检索结果-内蒙古大学图书馆

CloudSim 7G: An Integrated Toolkit for Modeling and Simulation of Future Generation Cloud computing Environments

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Andreoli, Remo Zhao, Jie Cucinotta, Tommaso Buyya, Rajkumar CLOUDS Lab School of Computing and Information Systems University of Melbourne Melbourne Australia ReTiS Lab TECIP Institute Sant’Anna School of Advanced Studies Pisa Italy

Cloud computing has established itself as an efficient and cost-effective paradigm for the execution of web-based applications, and scientific workloads, that need elasticity and on-demand scalability capabilities. However, the evaluation of novel resource provisioning and management techniques is a major challenge due to the complexity of large-scale data centers. Therefore, Cloud simulators are an essential tool for academic and industrial researchers, to investigate the effectiveness of novel algorithms and mechanisms in large-scale scenarios. This paper proposes CloudSim 7G, the seventh generation of CloudSim, which features a re-engineered and generalized internal architecture to facilitate the integration of multiple CloudSim extensions within the same simulated environment. As part of the new design, we introduced a set of standardized interfaces to abstract common functionalities and carried out extensive refactoring and refinement of the codebase. The result is a substantial reduction in lines of code with no loss in functionality, significant improvements in run-time performance and memory efficiency (up to 25% less heap memory allocated), as well as increased flexibility, ease-of-use, and extensibility of the framework. These improvements benefit not only CloudSim developers but also researchers and practitioners using the framework for modeling and simulating next-generation Cloud computing environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Cloud platforms

CurvDrop: A Ricci Curvature Based Approach to Prevent Graph Neural Networks from Over-Smoothing and Over-Squashing 23

学校读者我要写书评

暂无评论

CurvDrop: A Ricci Curvature Based Approach to Prevent Graph ...

2023 World Wide Web Conference, WWW 2023

作者： Liu, Yang Zhou, Chuan Pan, Shirui Wu, Jia Li, Zhao Chen, Hongyang Zhang, Peng Academy of Mathematics and Systems Science Chinese Academy of Sciences China Amss Chinese Academy of Science School of Cyber Security University of Chinese Academy of Science China School of Information and Communication Technology Griffith University Australia School of Computing Macquarie University Australia Hangzhou link2do Technology China Research Center for Graph Computing Zhejiang Lab China Cyberspace Institute of Advanced Technology Guangzhou University China

ISBN: (纸本)9781450394161

Graph neural networks (GNNs) are powerful models to handle graph data and can achieve state-of-the-art in many critical tasks including node classification and link prediction. However, existing graph neural networks still face both challenges of over-smoothing and over-squashing based on previous literature. To this end, we propose a new Curvature-based topology-aware Dropout sampling technique named CurvDrop, in which we integrate the Discrete Ricci Curvature into graph neural networks to enable more expressive graph models. Also, this work can improve graph neural networks by quantifying connections in graphs and using structural information such as community structures in graphs. As a result, our method can tackle the both challenges of over-smoothing and over-squashing with theoretical justification. Also, numerous experiments on public datasets show the effectiveness and robustness of our proposed method. The code and data are released in https://***/liu-yang-maker/Curvature-based-Dropout. © 2023 ACM.

关键词： Graph neural networks

DRPC: Distributed Reinforcement Learning Approach for Scalable Resource Provisioning in Container-based Clusters

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Bai, Haoyu Xu, Minxian Ye, Kejiang Buyya, Rajkumar Xu, Chengzhong The School of Computing and Information Systems The University of Melbourne Melbourne Australia Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China State Key Lab of IOTSC University of Macau China

Microservices have transformed monolithic applications into lightweight, self-contained, and isolated application components, establishing themselves as a dominant paradigm for application development and deployment in public clouds such as Google and Alibaba. Autoscaling emerges as an efficient strategy for managing resources allocated to microservices’ replicas. However, the dynamic and intricate dependencies within microservice chains present challenges to the effective management of scaled microservices. Additionally, the centralized autoscaling approach can encounter scalability issues, especially in the management of large-scale microservice-based clusters. To address these challenges and enhance scalability, we propose an innovative distributed resource provisioning approach for microservices based on the Twin Delayed Deep Deterministic Policy Gradient algorithm. This approach enables effective autoscaling decisions and decentralizes responsibilities from a central node to distributed nodes. Comparative results with state-of-the-art approaches, obtained from a realistic testbed and traces, indicate that our approach reduces the average response time by 15% and the number of failed requests by 24%, validating improved scalability as the number of requests increases. © 2024, CC BY.

关键词： Scalability

ChainsFormer: A Chain Latency-aware Resource Provisioning Approach for Microservices Cluster

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Song, Chenghao Xu, Minxian Ye, Kejiang Wu, Huaming Gill, Sukhpal Singh Buyya, Rajkumar Xu, Chengzhong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Tianjin University Tianjin China Queen Mary University of London London United Kingdom Lab School of Computing and Information Systems The University of Melbourne Australia State Key Lab of IoTSC University of Macau China

The trend towards transitioning from monolithic applications to microservices has been widely embraced in modern distributed systems and applications. This shift has resulted in the creation of lightweight, fine-grained, and self-contained microservices. Multiple microservices can be linked together via calls and inter-dependencies to form complex functions. One of the challenges in managing microservices is provisioning the optimal amount of resources for microservices in the chain to ensure application performance while improving resource usage efficiency. This paper presents ChainsFormer, a framework that analyzes microservice inter-dependencies to identify critical chains and nodes, and provision resources based on reinforcement learning. To analyze chains, ChainsFormer utilizes light-weight machine learning techniques to address the dynamic nature of microservice chains and workloads. For resource provisioning, a reinforcement learning approach is used that combines vertical and horizontal scaling to determine the amount of allocated resources and the number of replicates. We evaluate the effectiveness of ChainsFormer using realistic applications and traces on a real testbed based on Kubernetes. Our experimental results demonstrate that ChainsFormer can reduce response time by up to 26% and improve processed requests per second by 8% compared with state-of-the-art techniques. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

Field Trial Evaluation of Sensor-Based Aquaculture Automation for Improved Biofloc Shrimp Culture

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Sasikumar, R. Lincy, Lourdu L. Saranya, S. Roja, B. Thamanna, L. Sreekutty, V.P. Dhayanithi, S. Sathyan, Anish Chellapandi, P. Industrial Systems Biology Lab Department of Bioinformatics School of Life Sciences Bharathidasan University Tamil Nadu Tiruchirappalli620024 India Anish Sathyan Centre for Advancement of Advanced Computing Ministry of Electronics & Information Technology Government of India Kerala Thiruvananthapuram India

In this study, we investigated the application of a sensor-based automation system for biofloc shrimp farming and compared it with a manual practice. An automated controller system was developed to control the water quality parameters and aeration to improve the efficiency, precision, and sustainability of shrimp aquaculture. Environmental sensors tracked the status of the solenoid valves and aerators and adjusted their operation based on factors such as water conditions and shrimp growth requirements. A 60-day trial in a 12, 000 L pond showed that automated trial enhanced shrimp growth, feed efficiency, protein efficiency, bacterial population, and nutritional composition. Significant improvements were observed in the specific growth rate and the feed conversion ratio in automation pond. It also showed better survival rates of shrimp than in the control trial. Ammonia, nitrite, and nitrate levels in the water decreased in the automation pond during the grow-out period. Microbial community analysis of shrimp biofloc samples identified three dominant bacterial phyla: Firmicutes, Proteobacteria, and Actinobacteria. Among the 23 bacterial genera detected, Acinetobacter, Tissierella, and Pseudomonas were the most prevalent. The pH and dissolved oxygen sensors were found to be particularly important for the effective operation of shrimp biofloc systems. However, the sensor signals can be interrupted by a highly viscous environment. This field trial study concluded that the automated shrimp biofloc system significantly enhanced the efficiency and productivity of shrimp farming compared to manual practice. © 2023, The Authors. All rights reserved.

关键词： Controllers

CL4KGE: A Curriculum Learning Method for Knowledge Graph Embedding

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Liu, Yang Zhou, Chuan Zhang, Peng Cao, Yanan Liu, Yongchao Li, Zhao Chen, Hongyang Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Institute of Information Engineering Chinese Academy of Sciences Beijing China Ant Group Hangzhou China Research Center for Graph Computing Zhejiang Lab Hangzhou China

Knowledge graph embedding (KGE) constitutes a foundational task, directed towards learning representations for entities and relations within knowledge graphs (KGs), with the objective of crafting representations comprehensive enough to approximate the logical and symbolic interconnections among entities. In this paper, we define a metric Z-counts to measure the difficulty of training each triple () in KGs with theoretical analysis. Based on this metric, we propose CL4KGE, an efficient Curriculum Learning based training strategy for KGE. This method includes a difficulty measurer and a training scheduler that aids in the training of KGE models. Our approach possesses the flexibility to act as a plugin within a wide range of KGE models, with the added advantage of adaptability to the majority of KGs in existence. The proposed method has been evaluated on popular KGE models, and the results demonstrate that it enhances the state-of-the-art methods. The use of Z-counts as a metric has enabled the identification of challenging triples in KGs, which helps in devising effective training strategies. © 2024, CC BY.

关键词： Knowledge graph

Meta Structure Search for Link Weight Prediction in Heterogeneous Graphs

学校读者我要写书评

暂无评论

Meta Structure Search for Link Weight Prediction in Heteroge...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaoou Zhang Yang Gao Yang Liu Yujia Zhu Peng Zhang Chuan Zhou Qingyun Liu Hongyang Chen Chinese Academy of Sciences Institute of Information Engineering School of Cyber Security University of Chinese Academy of Sciences College of Computer Science and Technology Zhejiang University Chinese Academy of Sciences Academy of Mathematics and Systems Science Cyberspace Institute of Advanced Technology Guangzhou University Zhejiang Lab Research Center for Graph Computing

Recently link weight prediction has attracted an increasing research interest due to its merits in quantifying the strength between nodes within a graph. Nonetheless, current link weight prediction methods focus solely on graph topology, disregarding node feature information embedded in graphs. In real-world applications, we often collect heterogeneous graph data where multiple types of nodes linked by multiple types of edges are available for analysis, and it is essential and challenging to quantify the proximity of different types of nodes. To solve this challenge, we present a new model for Heterogeneous Graph Link Weight Prediction (HLWP for short). In HLWP, message passing in heterogeneous graph neural networks is described as a meta structure, which can be effectively designed by Differentiable Neural Architecture Search (DARTS) algorithms. Thus, HLWP can enhance the message passing in heterogeneous graphs by DARTS. In addition, HLWP employs a perturbation-based algorithm to enhance stability and precision. Through empirical experiments conducted on three real-world datasets, we demonstrate that HLWP achieves accurate predictions of link weights. Our results highlight the superiority of HLWP over existing methods for link weight prediction and baseline GNN models in terms of accurately predicting link weights within heterogeneous graphs.

关键词：

A Reinforcement Learning Based Approach to Identify Resource Bottlenecks for Multiple Services Interactions in Cloud computing Environments 16th

学校读者我要写书评

暂无评论

A Reinforcement Learning Based Approach to Identify Resource...

16th EAI International Conference on Collaborative computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Xu, Lingxiao Xu, Minxian Semmes, Richard Li, Hui Mu, Hong Gui, Shuangquan Tian, Wenhong Wu, Kui Buyya, Rajkumar School of Software and Information Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Co. Ltd. Chengdu China Department of Computer Science University of Victoria Victoria Canada CLOUDS Lab School of Computing and Information Systems University of Melbourne Melbourne Australia

ISBN: (纸本)9783030675394

Cloud service providers are provisioning resources including a variety of virtual machine instances to support customers that migrate their services to the cloud. From the customers’ perspective, selecting the appropriate amount of resources is tightly coupled with performance and cost. By identifying the potential resource bottlenecks in the early stage of the service deployment process, resource planning can be significantly optimized. However, due to the unpredictable workloads and heterogeneous resources, it is difficult to identify resource bottlenecks that can degrade system performance. To support system non-functional requirements (NFR) in a better manner, we propose a reinforcement learning based approach to support the NFR management of system concerning the multiple services interactions scenario by identifying the potential resource bottleneck and optimizing the demanded resources. The proposed approach can predict the resource bottleneck for multiple services interactions, e.g. bottleneck in CPU or overloads in specific service, and provide guidance for resource planning. We modeled and simulated the proposed approach using an extended version of the CloudSim toolkit. Comprehensive evaluations with realistic use case from Siemens Digital Industries Software’s MindSphere Solution on AliCloud show that our proposed approach can achieve high accuracy in terms of performance metrics, such as response time, queries per second (QPS), and resource usage. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Reinforcement learning

Hybrid malware detection approach with feedback-directed machine learning

学校读者我要写书评

暂无评论

Science China(information Sciences) 2020年第3期63卷 240-242页

作者： Zhetao LI Wenlin LI Fuyuan LIN Yi SUN Min YANG Yuan ZHANG Zhibo WANG Hunan Provincial Key Lab of Internet of Things and Information Security Xiangtan University The College of Information Engineering Xiangtan University Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science Fudan University Shanghai Institute of Intelligent Electronics & Systems Shanghai Institute for Advanced Communication and Data Science School of Cyber Science and Engineering Wuhan University

Dear editor,In recent years, the number of mobile malware has increased at an alarming rate. According to a report from G DATA [1], there are approximately 9000 new Android malware instances each *** malicious applica... 详细信息

关键词： Hybrid malware detection approach with feedback-directed machine learning