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A Review on the Blockchain Technique Applied in Cloud Energy Storage Power System

A Review on the Blockchain Technique Applied in Cloud Energy...

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IEEE international conference on Cloud computing and Big Data Analysis (ICCCBDA)

作者： Jun-fang Li Xun Lu Donghui Zhang Jie Sheng Grid Planning & Research Center Guangdong Power Grid Corporation CSG Guangzhou China Guangdong Power Grid Corporation CSG Guangzhou China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China

ISBN: (纸本)9781665423113;9781665446327

Blockchain is a distributed information sharing database system integrated by the ordered blocks according to encryption algorithm and consensus mechanism. To promote intelligence and efficiency, this paper reviews the blockchain technique applied for modeling the framework and typical applications in the cloud energy storage power system. The concepts about the blockchain, cloud computing, virtual power plant and cloud energy storage (CES) in the power system are introduced firstly. This paper presents a framework design and its function modules for Guangdong power grid's future CES modules using blockchain technique based on identifying and discussing various important literature about the multi-energy systems integration, end-user demand response and distributed energy transaction.

关键词： Cloud computing Computational modeling Blockchain Carbon dioxide System integration Load management Power grids

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Temporal Knowledge Graph Reasoning via Time-distributed Representation Learning

Temporal Knowledge Graph Reasoning via Time-Distributed Repr...

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IEEE international conference on Data Mining (ICDM)

作者： Kangzheng Liu Feng Zhao Guandong Xu Xianzhi Wang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Temporal knowledge graph (TKG) reasoning has attracted significant attention. Recent approaches for modeling historical information have led to great advances. However, the problems of time variability and unseen entities have become two major obstacles preventing further development. The time variability problem means that different historical timestamps play different roles in the inference process. Furthermore, in the context of time variability, the unseen entity problem means that a query cannot obtain a predicted entity that is unseen in the scale-varying history rather than in a fixed set, thus turning from static to dynamic. In this paper, we propose a novel method named DHU-NET for addressing the time variability challenge and the dynamic unseen entity challenge derived from it. With regard to the former concern, we propose a time-distributed representation learning method based on a graph convolutional network(GCN) and a self-attention mechanism, which learns the distributed representations of facts at different historical timestamps and comprehensively pays different levels of attention to the different timestamps. With regard to the latter issue, we extract the unseen entities from a global static KG based on a copy mechanism and bring them into consideration during the final prediction step. Experiments on six benchmark datasets demonstrate the substantial improvements achieved by DHUNET in terms of multiple evaluation metrics. Our released codes are available at https://***/CGCL-codes/DHUNET.

关键词： Representation learning Measurement Knowledge engineering Predictive models Turning Cognition Data models

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Knowledge Graph-based Intent Network-Enhanced Web Services Recommendation

Knowledge Graph-based Intent Network-Enhanced Web Services R...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Lisheng Chen Buqing Cao Xiang Xie Yueying Qing Guosheng Kang Jianxun Liu School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

APIs recommendation for Mashup creation is becoming a hot topic in service computing. However, most existing APIs recommendation approaches fail to model the relationship between Mashup and API at a fine-grained level of intent, much less exploit relational dependencies to preserve the semantics of remote connections. Therefore, we propose an API service recommendation method based on the interactive learning intent of knowledge graphs for Mashup creation in this paper. This approach first creates a knowledge graph of APIs using natural language processing. Then, it uses the interaction learning intent model in the knowledge graph to model the intent of Mashups and APIs, obtain more independent Mashups and API intents by filtering intent independence and distance correlation, and recursively integrate them to gain intent-aware Mashup and API representations. Finally, the obtained Mashup and API representations are subjected to inner product operations to predict and generate API recommendation results. The experiments on real dataset collected from Programmable Web are performed, and the experimental results show that the recall of the proposed method have 11.35%, 15.72%, 21.33% and 9.36% improvement, the NDCG of the proposed method have 9.36%, 22.13%, 5.25% and 31.26% improvement, respectively, compared to Deep-FM, KGAT, CKE and R-GCN.

关键词： Knowledge engineering Correlation Filtering Mashups Computational modeling Semantics Service computing

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Stateful Serverless computing with Crucial

Stateful Serverless Computing with Crucial

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作者： Barcelona-Pons, Daniel Sutra, Pierre Sánchez-Artigas, Marc París, Gerard García-López, Pedro Universitat Rovira i Virgili Tarragona43007 Spain Télécom SudParis 19 Place Marguerite Perey PalaiseauF-91120 France IBM T.J. Watson Research Center Monitors: An operating system structuring concept 1101 Kitchawan Road Yorktown HeightsNY10598 United States

Serverless computing greatly simplifies the use of cloud resources. In particular, Function-As-A-Service (FaaS) platforms enable programmers to develop applications as individual functions that can run and scale independently. Unfortunately, applications that require fine-grained support for mutable state and synchronization, such as machine learning (ML) and scientific computing, are notoriously hard to build with this new paradigm. In this work, we aim at bridging this gap. We present Crucial, a system to program highly-parallel stateful serverless applications. Crucial retains the simplicity of serverless computing. It is built upon the key insight that FaaS resembles to concurrent programming at the scale of a datacenter. Accordingly, a distributed shared memory layer is the natural answer to the needs for fine-grained state management and synchronization. Crucial allows to port effortlessly a multi-Threaded code base to serverless, where it can benefit from the scalability and pay-per-use model of FaaS platforms. We validate Crucial with the help of micro-benchmarks and by considering various stateful applications. Beyond classical parallel tasks (e.g., a Monte Carlo simulation), these applications include representative ML algorithms such as k-means and logistic regression. Our evaluation shows that Crucial obtains superior or comparable performance to Apache Spark at similar cost (18%-40% faster). We also use Crucial to port (part of) a state-of-The-Art multi-Threaded ML library to serverless. The ported application is up to 30% faster than with a dedicated high-end server. Finally, we attest that Crucial can rival in performance with a single-machine, multi-Threaded implementation of a complex coordination problem. Overall, Crucial delivers all these benefits with less than 6% of changes in the code bases of the evaluated applications. © 2022 Copyright held by the owner/author(s).

关键词： Synchronization

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Validating SMT Solvers via Skeleton Enumeration Empowered by Historical Bug-Triggering Inputs

Validating SMT Solvers via Skeleton Enumeration Empowered by...

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international conference on Software Engineering (ICSE)

作者： Maolin Sun Yibiao Yang Ming Wen Yongcong Wang Yuming Zhou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Technology Huazhong University of Science and Technology China

SMT solvers check the satisfiability of logic formulas over first-order theories, which have been utilized in a rich number of critical applications, such as software verification, test case generation, and program synthesis. Bugs hidden in SMT solvers would severely mislead those applications and further cause severe consequences. Therefore, ensuring the reliability and robustness of SMT solvers is of critical importance. Although many approaches have been proposed to test SMT solvers, it is still a challenge to discover bugs effectively. To tackle such a challenge, we conduct an empirical study on the historical bug-triggering formulas in SMT solvers' bug tracking systems. We observe that the historical bug-triggering formulas contain valuable skeletons (i.e., core structures of formulas) as well as associated atomic formulas which can cast significant impacts on formulas' ability in triggering bugs. Therefore, we propose a novel approach that utilizes the skeletons extracted from the historical bug-triggering formulas and enumerates atomic formulas under the guidance of association rules derived from historical formulas. In this study, we realized our approach as a practical fuzzing tool HistFuzz and conducted extensive testing on the well-known SMT solvers Z3 and cvc5. To date, HistFuzz has found 111 confirmed new bugs for Z3 and cvc5, of which 108 have been fixed by the developers. More notably, out of the confirmed bugs, 23 are soundness bugs and invalid model bugs found in the solvers' default mode, which are essential for SMT solvers. In addition, our experiments also demonstrate that HistFuzz outperforms the state-of-the-art SMT solver fuzzers in terms of achieved code coverage and effectiveness.

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Deinsum: practically I/O optimal multi-linear algebra 22

Deinsum: practically I/O optimal multi-linear algebra

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Proceedings of the international conference on High Performance computing, Networking, Storage and Analysis

作者： Alexandros Nikolaos Ziogas Grzegorz Kwasniewski Tal Ben-Nun Timo Schneider Torsten Hoefler ETH Zurich Zurich Switzerland NextSilicon Tel Aviv Israel

Multilinear algebra kernel performance on modern massively-parallel systems is determined mainly by data movement. However, deriving data movement-optimal distributed schedules for programs with many high-dimensional inputs is a notoriously hard problem. State-of-the-art libraries rely on heuristics and often fall back to suboptimal tensor folding and BLAS calls. We present Deinsum, an automated framework for distributed multilinear algebra computations expressed in Einstein notation, based on rigorous mathematical tools to address this problem. Our framework automatically derives data movement-optimal tiling and generates corresponding distributed schedules, further optimizing the performance of local computations by increasing their arithmetic intensity. To show the benefits of our approach, we test it on two important tensor kernel classes: Matricized Tensor Times Khatri-Rao Products and Tensor Times Matrix chains. We show performance results and scaling on the Piz Daint supercomputer, with up to 19x speedup over state-of-the-art solutions on 512 nodes.

关键词： performance analysis hardware acceleration distributed computing tensors linear algebra automatic programming

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distributed Edge computing Resource Allocation Algorithm Based on DRL in LTE Access Network 4

Distributed Edge Computing Resource Allocation Algorithm Bas...

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4th international conference on Smart grid and Smart Cities (ICSGSC)

作者： Li, Huan Wang, Gang Liu, Yang Ren, Shuai Li, Tong Wang, Dongdong State Grid Liaoning Elect Power Supply Co LTD Elect Power Res Inst Shenyang Peoples R China State Grid Liaoning Elect Power Supply Co LTD Shenyang Peoples R China

ISBN: (纸本)9781728194042

With the advent of IoT, as an extension of the traditional cloud-centric computing model, the edge computing model solves the network channel congestion caused by the increasing number of IoT devices and satisfies theirs low latency requirements. Since the edge network needs to adopt heterogeneous structure to meet multiple access requirements during construction, how to properly allocate limited edge resources to process computing requests from mobile edge terminals becomes a challenge under heterogeneous networks. In recent years, reinforcement learning shine in various types of decision-making problems, it has brought new ideas to solve complex decision problems. In this paper, we use reinforcement learning to solve the resource allocation problem of heterogeneous edge server (ES Edge Server) collaborative computing. In this model, we consider the multitasking and the heterogeneity and mobility of edge devices, abstracting the problem into Markov Decision Processes (MDPs) based on the real-time state of the network and the attributes of the task, using based on Actor Critic and Policy Gradient's DDPG (Deep Deterministic Policy Gradient) make resource allocation optimization decisions. The simulation results show that compared with the DQN-based algorithm, the DDPG-based algorithm has better simulation results in the experiment.

关键词： cloud computing edge computing IoT resource allocatiot

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Improving the Applicability of Visual Peer-to-Peer Navigation with Crowdsourcing 26

Improving the Applicability of Visual Peer-to-Peer Navigatio...

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26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Dong, Erqun Liang, Jianzhe Wang, Zeyu Xu, Jingao Shangguan, Longfei Ma, Qiang Yang, Zheng Tsinghua Univ Sch Software Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China Microsoft Cloud&AI Seattle WA USA

ISBN: (纸本)9781728190747

Visual peer-to-peer navigation is a suitable solution for indoor navigation for it relieves the labor of site-survey and eliminates infrastructure dependence. However, a major drawback hampers its application, as the peer-to-peer mode suffers from a deficiency of paths in large indoor scenarios with multifarious places-of-interest. Nevertheless, we propose one with a profound crowdsourcing scheme that addresses the drawback by merging the paths of different leaders' into a global map. To realize the idea, we further deal with entailed challenges, namely the unidirectional disadvantage, the scale ambiguity, and large computational overhead. We design a navigation strategy to solve the unidirectional problem and turn to VIO to tackle scale ambiguity. We devise a mobile-edge architecture to enable real-time navigation (30fps, 100ms end -to-end delay) and lighten the burden of smartphones (35% battery life for 2h35min) while assuring the accuracy of localization and map construction. Through experimental validations, we show that P2P navigation, previously relying on the abundance of independent paths, can enjoy a sufficiency of navigation paths with a crowdsourced global map. The experiments demonstrate a navigation success rate of 100% and spatial offset of less than 3.2m, better than existing works.

关键词： Crowdsourcing Edge computing Indoor navigation Visual inertial odometry

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Cross-chain Jamming Attack with Light Client Verification Clash in IBC Protocol

Cross-chain Jamming Attack with Light Client Verification Cl...

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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： Yaling Zhang Ziyan Wang Yichuan Wang Xiaoxue Liu Xinhong Hei School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

Inter-Blockchain Communication (IBC) in Cosmos is a Sidechains/Relays cross-chain protocol. During the implementation of the protocol, the blockchain uses relayers to establish connections, and uses light clients to track and verify the consensus state of counterparties. However, light clients lack effective malicious behavior detection mechanisms, which easily induces the effective attacks to cross-chain communication. This paper proposes a light client jamming attack in IBC cross-chain protocol. Attackers force the victim’s light client to be in the verification state frequently by continuously generating headers containing a large number of verifier signatures. By interfering with light clients, attackers can reduce the victim’s cross-chain transaction throughput with low cost and increase the probability of cross chain transaction failure. Based on the Cosmos blockchain, this paper tests the attack, models the attack process and analyzes its security. The experiment shows that when the header constructed by the attacker contains $5.0\times 10^{4}$ signatures, the probability of successful attack can reach 90%. Finally, this paper briefly introduces some feasible defense strategies against this attack.

关键词： Analytical models Protocols Costs Force Formal languages Throughput Ubiquitous computing

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Adaptive Load Balancing based on Machine Learning for Iterative parallel Applications 28

Adaptive Load Balancing based on Machine Learning for Iterat...

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28th Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Oikawa, Anna Victoria C. R. Freitas, Vinicius Castro, Marcio Pilla, Laercio L. Fed Univ Santa Catarina UFSC Florianopolis SC Brazil Univ Paris Saclay CNRS LRI Orsay France

ISBN: (纸本)9781728165820

The performance of irregular scientific applications can be easily affected by an uneven distribution of work among the computing resources. In this context, Load Balancing (LB) stands as one of the most important solutions to improve resource utilization. However, choosing the best-performing load balancing algorithm for a given application is not a trivial task. For instance, manually and statically choosing an LB algorithm does not work in situations where applications have a dynamic or unknown behavior. In this context, we propose a Machine Learning-based Adaptive Load Balancer (ADAPTIVELB) to automate the load balancing algorithm decision at run time. This approach monitors and collects information about the application dynamically, and according to the analyzed data, it makes a decision of invoking the most suitable LB algorithm. Our experiments show that ADAPTIVELB can select a good load balancing algorithm in most of the cases, leading to performance improvements over statically chosen LB algorithms and over the absence of a load balancer.

关键词： dynamic load balancing machine learning performance

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