检索结果-内蒙古大学图书馆

A Dynamic Load Balancing Method of cloud-Center Based on SDN

China Communications 2016年第2期13卷 130-137页

作者： WANG Yong TAO Xiaoling HE Qian KUANG Yuwen Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology School of Information and Communication Guilin University of Electronic Technology

In order to balancing based on data achieve dynamic load flow level, in this paper, we apply SDN technology to the cloud data center, and propose a dynamic load balancing method of cloud center based on SDN. The approach of using the SDN technology in the current task scheduling flexibility, accomplish real-time monitoring of the service node flow and load condition by the OpenFlow protocol. When the load of system is imbalanced, the controller can allocate globally network resources. What＇s more, by using dynamic correction, the load of the system is not obvious tilt in the long run. The results of simulation show that this approach can realize and ensure that the load will not tilt over a long period of time, and improve the system throughput.

关键词： SDN cloud computing data center dynamic load balancing

来源：评论

学校读者我要写书评

暂无评论

computing Power in the Sky: Digital Twin-assisted Collaborative computing with Multi-UAV Networks

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Chao Han, Yu Zhang, Long Jia, Ziye Zhang, Hongliang Hong, Choong Seon Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing. Due to the frequent interactions among edge servers and the increased complexities for large-scale task executions, we propose a digital twin (DT)-driven multi-UAV CPN framework, where the aerial DT network is created as a digital replica of the multi-UAV network to assist collaborative computing. A joint optimization problem of the service association, offloading power control, task partitions, CP allocation, and UAV trajectory design is formulated to minimize the long-term average delay of UEs. We use the Lyapunov technique to tackle the long-term energy constraint, forming a tractable optimization problem, which is modeled as a Markov decision process to balance the UAV's deficit queue stability and the UE's task completion delay. A hybrid soft actor-critic based deep reinforcement learning algorithm is developed to optimize the joint design by sampling the hybrid action from the Gumbel and Gaussian distributions. Simulation results verify that the multi-UAV CPN contributes to significant performance gains when supported by DTs. © 2025 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Verify All Traffic: Towards Zero-Trust In-Network Intrusion Detection against Multipath Routing

引用

IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2155-2171页

作者： Zhao, Ziming Li, Zhaoxuan Xie, Xiaofei Liu, Zhipeng Li, Tingting Yu, Jiongchi Zhang, Fan Chen, Binbin School of Software Technology Zhejiang University Ningbo315100 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Scientific and Technological Innovation Center 311200 China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province 310027 China Jiaxing Research Institute Zhejiang University 314000 China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou450001 China Singapore State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security UCAS Beijing100049 China School of Computing and Information Systems Singapore Management University Singapore188065 Singapore Advanced Digital Sciences Center Singapore138632 Singapore Singapore University of Technology and Design SingaporeB96049 Singapore

With the popularity of encryption protocols, machine learning (ML)-based traffic analysis technologies have attracted widespread attention. To adapt to modern high-speed bandwidth, recent research is dedicated to advancing zero-trust intrusion detection by offloading feature extraction and model inference into the network dataplane. Especially, with the rise of programmable switches, achieving line-speed ML inference becomes promising. However, existing research only considers a single switch node as a relay to conduct evaluation. This is far from real-world deployments involving multiple switches (given that zero-trust security assumes that threats can originate from anywhere, including within the network), particularly the multi-path routing phenomenon that exists in practice. In this paper, we reveal practical challenges in the context of enabling line-speed model inference in the network dataplane. Furthermore, we propose FCPlane, the forwarding and computing integrated dataplane for zero-trust intrusion detection that aims to enable efficient load balancing while providing reliable traffic analysis results, even against multipath routing. The core idea is to reconcile forwarding and computation to the flowlet level, for which a tailor-made Markov chain model is designed. Based on two public traffic datasets, we evaluate seven state-of-the-art in-network traffic analysis models deployed in four types of topologies (three with multipath routing and one without) to explore performance impact and demonstrate the effectiveness of our proposal. © 1983-2012 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

A Data-Driven Frequency Scaling Approach for Deadline-aware Energy Efficient Scheduling on Graphics Processing Units (GPUs)

A Data-Driven Frequency Scaling Approach for Deadline-aware ...

引用

IEEE/ACM International Symposium on Cluster computing and the Grid (CCGRID)

作者： Shashikant Ilager Rajeev Muralidhar Kotagiri Rammohanrao Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

Modern computing paradigms, such as cloud computing, are increasingly adopting GPUs to boost their computing capabilities primarily due to the heterogeneous nature of AI/ML/deep learning workloads. However, the energy consumption of GPUs is a critical problem. Dynamic Voltage Frequency Scaling (DVFS) is a widely used technique to reduce the dynamic power of GPUs. Yet, configuring the optimal clock frequency for essential performance requirements is a non-trivial task due to the complex nonlinear relationship between the application's runtime performance characteristics, energy, and execution time. It becomes more challenging when different applications behave distinctively with similar clock settings. Simple analytical solutions and standard GPU frequency scaling heuristics fail to capture these intricacies and scale the frequencies appropriately. In this regard, we propose a data-driven frequency scaling technique by predicting the power and execution time of a given application over different clock settings. We collect the data from application profiling and train the models to predict the outcome accurately. The proposed solution is generic and can be easily extended to different kinds of workloads and GPU architectures. Furthermore, using this frequency scaling by prediction models, we present a deadline-aware application scheduling algorithm to reduce energy consumption while simultaneously meeting their deadlines. We conduct real extensive experiments on NVIDIA GPUs using several benchmark applications. The experiment results have shown that our prediction models have high accuracy with the average RMSE values of 0.38 and 0.05 for energy and time prediction, respectively. Also, the scheduling algorithm consumes 15.07% less energy as compared to the baseline policies.

关键词： Graphics processing units Predictive models Time-frequency analysis Clocks Data models cloud computing

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous Task Co-location in Containerized cloud computing Environments

Heterogeneous Task Co-location in Containerized Cloud Comput...

引用

International Symposium on Object-Oriented Real-Time distributed computing

作者： Zhiheng Zhong Jiabo He Maria A. Rodriguez Sarah Erfani Ramamohanarao Kotagiri Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9781728169583

ISBN: (纸本)9781728169590

Although cloud computing became a mainstream industrial computing paradigm, low resource utilization remains a common problem that most warehouse-scale datacenters suffer from. This leads to a significant waste of hardware resources, infrastructure investment, and energy consumption. As the diversity in application workloads grows into an essential characteristic in modern datacenters, task co-location of different workloads to the same compute cluster has gained immense popularity as a heuristic solution for resource utilization optimization. Although the existing co-location methodologies manage to improve resource efficiency to a certain degree, application QoS is usually sacrificed as a trade-off when dealing with resource interference between different applications. This paper proposes a containerized task co-location (CTCL) scheduler to improve resource utilization and minimize task eviction rate. Our CTCL scheduler (1) applies an elastic task co-location strategy to improve resource utilization; and (2) supports a dynamic task rescheduling mechanism to prevent severe QoS degradation from frequent task evictions. We evaluate our approach in terms of resource efficiency and rescheduling cost through the ContainercloudSim simulator. Our experiments with the Alibaba 2018 workload traces demonstrate that CTCL could improve overall resource efficiency and reduce rescheduling rate by 38% and 99% respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Input-Based Ensemble-Learning Method for Dynamic Memory Configuration of Serverless computing Functions

Input-Based Ensemble-Learning Method for Dynamic Memory Conf...

引用

IEEE International Conference on Utility and cloud computing (UCC)

作者： Siddharth Agarwal Maria A. Rodriguez Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

In today’s Function-as-a-Service offerings, a programmer is usually responsible for configuring function memory for its successful execution, which allocates proportional function resources such as CPU and network. However, right-sizing the function memory force developers to speculate performance and make ad-hoc configuration decisions. Recent research has highlighted that a function’s input characteristics, such as input size, type and number of inputs, significantly impact its resource demand, run-time performance and costs with fluctuating workloads. This correlation further makes memory configuration a non-trivial task. On that account, an input-aware function memory allocator not only improves developer productivity by completely hiding resource-related decisions but also drives an opportunity to reduce resource wastage and offer a finer-grained cost-optimised pricing scheme. Therefore, we present MemFigLess, a serverless solution that estimates the memory requirement of a serverless function with input-awareness. The framework executes function profiling in an offline stage and trains a multi-output Random Forest Regression model on the collected metrics to invoke input-aware optimal configurations. We evaluate our work with the state-of-the-art approaches on AWS Lambda service to find that MemFigLess is able to capture the input-aware resource relationships and allocate upto 82% less resources and save up to 87% run-time costs.

关键词： Productivity Measurement Costs Correlation Memory management Force Serverless computing Pricing Random forests Optimization

来源：评论

学校读者我要写书评

暂无评论

cloudSimSC: A Toolkit for Modeling and Simulation of Serverless computing Environments

CloudSimSC: A Toolkit for Modeling and Simulation of Serverl...

引用

IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Anupama Mampage Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia

Serverless computing is gaining traction as an at-tractive model for the deployment of a multitude of work-loads in the cloud. Designing and building effective resource management solutions for any computing environment requires extensive long term testing, experimentation and analysis of the achieved performance metrics. Utilizing real test beds and serverless platforms for such experimentation work is often times not possible due to resource, time and cost constraints. Thus, employing simulators to model these environments is key to overcoming the challenge of examining the viability of such novel ideas for resource management. Existing simulation software developed for serverless environments lack generalizibility in terms of their architecture as well as the various aspects of re-source management, where most are purely focused on modeling function performance under a specific platform architecture. In contrast, we have developed a serverless simulation model with induced flexibility in its architecture as well as the key resource management aspects of function scheduling and scaling. Further, we incorporate techniques for easily deriving monitoring metrics required for evaluating any implemented solutions by users. Our work is presented as cloudSimSC, a modular extension to cloudSim which is a simulator tool extensively used for modeling cloud environments by the research community. We discuss the implemented features in our simulation tool using multiple use cases.

关键词：

来源：评论

学校读者我要写书评

暂无评论

iAnomaly: A Toolkit for Generating Performance Anomaly Datasets in Edge-cloud Integrated computing Environments

iAnomaly: A Toolkit for Generating Performance Anomaly Datas...

引用

IEEE International Conference on Utility and cloud computing (UCC)

作者： Duneesha Fernando Maria A. Rodriguez Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

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 in edge computing environments is limited primarily due to the absence of publicly available edge performance anomaly datasets or due to the lack of accessibility of real edge setups to generate necessary data. To address this gap, we propose iAnomaly: a full-system emulator equipped with open-source tools and fully automated dataset generation capabilities to generate labeled normal and anomaly data based on user-defined configurations. We also release a performance anomaly dataset generated using iAnomaly, which captures performance data for several microservice-based IoT applications with heterogeneous QoS and resource requirements while introducing a variety of anomalies. This dataset effectively represents the characteristics found in real edge environments, and the anomalous data in the dataset adheres to the required standards of a high-quality performance anomaly dataset.

关键词： cloud computing Image edge detection Microservice architectures Quality of service Computer architecture Internet of Things Anomaly detection Standards Edge computing Service level agreements

来源：评论

学校读者我要写书评

暂无评论

ECSNeT++: A simulator for distributed stream processing on edge and cloud environments

引用

Future Generation Computer systems 2020年 111卷 401-418页

作者： Amarasinghe, Gayashan de Assunção, Marcos D. Harwood, Aaron Karunasekera, Shanika School of Computing and Information Systems The University of Melbourne Australia Inria Avalon LIP Laboratory ENS Lyon University of Lyon France

The objective of Internet of Things (IoT) is ubiquitous computing. As a result many computing enabled, connected devices are deployed in various environments, where these devices keep generating unbounded event streams related to the deployed environment. The common paradigm is to process these event streams at the cloud using the available distributed Stream Processing (DSP) frameworks. However, with the emergence of Edge computing, another convenient computing paradigm has been presented for executing such applications. Edge computing introduces the concept of using the underutilised potential of a large number of computing enabled connected devices such as IoT, located outside the cloud. In order to develop optimal strategies to utilise this vast number of potential resources, a realistic test bed is required. However, due to the overwhelming scale and heterogeneity of the edge computing device deployments, the amount of effort and investment required to set up such an environment is high. Therefore, a realistic simulation environment that can accurately predict the behaviour and performance of a large-scale, real deployment is extremely valuable. While the state-of-the-art simulation tools consider different aspects of executing applications on edge or cloud computing environments, we found that no simulator considers all the key characteristics to perform a realistic simulation of the execution of DSP applications on edge and cloud computing environments. To the best of our knowledge, the publicly available simulators lack being verified against real world experimental measurements, i.e. for calibration and to obtain accurate estimates of e.g. latency and power consumption. In this paper, we present our ECSNeT++ simulation toolkit which has been verified using real world experimental measurements for executing DSP applications on edge and cloud computing environments. ECSNeT++ models deployment and processing of DSP applications on edge-cloud environments and i

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Efficient Priority Packet Recovery Based on Network Coding in DVB-IPDC System

引用

China Communications 2015年第7期12卷 167-178页

作者： WANG Lian PENG Daiyuan LIANG Hongbin Provincial Key Laboratory of Information Coding and Transmission School of Information Science and TechnologySouthwest Jiaotong University College of Computing Science and Technology Chongqing University of Posts and Telecommunications School of Transportation and Logistics Southwest Jiaotong University Secure Networking and Computing Institute School of Computing Informatics and Decision Systems EngineeringArizona State University

In DVB-IPDC system, due to the constraints of handheld devices and the broadcast nature of wireless network, packet loss is inevitable. ECDR-NC proposed is a retransmission encoding packet selection algorithm based on the dynamic information updating, which can find the current most effective complete decoding packet. ECDR-NC can not only avoid the redundant encoding packets due to the overlapping among encoding packets, but also reduce the computational complexity compared with the traditional encoding schemes. Furthermore, the retransmission upper bound of ECDR-NC is fully controlled. In time-sensitive applications, to maximize the aggregate number of recovery packets while minimizing the total number of discarded packets due to the time limit according to the priority preference, the adaptive priority scheme EPNC is formulized, and the weighted relation graph is constructed to find the maximum-weighted encoding packets sequence according to the decoding gains. In the same network environment, the performances comparisons between PNC and EPNC show that EPNC is more efficient and more rational, and the average discarded packets ratios ofEPNC can be reduced about 18%~27%. The main contributions of this paper are an effective retransmission encoding packet selection algorithm ECDR-NC proposed, and a new adaptive priority recovery scheme EPNC introduced into DVB-IPDC system.

关键词： digital video broadcasting（DVB） retransmission network coding（NC） longterm evolution（LTE） wireless network

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：