描述一种分享Http Live Streaming流媒体,同时对服务器和播放器透明的播放器中间件HLS-Share。HLS-Share通过手机节点间的协作计算,使手机共享3G网络带宽,减少3G网络流量,加速观看Http Live Streaming流。HLS-Share采用基于事件驱动的...描述一种分享Http Live Streaming流媒体,同时对服务器和播放器透明的播放器中间件HLS-Share。HLS-Share通过手机节点间的协作计算,使手机共享3G网络带宽,减少3G网络流量,加速观看Http Live Streaming流。HLS-Share采用基于事件驱动的方法在本地P2P网络中挑选部分节点作为中继节点负责分享从Internet获取的Http Live Streaming流。通过实验验证了相比于每部手机独立使用3G网络观看视频,HLS-Share在引入极小的CPU使用率的代价下,能够实现共享带宽、加快缓存速度的作用,并且消耗的电量小于前者。展开更多
With the new promising technique of mobile edge computing (MEC) emerging, by utilizing the edge computing and cloud computing capabilities to realize the HTTP adaptive video streaming transmission in MEC-based 5G netw...With the new promising technique of mobile edge computing (MEC) emerging, by utilizing the edge computing and cloud computing capabilities to realize the HTTP adaptive video streaming transmission in MEC-based 5G networks has been widely studied. Although many works have been done, most of the existing works focus on the issues of network resource utilization or the quality of experience (QoE) promotion, while the energy efficiency is largely ignored. In this paper, different from previous works, in order to realize the energy efficiency for video transmission in MEC-enhanced 5G networks, we propose a joint caching and transcoding schedule strategy for HTTP adaptive video streaming transmission by taking the caching and transcoding into consideration. We formulate the problem of energy-efficient joint caching and transcoding as an integer programming problem to minimize the system energy consumption. Due to solving the optimization problem brings huge computation complexity, therefore, to make the optimization problem tractable, a heuristic algorithm based on simulated annealing algorithm is proposed to iteratively reach the global optimum solution with a lower complexity and higher accuracy. Finally, numerical simulation results are illustrated to demonstrated that our proposed scheme brings an excellent performance.展开更多
Video streaming,especially hypertext transfer protocol based(HTTP)adaptive streaming(HAS)of video,has been expected to be a dominant application over mobile networks in the near future,which brings huge challenge for ...Video streaming,especially hypertext transfer protocol based(HTTP)adaptive streaming(HAS)of video,has been expected to be a dominant application over mobile networks in the near future,which brings huge challenge for the mobile networks.Although some works have been done for video streaming delivery in heterogeneous cellular networks,most of them focus on the video streaming scheduling or the caching strategy design.The problem of joint user association and rate allocation to maximize the system utility while satisfying the requirement of the quality of experience of users is largely ignored.In this paper,the problem of joint user association and rate allocation for HTTP adaptive streaming in heterogeneous cellular networks is studied,we model the optimization problem as a mixed integer programming problem.And to reduce the computational complexity,an optimal rate allocation using the Lagrangian dual method under the assumption of knowing user association for BSs is first solved.Then we use the many-to-one matching model to analyze the user association problem,and the joint user association and rate allocation based on the distributed greedy matching algorithm is proposed.Finally,extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.展开更多
本文介绍了基于Http Live Streaming的流媒体技术,包括系统架构、文件格式、数据结构和苹果提供的流媒体分割工具。结合应用场合提出了两种音视频采集方案,设计并搭建了一套基于Http Live Streaming的直播系统。分析了基于Http Live Str...本文介绍了基于Http Live Streaming的流媒体技术,包括系统架构、文件格式、数据结构和苹果提供的流媒体分割工具。结合应用场合提出了两种音视频采集方案,设计并搭建了一套基于Http Live Streaming的直播系统。分析了基于Http Live Streaming的直播系统的特点以及系统优化方向。展开更多
对于日益发展的移动互联网来说,流媒体是其最重要最有需求和市场的应用之一。本论文以Http Live Streaming技术为背景,详细介绍了Android平台架构和Android NDK开发,并在此基础上介绍并设计了移动流媒体直播系统,实现了无线网络视频的...对于日益发展的移动互联网来说,流媒体是其最重要最有需求和市场的应用之一。本论文以Http Live Streaming技术为背景,详细介绍了Android平台架构和Android NDK开发,并在此基础上介绍并设计了移动流媒体直播系统,实现了无线网络视频的传输。最后,通过性能测试,实现了客户端采集编码功能。展开更多
Streaming audio and video content currently accounts for the majority of the Internet traffic and is typically deployed over the top of the existing infrastructure. We are facing the challenge of a plethora of media p...Streaming audio and video content currently accounts for the majority of the Internet traffic and is typically deployed over the top of the existing infrastructure. We are facing the challenge of a plethora of media players and adaptation algorithms showing different behavior but lacking a common framework for both objective and subjective evaluation of such systems. This paper aims to close this gap by proposing such a framework, describing its architecture, providing an example evaluation, and discussing open issues.展开更多
No-wash bioassays based on nanoparticles are used widely in biochemical procedures because of their responsive sensing and no need forwashing processes.Essential for no-wash biosensing are the interactions between nan...No-wash bioassays based on nanoparticles are used widely in biochemical procedures because of their responsive sensing and no need forwashing processes.Essential for no-wash biosensing are the interactions between nanoparticles and biomolecules,but it is challenging toachieve controlled bioconjugation of molecules on nanomaterials.Reported here is a way to actively improve nanoparticle-based no-washbioassays by enhancing the binding between biomolecules and gold nanoparticles via acoustic streaming generated by a gigahertz piezoelectricnanoelectromechanical resonator.Tunable micro-vortices are generated at the device-liquid interface,thereby accelerating the internalcirculating flow of the solution,bypassing the diffusion limitation,and thus improving the binding between the biomolecules and goldnanoparticles.Combined with fluorescence quenching,an enhanced and ultrafast no-wash biosensing assay is realized for specific proteins.The sensing method presented here is a versatile tool for different types of biomolecule detection with high efficiency and simplicity.展开更多
With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Alth...With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.展开更多
The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits...In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits.Consumers now frequently rely on external sources to make well-informed purchasing decisions,leading to the emergence of live shopping as a prominent avenue for gathering product information and completing transactions.E-commerce live streaming has experienced rapid growth,leveraging its ability to generate traffic and capture consumer attention.The integration of content and live streaming not only meets users’psychological needs but also facilitates seamless communication between buyers and sellers.From the perspective of content marketing typologies,this paper examines content marketing across three key dimensions:informational content,entertainment content,and emotional content.It further explores the impact of content marketing on consumers’purchase intentions within the context of e-commerce live streaming.展开更多
With technological advancements,high-speed rail has emerged as a prevalent mode of transportation.During travel,passengers exhibit a growing demand for streaming media services.However,the high-speed mobile networks e...With technological advancements,high-speed rail has emerged as a prevalent mode of transportation.During travel,passengers exhibit a growing demand for streaming media services.However,the high-speed mobile networks environment poses challenges,including frequent base station handoffs,which significantly degrade wireless network transmission performance.Improving transmission efficiency in high-speed mobile networks and optimizing spatiotemporal wireless resource allocation to enhance passengers’media experiences are key research priorities.To address these issues,we propose an Adaptive Cross-Layer Optimization Transmission Method with Environment Awareness(ACOTM-EA)tailored for high-speed rail streaming media.Within this framework,we develop a channel quality prediction model utilizing Kalman filtering and an algorithm to identify packet loss causes.Additionally,we introduce a proactive base station handoffstrategy to minimize handoffrelated disruptions and optimize resource distribution across adjacent base stations.Moreover,this study presents a wireless resource allocation approach based on an enhanced genetic algorithm,coupled with an adaptive bitrate selection mechanism,to maximize passenger Quality of Experience(QoE).To evaluate the proposed method,we designed a simulation experiment and compared ACOTM-EA with established algorithms.Results indicate that ACOTM-EA improves throughput by 11%and enhances passengers’media experience by 5%.展开更多
Unmanned aerial vehicles(UAVs)bring more innovation and attraction to outdoor mobile high-definition(HD)live streaming with its unique perspective.Due to the heavy computational requirements of HD live broadcast tasks...Unmanned aerial vehicles(UAVs)bring more innovation and attraction to outdoor mobile high-definition(HD)live streaming with its unique perspective.Due to the heavy computational requirements of HD live broadcast tasks and the limited hardware performance of UAV equipment,how to reduce the system response delay and improve the energy efficiency of terminal equipment directly affects the secure broadcast of the system.Secure task offloading in this scenario is considered a promising solution and has received academic attention.In this paper,we simulate the UAV-aided outdoor mobile HD live streaming scenarios and optimize the relevant task offloading strategies.First,we design the total cost function of task offloading that jointly optimizes secure time latency and energy consumption.Additionally,we propose a collaborative computing model for multi-UAV task offloading.This model combines the idea of simulated annealing(SA)and introduces the compression factor to enhance the particle swarm optimization(PSO)to realize secure task offloading.The simulation results show that the proposed strategy has better performance in balancing network latency and energy consumption.Compared with the discrete teaching–learning-based optimization(DTLBO)and quantum PSO(QPSO)task offloading strategies,the fitness value of the proposed strategy is decreased by an average of 26.73%and 16.42%,respectively.展开更多
分布式拒绝服务(distributed denial of service,DDoS)攻击是重要的安全威胁,网络速度的不断提高给传统的检测方法带来了新的挑战。以Spark等为代表的大数据处理技术,给网络安全的高速检测带来了新的契机。提出了一种基于Spark Streamin...分布式拒绝服务(distributed denial of service,DDoS)攻击是重要的安全威胁,网络速度的不断提高给传统的检测方法带来了新的挑战。以Spark等为代表的大数据处理技术,给网络安全的高速检测带来了新的契机。提出了一种基于Spark Streaming框架的自适应实时DDoS检测防御技术,通过对滑动窗口内源簇进行分组,并根据与各分组内源簇比例的偏差统计,检测出DDoS攻击流量。通过感知合法的网络流量,实现了对DDoS攻击的自适应快速检测和有效响应。实验结果表明,该技术可极大地提升检测能力,为保障网络服务性能和安全检测的可扩展性提供了一种可行的解决方案。展开更多
文摘描述一种分享Http Live Streaming流媒体,同时对服务器和播放器透明的播放器中间件HLS-Share。HLS-Share通过手机节点间的协作计算,使手机共享3G网络带宽,减少3G网络流量,加速观看Http Live Streaming流。HLS-Share采用基于事件驱动的方法在本地P2P网络中挑选部分节点作为中继节点负责分享从Internet获取的Http Live Streaming流。通过实验验证了相比于每部手机独立使用3G网络观看视频,HLS-Share在引入极小的CPU使用率的代价下,能够实现共享带宽、加快缓存速度的作用,并且消耗的电量小于前者。
基金support by the Major National Science and Technology Projects (No. 2018ZX03001014-003)
文摘With the new promising technique of mobile edge computing (MEC) emerging, by utilizing the edge computing and cloud computing capabilities to realize the HTTP adaptive video streaming transmission in MEC-based 5G networks has been widely studied. Although many works have been done, most of the existing works focus on the issues of network resource utilization or the quality of experience (QoE) promotion, while the energy efficiency is largely ignored. In this paper, different from previous works, in order to realize the energy efficiency for video transmission in MEC-enhanced 5G networks, we propose a joint caching and transcoding schedule strategy for HTTP adaptive video streaming transmission by taking the caching and transcoding into consideration. We formulate the problem of energy-efficient joint caching and transcoding as an integer programming problem to minimize the system energy consumption. Due to solving the optimization problem brings huge computation complexity, therefore, to make the optimization problem tractable, a heuristic algorithm based on simulated annealing algorithm is proposed to iteratively reach the global optimum solution with a lower complexity and higher accuracy. Finally, numerical simulation results are illustrated to demonstrated that our proposed scheme brings an excellent performance.
基金fully supported under the National Natural Science Funds(Project Number:61501042 and 61302089)National High Technology Research and Development Program(863)of China(Project Number:2015AA016101 and 2015AA015702)BUPT Special Program for Youth Scientific Research Innovation(Grant No.2015RC10)
文摘Video streaming,especially hypertext transfer protocol based(HTTP)adaptive streaming(HAS)of video,has been expected to be a dominant application over mobile networks in the near future,which brings huge challenge for the mobile networks.Although some works have been done for video streaming delivery in heterogeneous cellular networks,most of them focus on the video streaming scheduling or the caching strategy design.The problem of joint user association and rate allocation to maximize the system utility while satisfying the requirement of the quality of experience of users is largely ignored.In this paper,the problem of joint user association and rate allocation for HTTP adaptive streaming in heterogeneous cellular networks is studied,we model the optimization problem as a mixed integer programming problem.And to reduce the computational complexity,an optimal rate allocation using the Lagrangian dual method under the assumption of knowing user association for BSs is first solved.Then we use the many-to-one matching model to analyze the user association problem,and the joint user association and rate allocation based on the distributed greedy matching algorithm is proposed.Finally,extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.
文摘本文介绍了基于Http Live Streaming的流媒体技术,包括系统架构、文件格式、数据结构和苹果提供的流媒体分割工具。结合应用场合提出了两种音视频采集方案,设计并搭建了一套基于Http Live Streaming的直播系统。分析了基于Http Live Streaming的直播系统的特点以及系统优化方向。
文摘对于日益发展的移动互联网来说,流媒体是其最重要最有需求和市场的应用之一。本论文以Http Live Streaming技术为背景,详细介绍了Android平台架构和Android NDK开发,并在此基础上介绍并设计了移动流媒体直播系统,实现了无线网络视频的传输。最后,通过性能测试,实现了客户端采集编码功能。
基金supported in part by the Austrian Research Promotion Agency(FFG)under the next generation video streaming project "PROMETHEUS"
文摘Streaming audio and video content currently accounts for the majority of the Internet traffic and is typically deployed over the top of the existing infrastructure. We are facing the challenge of a plethora of media players and adaptation algorithms showing different behavior but lacking a common framework for both objective and subjective evaluation of such systems. This paper aims to close this gap by proposing such a framework, describing its architecture, providing an example evaluation, and discussing open issues.
基金the financial support received from the National Natural Science Foundation of China(Grant No.62174119)the 111 Project (Grant No.B07014)the Foundation for Talent Scientists of Nanchang Institute for Microtechnology of Tianjin University
文摘No-wash bioassays based on nanoparticles are used widely in biochemical procedures because of their responsive sensing and no need forwashing processes.Essential for no-wash biosensing are the interactions between nanoparticles and biomolecules,but it is challenging toachieve controlled bioconjugation of molecules on nanomaterials.Reported here is a way to actively improve nanoparticle-based no-washbioassays by enhancing the binding between biomolecules and gold nanoparticles via acoustic streaming generated by a gigahertz piezoelectricnanoelectromechanical resonator.Tunable micro-vortices are generated at the device-liquid interface,thereby accelerating the internalcirculating flow of the solution,bypassing the diffusion limitation,and thus improving the binding between the biomolecules and goldnanoparticles.Combined with fluorescence quenching,an enhanced and ultrafast no-wash biosensing assay is realized for specific proteins.The sensing method presented here is a versatile tool for different types of biomolecule detection with high efficiency and simplicity.
基金funded by the Joint Project of Industry-University-Research of Jiangsu Province(Grant:BY20231146).
文摘With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
文摘In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits.Consumers now frequently rely on external sources to make well-informed purchasing decisions,leading to the emergence of live shopping as a prominent avenue for gathering product information and completing transactions.E-commerce live streaming has experienced rapid growth,leveraging its ability to generate traffic and capture consumer attention.The integration of content and live streaming not only meets users’psychological needs but also facilitates seamless communication between buyers and sellers.From the perspective of content marketing typologies,this paper examines content marketing across three key dimensions:informational content,entertainment content,and emotional content.It further explores the impact of content marketing on consumers’purchase intentions within the context of e-commerce live streaming.
基金substantially supported by the National Natural Science Foundation of China under Grant No.62002263in part by Tianjin Municipal Education Commission Research Program Project under 2022KJ012Tianjin Science and Technology Program Projects:24YDTPJC00630.
文摘With technological advancements,high-speed rail has emerged as a prevalent mode of transportation.During travel,passengers exhibit a growing demand for streaming media services.However,the high-speed mobile networks environment poses challenges,including frequent base station handoffs,which significantly degrade wireless network transmission performance.Improving transmission efficiency in high-speed mobile networks and optimizing spatiotemporal wireless resource allocation to enhance passengers’media experiences are key research priorities.To address these issues,we propose an Adaptive Cross-Layer Optimization Transmission Method with Environment Awareness(ACOTM-EA)tailored for high-speed rail streaming media.Within this framework,we develop a channel quality prediction model utilizing Kalman filtering and an algorithm to identify packet loss causes.Additionally,we introduce a proactive base station handoffstrategy to minimize handoffrelated disruptions and optimize resource distribution across adjacent base stations.Moreover,this study presents a wireless resource allocation approach based on an enhanced genetic algorithm,coupled with an adaptive bitrate selection mechanism,to maximize passenger Quality of Experience(QoE).To evaluate the proposed method,we designed a simulation experiment and compared ACOTM-EA with established algorithms.Results indicate that ACOTM-EA improves throughput by 11%and enhances passengers’media experience by 5%.
基金supported in part by the National Natural Science Foundation of China(Nos.62271454 and 62171119).
文摘Unmanned aerial vehicles(UAVs)bring more innovation and attraction to outdoor mobile high-definition(HD)live streaming with its unique perspective.Due to the heavy computational requirements of HD live broadcast tasks and the limited hardware performance of UAV equipment,how to reduce the system response delay and improve the energy efficiency of terminal equipment directly affects the secure broadcast of the system.Secure task offloading in this scenario is considered a promising solution and has received academic attention.In this paper,we simulate the UAV-aided outdoor mobile HD live streaming scenarios and optimize the relevant task offloading strategies.First,we design the total cost function of task offloading that jointly optimizes secure time latency and energy consumption.Additionally,we propose a collaborative computing model for multi-UAV task offloading.This model combines the idea of simulated annealing(SA)and introduces the compression factor to enhance the particle swarm optimization(PSO)to realize secure task offloading.The simulation results show that the proposed strategy has better performance in balancing network latency and energy consumption.Compared with the discrete teaching–learning-based optimization(DTLBO)and quantum PSO(QPSO)task offloading strategies,the fitness value of the proposed strategy is decreased by an average of 26.73%and 16.42%,respectively.
文摘分布式拒绝服务(distributed denial of service,DDoS)攻击是重要的安全威胁,网络速度的不断提高给传统的检测方法带来了新的挑战。以Spark等为代表的大数据处理技术,给网络安全的高速检测带来了新的契机。提出了一种基于Spark Streaming框架的自适应实时DDoS检测防御技术,通过对滑动窗口内源簇进行分组,并根据与各分组内源簇比例的偏差统计,检测出DDoS攻击流量。通过感知合法的网络流量,实现了对DDoS攻击的自适应快速检测和有效响应。实验结果表明,该技术可极大地提升检测能力,为保障网络服务性能和安全检测的可扩展性提供了一种可行的解决方案。
