sch Elect & Informat Nantong Peoples R China

ISBN: (纸本)9781728170022

distributed computing is the mainstream for large-scale machine learning and big data analytics, but its performance usually suffers from unpredictable stragglers, i.e., very slow nodes. Recently, coded computing has emerged as a new distributed computing paradigm that uses coding-theoretical approaches to mitigate the effect of stragglers. Most existing coding schemes only use the results from a certain number of fastest worker nodes to recover the output and completely ignore the partial work done by other worker nodes, leading to inferior performance. In this paper, for scenarios where each worker node transmits its local result to the master node only after it has finished the whole local computation, we introduce communication at full speed to characterize the full utilization of all the communication links between each worker node that has finished its local computation and the master node, and for scenarios where each worker node computes out the local result piece by piece and can forward each piece once available, we introduce computation at full speed to characterize the full utilization of the work done entirely or partially by all the worker nodes. Considering a general polynomial-based coding framework which encapsulates many advanced coding schemes for a variety of fundamental computing tasks, we propose a randomized approach where each worker node partitions its local result into pieces, generates and forwards random linear combinations of these pieces to the master node sequentially, and theoretically demonstrate that it can lead the coding framework to achieve communication at full speed. For some typical task scenarios, we further show that computation at full speed can be achieved by mapping the encoding operations in the randomized approach into a part of encoding on the input dataset. Experiments conducted on Alibaba Cloud as well as simulations show that our approaches can reduce the total runtime significantly.

关键词： distributed computing coded computing polynomial-based coding straggler tolerance

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A novel design for Data Processing Framework of Park-level Power System with Data Mesh concept

A novel design for Data Processing Framework of Park-level P...

引用

IEEE international conference on Energy Internet (ICEI)

作者： Jin Li Shixia Cai Lei Wang Mingyang Li Jiamao Li Hui Tu Alibaba Cloud Intelligence Beijing China Alibaba Cloud Intelligence Shenzhen China

Building a new-type power system with renewable energy is the key to achieving the target of carbon peak and carbon neutrality, which has been the consensus of the clean, low-carbon, and safe energy transition. The construction of a new-type power system at the industrial park level is the first demonstration of the coordinated optimization and intelligent control of power sources, grid, load, and energy storage to make overall planning to achieve diversified development. To sustain the ‘balance, security, diversity, and low cost’ construction goals of the new-type power system at the park level, the power grid dispatching and control system is transformed to the direction of intelligence and automation, which is reflected in the rapid emergence and construction of complex systems such as digital twin, simulation, reinforcement learning, model training and prediction, and intelligent decision-making of the EMS (Energy Manage System). This brings more complex data sharing, interaction, construction, and other technical requirements than ever before. At the same time, along with the simultaneous construction of new power systems such as micro-grids, virtual power plants, and distributed photovoltaic systems, it has exacerbated the problems of data divergence from business, low data utilization, and centralized architecture brought by the data lakes and data warehouses under the previous monolithic architecture, leading to the data model hard to deploy to meet the demand for efficient collection, interaction, and sharing of scattered and heterogeneous data under the new generation energy structure and greatly increases the work pressure of the data team, making the data team a bottleneck for business applications. Therefore, this paper proposes a novel design scheme of data processing framework with the data mesh concept, supports the federal computing and analysis engine by building a distributed metadata center, changes the previous centralized data modeling method t

关键词： Automation Buildings distributed databases Computer architecture Predictive models Metadata Data processing

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Democratizing parallel Filesystem Monitoring 22

Democratizing Parallel Filesystem Monitoring

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IEEE international conference on Cluster computing

作者： Evans, Richard Todd Univ Texas Austin Texas Adv Comp Ctr Austin TX 78712 USA

ISBN: (纸本)9781728166773

parallel filesystems (PFSs) are one of the most critical high-availability components of High Performance computing (HPC) systems. Most HPC workloads are dependent on the availability of a POSIX compliant parallel filesystem that provides a globally consistent view of data to all compute nodes of a HPC system. Because of this central role, failure or performance degradation events in the PFS can impact every user of a HPC resource. There is typically insufficient information available to users and even many HPC staff to identify the causes of these PFS events, impeding the implementation of timely and targeted remedies to PFS issues. The relevant information is distributed across PFS servers;however, access to these servers is highly restricted due to the sensitive role they play in the operations of a HPC system. Additionally, the information is challenging to aggregate and interpret, relegating diagnosis and treatment of PFS issues to a select few experts with privileged system access. To democratize this information, we are developing an opensource and user-facing parallel FileSystem TRacing and Analysis SErvice (PFSTRASE) that analyzes the requisite data to establish causal relationships between PFS activity and events detrimental to stability and performance. We are implementing the service for the open-source Lustre filesystem, which is the most commonly used PFS at large-scale HPC sites. Server loads for specific PFS I/O operations (IOPs) will be measured and aggregated by the service to automatically estimate an effective load generated by every client, job, and user. The infrastructure provides a real-time, user accessible text-based interface and a publicly accessible web interface displaying both real-time and historical data.

关键词： file system monitoring storage Lustre

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Hardware Accelerated ATLAS Workloads on the WLCG grid 19

Hardware Accelerated ATLAS Workloads on the WLCG Grid

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19th international Workshop on Advanced computing and Analysis Techniques in Physics Research (ACAT)

作者： Forti, A. C. Heinrich, L. Guth, M. Univ Manchester Sch Phys & Astron Oxford Rd Manchester M13 9PL Lancs England CERN European Lab Particle Phys Rue Geneve 23 CH-1211 Geneva Switzerland Albert Ludwigs Univ Freiburg Friedrichstr 39 D-79085 Freiburg Germany

In recent years the usage of machine learning techniques within data-intensive sciences in general and high-energy physics in particular has rapidly increased, in part due to the availability of large datasets on which such algorithms can be trained, as well as suitable hardware, such as graphic or tensor processing units, which greatly accelerate the training and execution of such algorithms. Within the HEP domain, the development of these techniques has so far relied on resources external to the primary computing infrastructure of the WLCG (Worldwide LHC computing grid). In this paper we present an integration of hardware-accelerated workloads into the grid through the declaration of dedicated queues with access to hardware accelerators and the use of Linux container images holding a modern data science software stack. A frequent use-case in the development of machine learning algorithms is the optimization of neural networks through the tuning of their Hyper Parameters (HP). For this often a large range of network variations must be trained and compared, which for some optimization schemes can be performed in parallel - a workload well suited for grid computing. An example of such a hyper-parameter scan on grid resources for the case of flavor tagging within ATLAS is presented.

关键词： High energy physics

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An Integrated Security Framework for OT System Based on Edge computing

An Integrated Security Framework for OT System Based on Edge...

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IEEE international Symposium on parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Haojin Wu M. Jiang Mingcan Cen College of Electronics and Engineering Guangxi Normal University Guilin China

The emergence of edge computing helps to address issues such as transmission latency, bandwidth, and privacy. Operational Technology (OT) systems require high costs to maintain the continuous operation of devices and generate large amounts of data. Combined with edge computing, the operational efficiency and data processing capability of OT systems can be improved. However, edge computing can also pose significant risks to OT systems. Many attackers are beginning to target OT systems, and attacks against industrial control systems have been growing these years. This paper focuses on protocol security, anomaly intrusion detection, and honeypot defense in industrial control systems (ICS). We propose an OTEC security framework that combines OT systems and edge computing to address these issues. The framework consists of three main vital functions: protocol improvement (enhancing the security of Modbus protocol), intrusion detection (detecting anomalous data from the system), and proactive defense (deploying honeypots). We verified the effectiveness of OTEC through simulation experiments. The results show that our framework enhances the Modbus protocol’s security and guarantees the protocol’s availability, integrity, and confidentiality. The intrusion detection module uses deep learning algorithms and can reach 94% detection accuracy. The honeynet we deployed recorded the attack behaviour of attackers from different regions, which can provide good training data for the intrusion detection module.

关键词： Integrated circuits Privacy Protocols Industrial control Image edge detection Intrusion detection Training data

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Performance optimization of Learning Trees modules in the ECL repository

Performance optimization of Learning Trees modules in the EC...

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Communication, Control and Intelligent Systems (CCIS)

作者： Shashank B Sudershan K S Shobha G S. Manonmani Roger Dev Dept. of Computer Science RV College of Engineering Bangalore India LexisNexis Risk Solutions USA

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

Multi-Node computation, also known as distributed computing, is a paradigm that allows for the efficient utilization of multiple interconnected nodes or machines to perform complex computational tasks. By dividing the workload and distributing it across multiple nodes, multi-node computation enables parallel processing, scalability, and improved performance for a wide range of applications. HPCC Systems® from LexisNexis Risk Solutions is a proven, open source solution for big data insights that can be implemented by businesses of all sizes. HPCC Systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. ECL is the Enterprise Control Language designed specifically for huge data projects using the HPCC Systems platform. It is extremely scalable and a powerful declarative data-processing language with native multi-processing capabilities and used for HPCC systems. Some core ECL functions, like the ones handling Learning Tree algorithms of Machine Learning Bundle are recursive in nature and hence high computational time is needed. This paper thus presents a solution to achieve optimized processing of ML learning tree algorithms in ECL through embedding python libraries. The proposed approach was tested on standard datasets and a significant decrease in computation time. For Real Estate Valuation Regression dataset the computation time was reduced from 11.371 seconds to 0.886 seconds using the proposed method showing 1324% improvement with no impact on the accuracy. Random Forests are also implemented using the proposed Decision Tree method allowing the users to specify the number of decision trees to be used in the forest.

关键词： Machine learning algorithms Scalability Big Data parallel processing Computational efficiency Decision trees Random forests Standards Optimization Python

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DQEMU: A Scalable Emulator with Retargetable DBT on distributed Platforms 20

DQEMU: A Scalable Emulator with Retargetable DBT on Distribu...

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49th international conference on parallel Processing (ICPP)

作者： Zhao, Ziyi Jiang, Zhang Liu, Ximing Gong, Xiaoli Wang, Wenwen Yew, Pen-Chung Nankai Univ Tianjin Peoples R China Univ Georgia Athens GA USA Univ Minnesota Minneapolis MN USA

ISBN: (纸本)9781450388160

The scalability of a dynamic binary translation (DBT) system has become important due to the prevalence of multicore systems and large multi-threaded applications. Several recent efforts have addressed some critical issues in extending a DBT system to run on multicore platforms for better scalability. In this paper, we present a distributed DBT framework, called DQEMU, that goes beyond a single-node multicore processor and can be scaled up to a cluster of multi-node servers. In such a distributed DBT system, we integrate a page-level directory-based data coherence protocol, a hierarchical locking mechanism, a delegation scheme for system calls, and a remote thread migration approach that are effective in reducing its overheads. We also proposed several performance optimization strategies that include page splitting to mitigate false data sharing among nodes, data forwarding for latency hiding, and a hint-based locality-aware scheduling scheme. Comprehensive experiments have been conducted on DQEMU with micro-benchmarks and the PARSEC benchmark suite. The results show that DQEMU can scale beyond a single-node machine with reasonable overheads. For "embarrassingly-parallel" benchmark programs, DQEMU can achieve near-linear speedup when the number of nodes increases - as opposed to flattened out due to lack of computing resources as in current single-node, multi-core version of QEMU.

关键词： Dynamic binary translator distributed system distributed emulator

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Scheme of Peer-to-Peer Trade in Multi-Microgrid Based on Blockchain

Scheme of Peer-to-Peer Trade in Multi-Microgrid Based on Blo...

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international conference on Advanced Cloud and Big Data (CBD)

作者： Guanjun Zhao Lizhong Jia Jie Xu Chang Liu Weijun Hu Chunpei Li State Grid Jinhua Power Supply Company Jinhua Zhejiang province China Advanced Innovation Center for Future Blockchain and Privacy Computing & Beihang University Beijing China

ISBN: (纸本)9798350309720

With the advent of the era of power trading, how to balance the interests of all parties has become the key to the development of electric power information. The Multi-Microgrid model has gradually become an important part of power trading due to its reliability and efficiency, but how to balance the profit game among the Multi-Microgrid has gradually become a difficult problem for market development. In view of the distributed transaction environment of multi-micro network, we propose to use blockchain technology for multi-party transaction, use Stackelberg game model to achieve the best balance of interests of all participants, and zero-knowledge proof combined with secret sharing scheme is used to achieve effective access control in transactions. We simulate and test the performance indexes of the scheme, and the results show that our scheme is feasible.

关键词： Access control Games Big Data Power markets Power grids Blockchains Peer-to-peer computing

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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international conference on Data Engineering

作者： Junhui Gao Qianru Wang Xin Zhang Juan Shi Xiang Zhao Qingye Han Yan Pan School of Computer Science Northwestern Polytechnical University School of Computer Science and Technology Xidian University National Key Laboratory of Information Systems Engineering National University of Defense Technology Air Force Engineering University Laboratory for Big Data and Decision National University of Defense Technology School of Management Science and Real Estate Chongqing University National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery.

关键词： Autonomous aerial vehicles Data engineering Air to ground communication Trajectory Batteries Time factors Task analysis

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