检索结果-内蒙古大学图书馆

Efficient Training Approaches for Performance Anomaly Detection Models in Edge computing Environments

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Fernando, Duneesha Rodriguez, Maria A. Arroba, Patricia Ismail, Leila Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne Parkville Australia The CCS Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain Lab Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates

Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL's model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

Energy and thermal-aware resource management of cloud data centres: A taxonomy and future directions

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Ilager, Shashikant Buyya, Rajkumar Laboratory School of Computing and Information Systems Parkville Campus MelbourneVIC3010 Australia

This paper investigates the existing resource management approaches in cloud Data Centres for energy and thermal efficiency. It identifies the need for integrated computing and cooling systems management and learning-based solutions in resource management systems. A taxonomy on energy and thermal efficient resource management in data centres is proposed based on an in-depth analysis of the literature. Furthermore, a detailed survey on existing approaches is conducted according to the taxonomy and recent advancements including machine learning-based resource management approaches and cooling management technologies are discussed. Finally, key future research directions for sustainable growth of cloud computing services are proposed. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural resources management

A taxonomy of live migration management in cloud computing

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Tianzhang, H.E. Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne MelbourneVIC3000 Australia

cloud Data Centers have become the backbone infrastructure to provide services. With the emerging edge computing paradigm, computation and networking capabilities have been pushed from clouds to the edge to provide computation, intelligence, networking management with low end-to-end latency. Service migration across different computing nodes in edge and cloud computing becomes essential to guarantee the quality of service in the dynamic environment. Many studies have been conducted on the dynamic resource management involving migrating Virtual Machines to achieve various objectives, such as load balancing, consolidation, performance, energy-saving, and disaster recovery. Some have investigated to improve and predict the performance of single live migration of VM and container. Recently, several studies service migration in the edge-centric computing paradigms. However, there is a lack of surveys to focus on the live migration management in edge and cloud computing environments. We examine the characteristics of each field and conduct a migration management-centric taxonomy and survey. We also identify the gap and research opportunities to guarantee the performance of resource management with live migrations. Copyright © 2021, The Authors. All rights reserved.

关键词： Resource allocation

Architecture and Key Technologies of Parallel Dispatching System for Railway Technical Operation Stations

学校读者我要写书评

暂无评论

Architecture and Key Technologies of Parallel Dispatching Sy...

2023 International Annual Conference on Complex systems and Intelligent Science, CSIS-IAC 2023

作者： Xiong, Gang Yang, Donghu Xu, Wei Li, Runmei Chen, Shichao Song, Bing Dong, Xisong Zhu, Fenghua Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Signal and Communication Research Institute China Academy of Railway Science Corporation Limited Beijing100081 China The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100081 China Beijing Huairou Academy of Parallel Sensing Beijing101400 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China

ISBN: (纸本)9798350309003

A railroad technical operation station is an extensive and complex operation system and a variety of random disturbing factors further increase the complexity of its command and dispatching. To enhance the railroad technical operation stations' intelligence, and integration level, and enhance its dispatching management ability of complex operation plans, based on the ACP approach, this paper designs the parallel dispatching system (PDS) architecture, and describes its key technologies including the artificial dispatching system (ADS), computing experiments, parallel execution, etc. On the basis of the artificial dispatching system, computational experiments can optimize various types of technical operations and the overall operational process, to achieve parallel execution between the actual dispatching system and ADS. PDS can provide more efficient and intelligent command and dispatching solutions for railroad technical work stations, and promote the development of the railroad transportation industry in the direction of higher quality and efficiency. © 2023 IEEE.

关键词： Railroads

iFogSim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Mahmud, Redowan Pallewatta, Samodha Goudarzi, Mohammad Buyya, Rajkumar The School of Computing Technologies STEM College RMIT University Melbourne Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Internet of Things (IoT) has already proven to be the building block for next-generation Cyber-Physical systems (CPSs). The considerable amount of data generated by the IoT devices needs latency-sensitive processing, which is not feasible by deploying the respective applications in remote cloud datacentres. Edge/Fog computing, a promising extension of cloud at the IoT-proximate network, can meet such requirements for smart CPSs. However, the structural and operational differences of Edge/Fog infrastructure resist employing cloud-based service regulations directly to these environments. As a result, many research works have been recently conducted, focusing on efficient application and resource management in Edge/Fog computing environments. Scalable Edge/Fog infrastructure is a must to validate these policies, which is also challenging to accommodate in the real-world due to high cost and implementation time. Considering simulation as a key to this constraint, various software has been developed that can imitate the physical behaviour of Edge/Fog computing environments. Nevertheless, the existing simulators often fail to support advanced service management features because of their monolithic architecture, lack of actual dataset, and limited scope for a periodic update. To overcome these issues, we have developed multiple simulation models for service migration, dynamic distributed cluster formation, and microservice orchestration for Edge/Fog computing in this work and integrated with the existing iFogSim simulation toolkit for launching it as iFogSim2. The performance of iFogSim2 and its built-in policies are evaluated using three use case scenarios and compared with the contemporary simulators and benchmark policies under different settings. Results indicate that the proposed solution outperform others in service management time, network usage, ram consumption, and simulation time. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Jiang, Qize Qin, Minhao Shi, Shengmin Sun, Weiwei Zheng, Baihua School of Computer Science Fudan University China Shanghai Key Laboratory of Data Science Fudan University China Shanghai Institute of Intelligent Electronics & Systems China School of Computing and Information Systems Singapore Management University Singapore

How to coordinate the communication among intersections effectively in real complex traffic scenarios with multi-intersection is challenging. Existing approaches only enable the communication in a heuristic manner without considering the content/importance of information to be shared. In this paper, we propose a universal communication form UniComm between intersections. UniComm embeds massive observations collected at one agent into crucial predictions of their impact on its neighbors, which improves the communication efficiency and is universal across existing methods. We also propose a concise network UniLight to make full use of communications enabled by UniComm. Experimental results on real datasets demonstrate that UniComm universally improves the performance of existing state-of-the-art methods, and UniLight significantly outperforms existing methods on a wide range of traffic situations. Source codes are available at https://***/zyr17/UniLight. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

New Dynamic Bayesian Network for Time-to-Event Prediction

学校读者我要写书评

暂无评论

New Dynamic Bayesian Network for Time-to-Event Prediction

ACS/IEEE International Conference on Computer systems and Applications

作者： Walid Ben Abdallah Hasna Njah Salma Jamoussi National Engineering School of Gabes Gabes University Tunisia Multimedia InfoRmation systems and Advanced Computing Laboratory Sfax University Tunisia Higher institute of Computer sciences and multimedia of Gabes Gabes University Tunisia Higher institute of Computer sciences and multimedia of Sfax Sfax university Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Time-to-event prediction is an important analytical approach in medical research and personalized medicine that aims to predict the timing of clinically relevant occurrences and find associated risk variables. In the context of chronic diseases such as multiple sclerosis (MS), precisely modelling time to disease progression, adverse events, or therapeutic responses can have a significant impact on patient management strategies and clinical results. This study investigate the use of Bayesian networks(BN), augmented with a temporal factor, for time-to-event prediction in predicting disease progression in MS patients. We suggest using dynamic Bayesian networks (DBNs) as a versatile temporal modeling framework to capture the complex dynamics and inter-dependencies found in time-to-event processes.

关键词： Multiple sclerosis Accuracy Precision medicine Probabilistic logic Bayes methods Timing Medical diagnostic imaging Context modeling

Geographic and Temporal Deep Learning Method for Traffic Flow Prediction in Highway Network 17th

学校读者我要写书评

暂无评论

Geographic and Temporal Deep Learning Method for Traffic F...

17th EAI International Conference on Collaborative computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Zhang, Tianpu Ding, Weilong Xing, Mengda Chen, Jun Du, Yongkang Liang, Ying School of Information Science and Technology North China University of Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing100144 China Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Science Beijing China

ISBN: (纸本)9783030926373

Traffic congestion has become an inevitable situation faced by all countries and the prediction accuracy of traffic flow, as one of the means to solve this problem, still needs to be improved. Most studies lack the consideration of the influence of multiple factors such as spatial factors, time series factors and other external factors, which makes the prediction effect of traffic flow unsatisfactory. In this paper a method is proposed based on deep learning that can capture the geographic spatial relationship among toll stations, the dynamic temporal relationship of historical traffic flow, extreme weather and calendar types. On the three metrics of MAPE, MAE, and RMSE, the prediction effect of our model has increased by 30% compared with KNN, GBRT and LSTM models. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Traffic congestion

Generalization Boosted Adapter for Open-Vocabulary Segmentation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Xu, Wenhao Wang, Changwei Feng, Xuxiang Xu, Rongtao Huang, Longzhao Zhang, Zherui Guo, Li Xu, Shibiao School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250013 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China The Aerospace Information Research Institute Chinese Academy of Sciences China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Vision-language models (VLMs) have demonstrated remarkable open-vocabulary object recognition capabilities, motivating their adaptation for dense prediction tasks like segmentation. However, directly applying VLMs to such tasks remains challenging due to their lack of pixel-level granularity and the limited data available for fine-tuning, leading to overfitting and poor generalization. To address these limitations, we propose Generalization Boosted Adapter (GBA), a novel adapter strategy that enhances the generalization and robustness of VLMs for open-vocabulary segmentation. GBA comprises two core components: (1) a Style Diversification Adapter (SDA) that decouples features into amplitude and phase components, operating solely on the amplitude to enrich the feature space representation while preserving semantic consistency;and (2) a Correlation Constraint Adapter (CCA) that employs cross-attention to establish tighter semantic associations between text categories and target regions, suppressing irrelevant low-frequency "noise" information and avoiding erroneous associations. Through the synergistic effect of the shallow SDA and the deep CCA, GBA effectively alleviates overfitting issues and enhances the semantic relevance of feature representations. As a simple, efficient, and plug-and-play component, GBA can be flexibly integrated into various CLIP-based methods, demonstrating broad applicability and achieving state-of-the-art performance on multiple open-vocabulary segmentation benchmarks. © 2024, CC BY.

关键词： Semantics