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distributed MONITORING FOR ENERGY INFRASTRUCTURES: A TWO-TIER ANALYSIS OVER WIRELESS NETWORKS

IEEE WIRELESS COMMUNICATIONS 2021年第6期28卷 13-18页

作者： Zhang, Chi Zhen, Liangli Zhou, Joey Chen, Cen ASTAR Inst High Performance Comp Singapore Singapore ASTAR Inst Infocomm Res Singapore Singapore

Wireless networks (e.g., 5G networks) enable distributed energy infrastructures to be connected even when they are geometrically isolated. Intelligent monitoring from remote sites therefore becomes possible, allowing decision makers to examine the status of distributed energy infrastructures from a central location. The major challenge is when local devices cannot perform the monitoring independently;transmitting every signal back to the central server triggers enormous amounts of wireless communication. To address this, we propose a two-tier AI system by offloading computations to multiple devices. Specifically, we build lightweight AI models for deployment on edge clients (i.e., edge sensors) and a large-scale AI model for the central server. These two types of AI models are trained with different criteria: the models on the edges act as the filtering tools to detect abnormal events and maximally avoid making false negative predictions, whereas the server model is supposed to be an expert for accurate predictions. By validating on a power theft dataset, we show that such a cascading methodology could filter out sufficient negative examples on the edge side while still being able to provide precise predictions on the second-round analysis.

关键词： Performance evaluation distributed computing Image edge detection Computational modeling Wireless networks Predictive models Sensors 5G mobile communication

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Parallel multi-view HEVC for heterogeneously embedded cluster system

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PARALLEL computing 2022年 112卷

作者： Jang, Seo Jin Liu, Wei Li, Wei Cho, Yong Beom Konkuk Univ Dept Elect Engn Seoul South Korea Deep ET Seoul South Korea

In this paper, we present a computer cluster with heterogeneous computing components intended to provide concurrency and parallelism with embedded processors to achieve a real-time Multi-View High-Efficiency Video Coding (MV-HEVC) encoder/decoder with a maximum resolution of 1088p. The latest MV-HEVC standard represents a significant improvement over the previous video coding standard (MVC). However, the MV-HEVC standard also has higher computational complexity. To this point, research using the MV-HEVC has had to use the Central Processing Unit (CPU) on a Personal Computer (PC) or workstation for decompression, because MVHEVC is much more complex than High-Efficiency Video Coding (HEVC), and because decompressors need higher parallelism to decompress in real time. It is particularly difficult to encode/decode in an embedded device. Therefore, we propose a novel framework for an MV-HEVC encoder/decoder that is based on a heterogeneously distributed embedded system. To this end, we use a parallel computing method to divide the video into multiple blocks and then code the blocks independently in each sub-work node with a group of pictures and a coding tree unit level. To appropriately assign the tasks to each work node, we propose a new allocation method that makes the operation of the entire heterogeneously distributed system more efficient. Our experimental results show that, compared to the single device (3D-HTM single threading), the proposed distributed MV-HEVC decoder and encoder performance increased approximately (20.39 and 68.7) times under 20 devices (multithreading) with the CTU level of a 1088p resolution video, respectively. Further, at the proposed GOP level, the decoder and encoder performance with 20 devices (multithreading) respectively increased approximately (20.78 and 77) times for a 1088p resolution video with heterogeneously distributed computing compared to the single device (3D-HTM single threading).

关键词： Multithreading Embedded device distributed computing Group of pictures Coding tree unit

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Cross Subspace Alignment Codes for Coded distributed Batch Computation

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第5期67卷 2821-2846页

作者： Jia, Zhuqing Jafar, Syed Ali Univ Calif Irvine Ctr Pervas Commun & Comp Dept Elect Engn & Comp Sci Irvine CA 92697 USA

The goal of coded distributed computation is to efficiently distribute a computation task, such as matrix multiplication, N-linear computation, or multivariate polynomial evaluation, across S servers through a coding scheme, such that the response from any R servers (R is called the recovery threshold) is sufficient for the user to recover the desired computed value. Current state-of-art approaches are based on either exclusively matrix-partitioning (Entangled Polynomial (EP) Codes for matrix multiplication), or exclusively batch processing (Lagrange Coded computing (LCC) for N-linear computations or multivariate polynomial evaluations). We present three related classes of codes, based on the idea of Cross-Subspace Alignment (CSA) which was introduced originally in the context of secure and private information retrieval. CSA codes are characterized by a Cauchy-Vandermonde matrix structure that facilitates interference alignment along Vandermonde terms, while the desired computations remain resolvable along the Cauchy terms. These codes are shown to unify, generalize and improve upon the state-of-art codes for distributed computing. First we introduce CSA codes for matrix multiplication, which yield LCC codes as a special case, and are shown to outperform LCC codes in general in download-limited settings. While matrix-partitioning approaches (EP codes) for distributed matrix multiplication have the advantage of flexible server computation latency, batch processing approaches (CSA, LCC) have significant advantages in communication costs as well as encoding and decoding complexity per matrix multiplication. In order to combine the benefits of these approaches, we introduce Generalized CSA (GCSA) codes for matrix multiplication that bridge the extremes of matrix-partitioning and batch processing approaches and demonstrate synergistic gains due to cross subspace alignment. Finally, we introduce N-CSA codes for N-linear distributed batch computations and multivariate batc

关键词： Coding distributed computing parallelization security

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distributed parametric model checking timed automata under non-Zenoness assumption

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FORMAL METHODS IN SYSTEM DESIGN 2021年第1-3期59卷 253-290页

作者： Andre, Etienne Hoang Gia Nguyen Petrucci, Laure Sun, Jun Univ Lorraine LORIA INRIA CNRS Nancy France CEA Saclay NANOINNOV CEA LIST F-91120 Palaiseau France Univ Sorbonne Paris Nord LIPN CNRS UMR 7030 F-93430 Villetaneuse France Singapore Management Univ Sch Informat Syst Singapore Singapore

Model checking timed systems may be negatively impacted by the presence of Zeno runs as counterexamples. Such runs contain an infinite number of discrete actions occurring in a finite time, which is unrealistic;therefore, these runs must be pruned during model checking. Given a model of a timed system and a property, parametric model checking aims at synthesizing timing valuations such that the model satisfies the property. A popular formalism for parametric timed model checking is parametric timed automata (PTAs), an extension of timed automata with unknown constants. We address the presence of Zeno runs in parametric model checking. We first show that the emptiness of the set of parameter valuations for which at least one counterexample run being non-Zeno is undecidable for PTAs. As a second contribution, we adopt a more pragmatic approach, and propose a procedure based on a transformation of PTAs into Clock Upper Bound PTAs to derive all valuations whenever it terminates, and some of them otherwise. We implemented our algorithm in IMITATOR, and we show that our method can be distributed over a cluster to further speedup the computation.

关键词： Parameter synthesis Parametric model checking Zeno behaviors Parametric timed automata distributed computing

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Securely Straggler-Exploiting Coded Computation for distributed Matrix Multiplication

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IEEE ACCESS 2021年 9卷 167374-167388页

作者： Yang, Heecheol Hong, Sangwoo Lee, Jungwoo Chungnam Natl Univ Div Comp Convergence Daejeon 34134 South Korea Seoul Natl Univ Dept Elect & Comp Engn Commun & Machine Learning Lab Seoul 08826 South Korea

In this paper, we consider coded computation for matrix multiplication tasks in distributed computing, which can mitigate the effect of slow workers, called stragglers, using a coding approach. We assume that the straggler computation results can be leveraged at the master by assigning multiple sub-tasks to the workers. In this scenario, a new coded computation scheme is proposed to preserve the data security and privacy from workers, which is called securely straggler-exploiting codes (SSEC). Moreover, the proposed SSEC can efficiently reduce the communication load in distributed computing for assigning the sub-tasks to the workers, by overlapping the encoded matrices in assigning multiple sub-tasks with appropriate polynomial functions. It is also proven that the data security and privacy constraints are satisfied in SSEC in an information-theoretic sense. In conclusion, SSEC shows good performance on the recovery threshold and communication loads and compare them with the existing secure coded computation schemes for matrix multiplication tasks.

关键词： Task analysis Encoding distributed computing Data privacy Codes Data security Libraries distributed computing coded computation matrix multiplication polynomial codes

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distributed Resource Allocation in RF-Powered Cognitive Ambient Backscatter Networks

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2021年第4期5卷 1657-1668页

作者： Zhu, Kun Xu, Longteng Niyato, Dusit Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Huawei Technol Co Ltd Cloud Technol Ctr Nanjing 210012 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

Ambient backscatter communications has shown the great potentials to achieve spectrum and energy efficient communications for future wireless networks (e.g., 6G). Besides, to fully exploit the ambient RF environment, backscatter communications can be combined with RF-powered cognitive networks which brings more reliable and flexible communications. In such networks, a cognitive user pair could communicate through active transmission or backscattering existing signals. In this work, we investigate the distributed resource allocation in terms of channel selection and backscatter power allocation in such setting. A unified framework integrating stochastic geometry and evolutionary game is proposed to model and analyze the distributed resource allocation. Specifically, stochastic geometry is used to analyze the average performance for users considering different communication modes. Then with the average performance obtained, evolutionary game theory is used to model the competitive resource allocation, and replicator dynamics is used to capture the dynamic change of user selections. Based on the formulated evolutionary game, a distributed channel selection and backscatter power allocation algorithm is proposed. Simulations have been performed which validate the theoretical analysis. Also the effectiveness of the proposed scheme is verified.

关键词： Backscatter Resource management Stochastic processes Receivers Stochastic processes Radio frequency Energy harvesting Games distributed computing Cognitive ambient backscatter networks RF energy harvesting stochastic geometry evolutionary game

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A distributed Multimodel Platform to Cosimulate Multienergy Systems in Smart Buildings

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2021年第5期57卷 4428-4440页

作者： Schiera, Daniele Salvatore Barbierato, Luca Lanzini, Andrea Borchiellini, Romano Pons, Enrico Bompard, Ettore Patti, Edoardo Macii, Enrico Bottaccioli, Lorenzo Politecn Torino Energy Ctr Lab I-10129 Turin Italy Politecn Torino Dept Energy I-10129 Turin Italy Politecn Torino Dept Control & Comp Engn I-10129 Turin Italy Politecn Torino Interuniv Dept Reg & Urban Studies & Planning I-10129 Turin Italy

Nowadays, buildings are responsible for large consumption of energy in our cities. Moreover, buildings can be seen as the smallest entity of urban energy systems. On these premises, in this article, we present a flexible and distributed cosimulation platform that exploits a multimodeling approach to simulate and evaluate energy performance in smart buildings. The developed platform exploits the Mosaik cosimulation framework and implements the functional mock-up interface standard in order to couple and synchronize heterogeneous simulators and models. The platform combines in a shared simulation environment: 1) the thermal performance of the building simulated with EnergyPlus;2) a heat pump integrated with a proportional-integral-derivative control strategy modeled in Modelica to satisfy the heating demand of the building;3) an electrical energy storage system modeled in MATLAB Simulink;and 4) different Python models used to simulate household occupancy, electrical loads, photovoltaic production, and smart meters, respectively. The platform guarantees a plug-and-play integration of models and simulators, in which one or more models can be easily replaced without affecting the whole simulation engine. Finally, we present a demonstration example to test the functionalities, capability, and usability of the developed platform and discuss future developments of our framework.

关键词： Buildings Load modeling Analytical models Tools Resistance heating Usability Solid modeling Building energy system (BES) cosimulation cyber-physical multienergy system distributed computing electrical energy storage functional mock-up interface (FMI) heat pump Mosaik photovoltaics (PV) system simulation

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Nature-inspired optimization algorithms for different computing systems: novel perspective and systematic review

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第19期81卷 26779-26801页

作者： Kaul, Surabhi Kumar, Yogesh Ghosh, Uttam Alnumay, Waleed Chandigarh Univ Mohali 140413 Punjab India Chandigarh Grp Coll Mohali 140307 Punjab India Vanderbilt Univ Nashville TN 37235 USA King Saud Univ Riyadh 11451 Saudi Arabia

Nature inspired algorithm plays a very vibrant role in solving the different optimization problems these days. The fundamental attitude of naturalistic approaches is to boost the competence, improvement, proficiency, success in the task except from it to help in underrating the energy use, cost, size. Several computing techniques are taking the benefits from nature inspired algorithms for solving their problems related to load balancing, scheduling and many others. These algorithms have come up with lots of improvements in the results. The aim of this analysis is to make efforts in the betterment in different areas of computing and help in solving various problems related to load balancing, scheduling and energy efficiency. The structure of the paper includes an introduction, contribution to the work, background study, which includes the role of nature inspired techniques in a different computing environment, research challenges and its applications. The sustainable goal and objective of the article is to perform the energy efficiency, load balancing and scheduling on different computing systems which include grid, cloud, distributed, fog and edge computing by using various nature inspired algorithms. This comprehensive study gives the awareness and valuable provision for the researchers in this area by providing a thorough study of different computing techniques in different research fields.

关键词： Sustainability Energy efficiency Load balancing Scheduling Grid computing Cloud computing distributed computing Fog computing Edge computing

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KEEP: Secure and Efficient Communication for distributed IoT Devices

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IEEE INTERNET OF THINGS JOURNAL 2021年第16期8卷 12758-12770页

作者： Xi, Wei Duan, Meichen Bai, Xiuxiu Zhao, Kun Mo, Lufeng Zhao, Jizhong Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Zhejiang A&F Univ Coll Informat Engn Hangzhou 311300 Peoples R China

Security over mobile Internet-of-Things (IoT) devices is critical due to the open nature of distributed wireless communication. To efficiently establish a secure connection between two communication parties, a fast mobile key extraction protocol, KEEP, is proposed. KEEP fastly generates similar bit sequences from two communication parties' measurements of channel-state information (CSI) of different subcarriers. Then, a distributed "verification-recombination" mechanism is introduced to generate the same encryption key from bit sequences without the public-key authentication, digital signature, or key distribution center of the other party. We implemented real-world experiments using commercial off-the-shelf 802.11n devices to evaluate the performance of KEEP in various scenarios. Theoretical analysis and experimental verification show that KEEP is more secure, effective, and reliable than the state-of-the-art methods.

关键词： Internet of Things Privacy Wireless communication OFDM Protocols IEEE 802 11n Standard Correlation distributed computing Internet of Things (IoT) key generation

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DTransE: distributed Translating Embedding for Knowledge Graph

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2021年第10期32卷 2509-2523页

作者： Song, Dandan Zhang, Feng Lu, Meiyan Yang, Sicheng Huang, Heyan Beijing Inst Technol Sch Comp Sci & Technol Engn Res Ctr High Volume Language Informat Proc & Beijing 100081 Peoples R China Renmin Univ China Key Lab Data Engn & Knowledge Engn MOE Beijing 100872 Peoples R China Renmin Univ China Sch Informat Beijing 100872 Peoples R China

Knowledge graphs play an important role in many applications, such as link prediction and question answering. Translating embedding for knowledge graphs is done with the aim of encoding structured information on entities and their rich relations in a low-dimensional embedding space. TransE is one of the most important methods in translation-based models, and uses translation invariance to implement translating embedding for knowledge graphs. In this line of work, translating embedding models represent the relation as a translation from the head entity to the tail entity and have achieved impressive results. Currently, the TransE model is only developed on single-node machines. Unfortunately, the computing and storage capacities of a single machine can easily reach their limits as knowledge graphs become larger and more complex, which limits the application scope of TransE. In order to solve this problem, we propose a distributed TransE method, known as DTransE, which can utilize distributed computing resources to calculate knowledge graph embeddings. However, building a distributed TransE is complicated and involves challenges of knowledge graph partitioning and computation. To solve these challenges, we provide a high-quality edge partitioning algorithm for the power-law graph by considering the high-degree and low-degree vertices with adaptive weights, which can balance the workload. By using the unactivated Gather-Apply-Scatter model on TransE, the processes periodically exchange messages in a loop. The irregular data distribution among the processes is also optimized to further accelerate communication. As far as we know, this is the first work on a distributed TransE method. We use link prediction to evaluate the DTransE in a distributed environment. Experiments show that, compared to the original TransE method, our proposed DTransE is, on average, 24.5 times faster with a minimum loss of accuracy;compared to the state-of-the-art parallel TransE implementation,

关键词： Computational modeling Training Partitioning algorithms Mathematical model Parallel processing Memory management Prediction algorithms TransE distributed computing workload partitioning knowledge graph and representation

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