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Adaptive Fault-Tolerant Strategy for Latency-Aware IoT Application Executing in Edge computing Environment

IEEE INTERNET OF THINGS JOURNAL 2022年第15期9卷 13250-13262页

作者： Mudassar, Muhammad Zhai, Yanlong Lejian, Liao Beijing Inst Technol Sch Comp Sci Beijing 100081 Peoples R China COMSATS Univ Islamabad Dept Comp Sci Vehari Campus Vehari 61100 Pakistan

Edge computing has recently evolved that offers to execute jobs efficiently by pushing cloud capabilities to edge of the network, this improves the quality of services to latency-oriented Internet of Things (IoT) applications when compared with cloud computing. By using current smart devices as edge nodes, edge computing can provide elastic resources that allow distributed data processing in a decentralized way. Still these smart devices are resource constrained in nature and tends to face a high failure rate than traditional distributed systems, the implementation of a fault-tolerant system that ensures the reliability and application availability becomes a key requirement. In this article, we propose a fault-tolerance methodology based on checkpointing and replication for the edge computing. Our proposed system uses a smart checkpointing for the IoT application tasks executing in a distributed edge network, and by replicating the checkpoint files on alternative edge nodes in the vicinity allowed to increase the system reliability. The experimental results show that our approach is effective in terms of reliability and availability of tasks executing in the edge network along with meeting deadlines of an IoT application.

关键词： Internet of Things Checkpointing Fault tolerant systems Fault tolerance Reliability Edge computing Cloud computing distributed computing edge computing fault tolerance Internet of Things (IoT)

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A comparative study to accelerate field development plan optimization

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第PartD期208卷 109708-109708页

作者： Loomba, Ashish Kumar Botechia, Vinicius Eduardo Schiozer, Denis Jose Univ Estadual Campinas Ctr Petr Studies Sch Mech Engn Campinas SP Brazil

Rigorous and continual advancement in the computational domain have drastically reduced the time consumed for simulations. Despite this, the heterogeneity and requirement to use compositional models, among constraints such as field size, can still thwart the optimization of the field development plan (FDP) in terms of time spent. To address this, we proposed and compared different techniques to underscore how to optimize FDP efficiently under uncertainty using an example of a giant benchmark field. We compared four workflows to improve the efficiency of the optimization process. Method 1 uses a full-field model (FFM) approach with an intelligent selection process to eliminate the poorest FDPs. As it is usual to divide a field into sectors for risk-aversion and strategic purposes, Method 2 uses an isolated-sector approach to reduce average simulation time. Method 3 employs the FFM approach to perform the optimization using only the monetary value of the partial life of the field. Method 4 uses a cluster-based search space reduction technique for a predictive analysis from the technical results obtained with partial simulations, which are similar to Method 3. To ensure good decisions, the optimized FDP was always implemented in the FFM with complete field-life at the end of all methods. All proposed workflows are promising in terms of efficiency, acknowledge the entire envelope of uncertainty, and consider multiple scenarios to improve the chances of success of the optimized FDP in the real field. Aside from obtaining good results when compared to traditional methods, we also saved 80-93% of the computational time with these methods. Thus, one can reduce exorbitant costs and delays in performing the FDP optimization. As anticipated, we observed a generic trade-off between decreasing computational time and increasing field objective function. Despite this, using a new algorithm with predictive analytics in Method 4 produced the best improvement within the shortest time

关键词： distributed computing Efficiency Field development plan Giant field Isolated sector Optimization Partial simulations Predictive analytics Propagation of best experiments Reservoir simulation Time-consuming models Uncertainties

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A Blockchain-Based Decentralized Marketplace for Trustworthy Trade in Developing Countries

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IEEE ACCESS 2022年 10卷 79100-79123页

作者： Pereira Lamela, Manuel Rodriguez-Molina, Jesus Martinez-Nunez, Margarita Garbajosa, Juan Univ Politecn Madrid Dept Telemat & Elect Engn Madrid 28040 Spain Univ Politecn Madrid Dept Org Engn Business Adm & Stat Madrid 28040 Spain Univ Politecn Madrid Higher Tech Sch Engn Informat Syst Madrid 28040 Spain

The possibilities that distributed Ledger Technologies (DLTs) offer for cooperation, development, and achievement of the Sustainable Development Goals (SDGs) are remarkable. This is because DLTs enable several key features, such as sharing complete information about every data transaction in the distributed system that participants belong to, the immutability of the recorded data transactions, or consensus among what data can be regarded as true, of great usefulness for the implementation of the SDGs. As far as developing countries are concerned, this information could be useful in trading locally produced goods, as it could enhance the reputation and profitability of Small-Scale Producers (SSPs). Unfortunately, it is rare to find a digitalized marketplace that has been specifically implemented for this application domain. This paper puts forward a blockchain-based marketplace that makes use of Smart Contracts and offers information about how the sold goods were produced and can be traced to their very origin. Besides, cloud computing has been conceived to be used in this development from the beginning to reduce the computational resources required by end user operations. An implementation with cloud computing facilities, software components running on the Ethereum blockchain and a web front end have been tested with satisfactory performance results.

关键词： Blockchains Developing countries Sustainable development Costs Profitability Cloud computing Supply chains distributed computing sustainable development peer-to-peer computing

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TOWARD ENERGY-EFFICIENT distributed FEDERATED LEARNING FOR 6G NETWORKS

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IEEE WIRELESS COMMUNICATIONS 2021年第6期28卷 34-40页

作者： Khowaja, Sunder Ali Dev, Kapal Khowaja, Parus Bellavista, Paolo Korea Polytech Univ Dept Mechatron Engn Shihung South Korea Univ Sindh Dept Telecommun Engn Fac Engn & Technol Jamshoro Sindh Pakistan Univ Sindh Jamshoro Sindh Pakistan Univ Johannesburg Johannesburg South Africa Univ Bologna Bologna Italy

The provision of communication services via portable and mobile devices, such as aerial base stations, is a crucial concept to be realized in 5G/6G networks. Conventionally, IoT/edge devices need to transmit data directly to the base station for training the model using machine learning techniques. The data transmission introduces privacy issues that might lead to security concerns and monetary losses. Recently, federated learning was proposed to partially solve privacy issues via model sharing with the base station. However, the centralized nature of federated learning only allows the devices within the vicinity of base stations to share trained models. Furthermore, the long-range communication compels the devices to increase transmission power, which raises energy efficiency concerns. In this work, we propose the distributed federated learning (DBFL) framework that overcomes the connectivity and energy efficiency issues for distant devices. The DBFL framework is compatible with mobile edge computing architecture that connects the devices in a distributed manner using clustering protocols. Experimental results show that the framework increases the classification performance by 7.4 percent in comparison to conventional federated learning while reducing the energy consumption.

关键词： Training data Base stations Privacy Energy efficiency 6G mobile communication Collaborative work Propagation losses distributed computing

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Research on power ecological data service model based on privacy computing and computing power network technology

Research on power ecological data service model based on pri...

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2024 International Conference on Cloud computing, Performance computing, and Deep Learning, CCPCDL 2024

作者： Song, Yinghua Fan, Qiaoyun Nanjing211106 China NARI Technology Co. Ltd. Nanjing211106 China China Mobile Group Jiangsu Co. Ltd. Jiangsu Nanjing210006 China

ISBN: (纸本)9781510683044

Data has become an important factor of production, and the collection, storage and value sharing of data have become increasingly important. At present, there is a difficult problem of how to realize data value mining and sharing on the premise of ensuring data security. In order to solve these problems, this article conducts relevant research on the key technologies of the "data+cloud" power ecological data service model based on privacy computing and computing power network technology, and proposes to use privacy computing technology and computing power network to build a "data+cloud" power ecological data service realizes multi-party data joint modeling and analysis, thereby building a safer and more reliable data application solution. © 2024 SPIE.

关键词： Data modeling Data privacy Data processing distributed computing Computer security Clouds Industry Network security Network architectures Real-time computing

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Efficient microscopy image analysis on CPU-GPU systems with cost-aware irregular data partitioning

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JOURNAL OF PARALLEL AND distributed computing 2022年 164卷 40-54页

作者： Barreiros Jr, Willian Melo, Alba C. M. A. Kong, Jun Ferreira, Renato Kurc, Tahsin M. Saltz, Joel H. Teodoro, George Univ Brasilia Dept Comp Sci Brasilia DF Brazil Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA Georgia State Univ Dept Math & Stat Atlanta GA 30303 USA

The analysis of high resolution whole slide tissue images is a computationally expensive task, which adversely impacts effective use of pathology imaging data in research. We propose runtime solutions to enable efficient execution of pathology image analysis applications on modern distributed memory hybrid platforms equipped with both CPUs and GPUs. Hybrid systems offer significant computation capacity, but taking advantage of this computing power is complex. An application developer may have to implement multiple versions of data processing codes targeted for different computing devices. The developer also has to tackle the challenges of efficiently distributing computational load among the nodes of a distributed memory machine and among computing devices on a node. This is particularly difficult in analysis of high resolution images because of irregular computing costs of processing different image regions. In order to address these problems, we have leveraged a high-level image processing language (Halide) and integrated it into our runtime system called Region Templates (RT). The language simplifies the application development while generating code for multiple devices, such as CPU and GPU. The integration with RT allows for efficient multiple node hybrid execution. We also developed a novel cost-aware data partitioning (CADP) strategy that considers the workload irregularity to minimize load imbalance. Our experimental evaluation shows significant performance improvements on hybrid CPU-GPU machines, as compared with using a single processor (CPU or GPU), as well as on multi-GPU systems. CADP resulted in 1.7x better performance than other workload partitioning approaches (e.g., KD-Trees) on a hybrid machine and was up to 2.24x faster in multi-node settings. (C)& nbsp;2022 Elsevier Inc. All rights reserved.

关键词： Microscopy imaging Region-templates framework Halide distributed computing Hybrid computing

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Coded Computation Across Shared Heterogeneous Workers With Communication Delay

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 3371-3385页

作者： Sun, Yuxuan Zhang, Fan Zhao, Junlin Zhou, Sheng Niu, Zhisheng Gunduz, Deniz Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn Beijing 100084 Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen 518172 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2BT England Univ Modena & Reggio Emilia UNIMORE Dept Engn Enzo Ferrari I-41121 Modena Italy

distributed computing enables large-scale computation tasks to be processed by multiple workers in parallel. However, the randomness of communication and computation delays across the workers causes the straggler effect, which may degrade the delay performance. Coded computation helps to mitigate the straggler effect, but the amount of redundant load and task assignment to the workers should be carefully optimized. In this work, we consider a multi-master heterogeneous-worker distributed computing scenario, where multiple matrix multiplication tasks are encoded and allocated to the workers with different computing capabilities. The goal is to minimize the communication plus computation delay of all the tasks. We propose joint worker assignment, resource allocation and load allocation algorithms under both dedicated and fractional worker assignment policies, where each worker can process the encoded tasks from either a single master or multiple masters, respectively. Then, the non-convex delay minimization problem is solved by employing the Markov's inequality-based approximation, Karush-Kuhn-Tucker conditions, and successive convex approximation methods. Through extensive simulations, we show that the proposed algorithms can reduce the task completion delay compared to the benchmarks.

关键词： Task analysis Delays Resource management Encoding distributed computing Computational modeling Markov processes Coded computation communication delay Markov's inequality convex optimization successive convex approximation

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Federated Learning With Heterogeneity-Aware Probabilistic Synchronous Parallel on Edge

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IEEE TRANSACTIONS ON SERVICES computing 2022年第2期15卷 614-626页

作者： Zhao, Jianxin Han, Rui Yang, Yongkai Catterall, Benjamin Liu, Chi Harold Chen, Lydia Y. Mortier, Richard Crowcroft, Jon Wang, Liang Beijing Inst Technol Sch Comp Sci & Technol Beijing 100811 Peoples R China TravelSky Technol Ltd Beijing Engn Res Ctr Civil Aviat Big Data Beijing 101318 Peoples R China Key Lab Intelligent Passenger Serv Civil Aviat CA Beijing 101318 Peoples R China Univ Cambridge Dept Comp Sci & Technol Cambridge CB2 3AX England Technol Univ Delft Dept Comp Sci NL-2628 Delft Netherlands

With the massive amount of data generated from mobile devices and the increase of computing power of edge devices, the paradigm of Federated Learning has attracted great momentum. In federated learning, distributed and heterogeneous nodes collaborate to learn model parameters. However, while providing benefits such as privacy by design and reduced latency, the heterogeneous network present challenges to the synchronisation methods, or barrier control methods, used in training, regarding system progress and model convergence etc. The design of these barrier mechanisms is critical for the performance and scalability of federated learning systems. We propose a new barrier control technique called Probabilistic Synchronous Parallel (PSP). In contrast to existing mechanisms, it introduces a sampling primitive that composes with existing barrier control mechanisms to produce a family of mechanisms with improved convergence speed and scalability. Our proposal is supported with a convergence analysis of PSP-based SGD algorithm. In practice, we also propose heuristic techniques that further improve the efficiency of PSP. We evaluate the performance of proposed methods using the federated learning specific FEMNSIT dataset. The evaluation results show that PSP can effectively achieve good balance between system efficiency and model accuracy, mitigating the challenge of heterogeneity in federated learning.

关键词： Collaborative work Synchronization Convergence Training Servers Computational modeling Probabilistic logic Federated learning edge computing distributed computing barrier control

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Accelerating physics simulations with tensor processing units: An inundation modeling example

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE computing APPLICATIONS 2022年第4期36卷 510-523页

作者： Hu, R. Lily Pierce, Damien Shafi, Yusef Boral, Anudhyan Anisimov, Vladimir Nevo, Sella Chen, Yi-fan Google Res 1600 Amphitheatre Pkwy Mountain View CA 94043 USA

Recent advancements in hardware accelerators such as Tensor Processing Units (TPUs) speed up computation time relative to Central Processing Units (CPUs) not only for machine learning but, as demonstrated here, also for scientific modeling and computer simulations. To study TPU hardware for distributed scientific computing, we solve partial differential equations (PDEs) for the physics simulation of fluids to model riverine floods. We demonstrate that TPUs achieve a two orders of magnitude speedup over CPUs. Running physics simulations on TPUs is publicly accessible via the Google Cloud Platform, and we release a Python interactive notebook version of the simulation.

关键词： CFD computational fluid dynamics distributed computing flood forecasting flood modeling inundation modeling large scale simulations parallel computing tensor processing units

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A simple two-agent system for multi-objective flexible job-shop scheduling

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2022年第1期43卷 42-64页

作者： Li, Yingli Wang, Jiahai Liu, Zhengwei Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

In this paper, a simple two-agent multi-objective scheduling system for flexible job-shop scheduling problem is proposed and corresponding framework is given. Under the framework, a broadcast communication mechanism with task mark and leader-follower control mode are designed and used to ensure orderly activities among agents. To obtain initial solution and improve it, competition and cooperation strategies are developed. To jump out of the local optimal solution and expend the search space, an adaptive iterative loop solving mechanism is designed. Three commonly used benchmark instance sets are adopted to test the performance of the proposed method. Computation results and comparison analysis with other excellent algorithms demonstrate that it is feasible and effective.

关键词： Flexible job-shop Multi-objective optimization Multi-agent technology distributed computing

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