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Online public opinion time series prediction based on improved N-Beat and multimodal hybrid fusion 22

Online public opinion time series prediction based on improv...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Tian, Yuling Miao, Shengfa Yao, Shaowen Wang, Puming Jin, Xin Ma, Shichao Engineering Research Center of Cyberspace Yunnan University School of Software Yunnan University Kunming China

ISBN: (纸本)9798331509712

Social media offers a promising way to analyze online public opinion, which has drawn extensive attention from various sectors. In academia, most studies focus on predicting public opinion using unimodal time series methods, paying little attention to multimodal approaches. However, public opinion may be affected by various complex social factors, so it is necessary to explore multimodal elements. Based on the N-Beats model, we propose a novel model, HFN-BeatsConv, which employs a powerful modal alignment strategy, 3D-TCN. Most fuses multimodal data for public opinion prediction. The model employs a component, 3D-TCN, for modal alignment, differs from other research in that it focuses on the time at which the text appears. Subsequently, the model, HFN-BeatsConv, the N-Beats model is enhanced through the utilization of 3D-TCN, which enables the processing of multivariate time series data and the reduction of multimodal time series forecast error. To increase the usability and sustainability of research, this study provides a valuable social media dataset, as a supplementary feature of time series prediction. Through extensive experiments, the proposed method outperforms the existing methods. © 2024 IEEE.

关键词： Prediction models

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ATL: A Link Failure Recovery Method with Fast Recovery Speed, Low Interruption Rate, and Small TCAM Consumption in SDN 29

ATL: A Link Failure Recovery Method with Fast Recovery Speed...

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29th IEEE international Conference on parallel and distributed systems, ICPADS 2023

作者： Zhang, Xinyu Chen, Jue Shanghai University of Engineering Science School of Electronic and Electric Engineering Shanghai China

ISBN: (纸本)9798350330717

Based on the architecture of the software Defined Network (SDN), we argue that SDN together with Internet of Things (IoT) applications can enhance the control and management of IoT in terms of flexibility and intelligence. However, with the rapid development of SDN, link fault tolerance has become a crucial challenge in ensuring network reliability and stability. In this field, the current solutions mainly consist of proactive and reactive schemes, but there are still limitations and challenges. The proactive solutions often waste significant TCAM resources, while the reactive solutions face longer fault recovery time. In order to solve the shortcoming of both solutions, and to address the issues of traffic interruption caused by insufficient link bandwidth after link failures, this paper proposes a method called Adapt To Link (ATL) which combines the greedy algorithm with Pro-VLAN technology (a proactive method which calculates a backup path and assigns a VLAN ID for each link before link failures), aiming to achieve efficient fault-tolerant recovery for link failures in the SDN data plane. In this algorithm, we utilize the Pro-VLAN method and pre-compute multiple backup paths for each link before a failure occurs. When a link failure occurs, we can swiftly switch the affected traffic to the backup paths automatically without the controller, ensuring continuous flow of network traffic. Additionally, we introduce a greedy algorithm to satisfy the requirement of elephant flows, thereby improving network resource utilization and performance. By combining these techniques and methods, our algorithm exhibits the characteristics of low interruption rate, minimal resource consumption, and high efficiency. The experimental results demonstrate that under high load conditions, the ATL scheme reduces the interruption rate by 72.4% compared to the proactive approach and by 54.9% compared to the reactive approach, respectively. The number of additional flow rules in ATL is only

关键词： Fault Tolerance Heuristic Algorithm Proactive Approach Reactive Approach software Defined Network

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LWSA: A Learning-Based Workflow Scheduling Algorithm for Energy-Efficient UAV Delivery System 29

LWSA: A Learning-Based Workflow Scheduling Algorithm for Ene...

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29th IEEE international Conference on parallel and distributed systems, ICPADS 2023

作者： Wang, Jiali Ye, Yutong Wang, Ting Chen, Mingsong East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350330717

Due to their fast speed and easy deployment, Unmanned Aerial Vehicles (UAVs) have been widely used across various sectors, such as earthquake rescue, medical assistance, and smart agriculture. However, UAVs in delivery networks face significant challenges due to limited battery life and computational capabilities, particularly for tasks that entail intensive computing workflows. In this context, Multi-access Edge Computing (MEC), which provides computing resources in close proximity to mobile terminal devices, has emerged as a promising solution. UAVs can offload computing tasks to MEC resources across diverse Internet of Things (IoT) environments. Although task offloading can enhance their task processing capability, it simultaneously brings additional costs, encompassing data transmission time and energy consumption. To address these issues, this paper proposes a novel workflow scheduling method based on the Proximal Policy Optimization (PPO) algorithm, aimed at optimizing UAV energy consumption within MEC environments. The proposed approach establishes a learning-based workflow scheduling strategy harnessing the adaptability of the PPO algorithm to manage dynamic and intricate scenarios, which facilitates efficient task allocation to optimal computational resources while accounting for flight time constraints. Extensive experiments conducted on various well-known scientific workflow benchmarks in real-world UAV delivery networks validate the effectiveness of our method. Compared with state-of-the-art methods, our approach significantly reduces UAV energy consumption and task completion time, simultaneously increasing UAV's effective payload capacity. © 2023 IEEE.

关键词： Energy Efficiency Multi-Access Edge Computing Reinforcement Learning Workflow Scheduling

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DNN Inference Task Offloading Based on distributed Soft Actor-Critic in Mobile Edge Computing 35

DNN Inference Task Offloading Based on Distributed Soft Acto...

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35th international Conference on software engineering and Knowledge engineering, SEKE 2023

作者： Xu, Wenxiu Chen, Ningjiang Tu, Huan College of Computer and Electronic Information Guangxi University Nanning China

In mobile edge computing, DNN-driven intelligent inference service is highly sensitive to latency. Recently, collaborative inference between user devices and Edge Servers (ESs) based on DNN partition has been used in service acceleration. However, due to the limited computing resources of ESs, there is resource competition between concurrent requests, resulting in the partition tasks cannot be offloaded to ESs in time. Therefore, it is necessary to design an efficient offloading scheme for partition-based concurrent inference tasks. Existing task offloading schemes based on Deep Reinforcement Learning (DRL) can solve complex decision-making problems in high-dimensional state space, but there are problems such as insufficient sample diversity and easily falling into local optimum. Therefore, we propose a collaborative DNN inference task offloading scheme based on distributed Soft Actor-Critic(SAC). It supports SAC Agents to explore samples in parallel and share learning experiences, and improves the randomness of the policy through the maximum entropy mechanism to avoid falling into local optimum, thus achieving efficient offloading of concurrent partition tasks. Experimental results on DNN benchmarks show that compared with the baseline schemes, the average service latency of our scheme is reduced by more than 18.3%, and it has a higher convergence speed and task success rate, which can make ESs achieve load balancing. © 2023 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Mobile edge computing

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TPTO: A Transformer-PPO based Task Offloading Solution for Edge Computing Environments 29

TPTO: A Transformer-PPO based Task Offloading Solution for E...

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29th IEEE international Conference on parallel and distributed systems, ICPADS 2023

作者： Gholipour, Niloofar De Assuncao, Marcos Dias Agarwal, Pranav Gascon-Samson, Julien Buyya, Rajkumar Univ. of Quebec Dept. of Software Engineering and IT École de Technologie Supérieure Montreal Canada The Univ. of Melbourne CLOUDS Lab School of Computing and Information Systems Australia

ISBN: (纸本)9798350330717

Emerging applications in healthcare, autonomous vehicles, and wearable assistance require interactive and low-latency data analysis services. Unfortunately, cloud-centric architectures cannot fulfill the low-latency demands of these applications, as user devices are often distant from cloud data centers. Edge computing aims to reduce the latency by enabling processing tasks to be offloaded to resources located at the network's edge. However, determining which tasks must be offloaded to edge servers to reduce the latency of application requests is not trivial, especially if the tasks present dependencies. This paper proposes a Deep Reinforcement Learning (DRL) approach called TPTO, which leverages Transformer Networks and Proximal Policy Optimization (PPO) to offload dependent tasks of IoT applications in edge computing. We consider users with various preferences, where devices can offload computation to an edge server via wireless channels. Performance evaluation results demonstrate that under fat application graphs, TPTO is more effective than state-of-the-art methods, such as Greedy, HEFT, and MRLCO, by reducing latency by 30.24%, 29.61%, and 12.41%, respectively. In addition, TPTO presents a training time approximately 2.5 times faster than an existing DRL approach. © 2023 IEEE.

关键词： Edge computing reinforcement learning task offloading Transformers

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Making Uintah Performance Portable for Department of Energy Exascale Testbeds

Making Uintah Performance Portable for Department of Energy ...

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29th international Conference on parallel and distributed Computing (Euro-Par)

作者： Hohnen, John K. Garcia, Marta Bagusetty, Abhishek Sanderson, Allen Berzins, Martin Oak Ridge Natl Lab Oak Ridge TN 37830 USA Argonne Natl Lab Lemont IL USA Univ Utah Salt Lake City UT USA

ISBN: (纸本)9783031488023;9783031488030

To help ease ports to forthcoming Department of Energy (DOE) exascale systems, testbeds have been made available to select users. These testbeds are helpful for preparing codes to run on the same hardware and similar software as in their respective exascale systems. This paper describes how the Uintah Computational Framework, an open-source asynchronous many-task (AMT) runtime system, has been modified to be performance portable across the DOE Crusher, DOE Polaris, and DOE Sunspot testbeds in preparation for portable simulations across the exascale DOE Frontier and DOE Aurora systems. The Crusher, Polaris, and Sunspot testbeds feature the AMD MI250X, NVIDIA A100, and Intel PVC GPUs, respectively. This performance portability has been made possible by extending Uintah's intermediate portability layer [18] to additionally support the Kokkos::HIP, Kokkos::OpenMPTarget, and Kokkos::SYCL back-ends. This paper also describes notable updates to Uintah's support for Kokkos, which were required to make this extension possible. Results are shown for a challenging radiative heat transfer calculation, central to the University of Utah's predictive boiler simulations. These results demonstrate singlesource portability across AMD-, NVIDIA-, and Intel-based GPUs using various Kokkos back-ends.

关键词： Asynchronous Many-Task Runtime System Performance Portability parallelism and Concurrency Portability software engineering

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AdvBinSD: Poisoning the Binary Code Similarity Detector via Isolated Instruction Sequences 21

AdvBinSD: Poisoning the Binary Code Similarity Detector via ...

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21st IEEE international symposium on parallel and distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud Computing, 16th IEEE international Conference on Social Computing and Networking and 13th international Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Yi, Xiaoyu Li, Gaolei Ding, Ao Zheng, Yan Li, Yuqing Li, Jianhua Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China China University of Political Science and Law School of Information Management for Law Beijing China Wuhan University School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350329223

Binary code similarity detection (BinSD) systems trend to utilize deep learning to identify semantic features of assembly code and exhibits superior performance, gaining increasing popularity against traditional methods. However, it has been observed that existing deep learning models are susceptible to data poisoning attacks, posing a latent threat to the robustness and reliability of BinSD. Existing data poisoning strategies in BinSD are easily detectable for the generated triggers will destroy code functions. Moreover, selecting trigger injection location needs repeated exploration and verification, increasing the attack cost. To address this issue, we propose a novel adversarial scheme, named as AdvBinSD, which can poison the deep learning-based binary code similarity detector and make it sensitive to isolated instruction sequences. In AdvBinSD, the isolated instruction sequences generally refer to those instructions that have no data dependencies with other instructions and do not affect the function of original binary code, and also it is difficult to discovery those isolated instruction sequences by verifying syntactic validity and semantic integrity. Different from existing data poisoning strategies, AdvBinSD first estimates a code fragment that has the greatest impact on software functionality as the poisoning location, and then add isolated instruction sequences into this location to synthesize effective poisoned samples. This location estimation is achieved by maximizing the similarity between function-level feature vectors and instruction-level feature vectors, ensuring that the modified assembly code can execute correctly. Furthermore, to improve the efficiency of feature vector similarity computing process, a k-order greedy feature comparison (k-GFC) algorithm is also designated. Extensive experiments demonstrate that our proposed AdvBinSD can successfully poison the state-of-the-art deep learning-based binary code similarity detectors. © 2023 IEEE.

关键词： Binary codes

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The MIT Supercloud Workload Classification Challenge 36

The MIT Supercloud Workload Classification Challenge

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Tang, Benny J. Chen, Qiqi Weiss, Matthew L. Frey, Nathan C. McDonald, Joseph Bestor, David Yee, Charles Arcand, William Bergeron, William Byun, Chansup Edelman, Daniel Houle, Michael Hubbell, Matthew Jones, Michael Kepner, Jeremy Klein, Anna Michaleas, Adam Michaleas, Peter Milechin, Lauren Mullen, Julia Prout, Andrew Reuther, Albert Rosa, Antonio Bowne, Andrew McEvoy, Lindsey Li, Baolin Tiwari, Devesh Gadepally, Jiay Samsi, Siddharth MIT 77 Massachusetts Ave Cambridge MA 02139 USA MIT Lincoln Lab 244 Wood St Lexington MA 02173 USA Northeastern Univ Boston MA 02115 USA US Air Force Cambridge MA USA

ISBN: (纸本)9781665497473

High-Performance Computing (HPC) centers and cloud providers support an increasingly diverse set of applications on heterogenous hardware. As Artificial Intelligence (AI) and Machine Learning (ML) workloads have become an increasingly larger share of the compute workloads, new approaches to optimized resource usage, allocation, and deployment of new AI frameworks are needed. By identifying compute workloads and their utilization characteristics, HPC systems may be able to better match available resources with the application demand. By leveraging datacenter instrumentation, it may be possible to develop AI-based approaches that can identify workloads and provide feedback to researchers and datacenter operators for improving operational efficiency. To enable this research, we released the MIT Supercloud Dataset, which provides detailed monitoring logs from the MIT Supercloud cluster. This dataset includes CPU and GPU usage by jobs, memory usage, and file system logs. In this paper, we present a workload classification challenge based on this dataset. We introduce a labelled dataset that can be used to develop new approaches to workload classification and present initial results based on existing approaches. The goal of this challenge is to foster algorithmic innovations in the analysis of compute workloads that can achieve higher accuracy than existing methods. Data and code will be made publicly available via the Datacenter Challenge website : https://***.

关键词： Technological innovation distributed processing File systems Instruments High performance computing Graphics processing units Machine learning

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A Physics-Informed Integrated Modeling Method for Lithium-ion Batteries 21

A Physics-Informed Integrated Modeling Method for Lithium-io...

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作者： Fan, Yunsheng Huang, Zhiwu Guan, Kaifu Shu, Boyu Liu, Yongjie Zhu, Zeyu He, Peinan Li, Shuo Central South University School of Automation Changsha China Central South University School of Computer Science and Engineering Changsha China Changsha University of Science and Technology School of Electronic Information and Electrical Engineering Changsha China

ISBN: (纸本)9798350329223

Battery models play a crucial role in battery management systems, describing the inner workings of batteries. However, offline models cannot adapt to the battery parameter degradation. To address this issue, a physics-informed integrated battery model combines equivalent electrical circuits and a neural network regression model is proposed. Electrical circuits capture essential battery electricity behaviors while a regression model with the recurrent neural network is employed to simulate the nonlinear open circuit voltage (OCV). The regression model is trained using unlabeled learning and the parameters of the electrical circuit are identified online. Finally, the proposed battery model is validated with a cubature Kalman filter. Extensive experiments confirmed that the proposed modeling method outperforms other methods with Less than 1.66% RMSE in SOC estimation. © 2023 IEEE.

关键词： Kalman filters

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AIO: Automating I/O Optimization Pipeline for Data-Intensive Applications in HPC

AIO: Automating I/O Optimization Pipeline for Data-Intensive...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Wanxin Wang Huijun Wu Lihua Yang Zhangyu Liu Bin Zheng School of Computer and Communication Engineering Changsha University of Science and Technology College of Computer Science and Technology National University of Defense Technology

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

High-performance computing (HPC) systems has entered the exascale era, but I/O performance has lagged behind due to storage hardware limitations, creating a "storage wall effect" that hinders HPC systems full potential. Modern HPC storage systems employ a layered storage architecture with local node storage, burst buffers, and global parallel file systems to improve I/O performance. However, application workloads vary, and fast storage layers may not always outperform global parallel file systems, while a static storage software stack configuration may not achieve optimal performance across different applications. Traditional I/O optimization is time-consuming, laborintensive, and prone to human error. To address this issue, this paper proposes AIO, a data-driven I/O optimization method. AIO automatically selects the optimal storage hierarchy and software stack based on the task's I/O patterns, and further enhances performance by identifying and applying the most efficient configuration settings. We evaluated AIO with eight applications in a cluster that features shared burst buffers, local burst buffers, and a global shared file system. The experiments demonstrate that AIO effectively optimizes I/O performance, achieving an overall speedup of 2.74.

关键词： distributed processing File systems High performance computing Pipelines Full stack Optimization methods Computer architecture Hardware

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