The scope of the 5G network is not only limited to the enhancements in the form of the quality of service(QoS),but it also includes a wide range of services with various requirements.Besides this,many approaches and p...The scope of the 5G network is not only limited to the enhancements in the form of the quality of service(QoS),but it also includes a wide range of services with various requirements.Besides this,many approaches and platforms are under the umbrella of 5G to achieve the goals of endto-end service provisioning.However,the management of multiple services over heterogeneous platforms is a complex task.Each platform and service have various requirements to be handled by domain experts.Still,if the next-generation network management is dependent on manual updates,it will become impossible to provide seamless service provisioning in runtime.Since the traffic for a particular type of service varies significantly over time,automatic provisioning of resources and orchestration in runtime need to be integrated.Besides,with the increase in the number of devices,amount,and variety of traffic,the management of resources with optimization becomes a challenging task.To this end,this manuscript provides a solution that automates the management and service provisioning through multiple platforms while assuring various aspects,including automation,resource management and service assurance.The solution consists of an intent-based system that automaticallymanages different orchestrators,and eliminates manual control by abstracting the complex configuration requirements into simple and generic contracts.The proposed systemconsiders handling the scalability of resources in runtime by usingMachine Learning(ML)to automate and optimize service resource utilization.展开更多
Data is growing quickly due to a significant increase in social media applications.Today,billions of people use an enormous amount of data to access the Internet.The backbone network experiences a substantial load as ...Data is growing quickly due to a significant increase in social media applications.Today,billions of people use an enormous amount of data to access the Internet.The backbone network experiences a substantial load as a result of an increase in users.Users in the same region or company frequently ask for similar material,especially on social media platforms.The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user.Applications that require relatively low latency can use Content Delivery Network(CDN)technology to meet their requirements.An edge and the data center con-stitute the CDN architecture.To fulfill requests from the edge and minimize the impact on the network,the requested content can be buffered closer to the user device.Which content should be kept on the edge is the primary concern.The cache policy has been optimized using various conventional and unconventional methods,but they have yet to include the timestamp beside a video request.The 24-h content request pattern was obtained from publicly available datasets.The popularity of a video is influenced by the time of day,as shown by a time-based video profile.We present a cache optimization method based on a time-based pat-tern of requests.The problem is described as a cache hit ratio maximization pro-blem emphasizing a relevance score and machine learning model accuracy.A model predicts the video to be cached in the next time stamp,and the relevance score identifies the video to be removed from the cache.Afterwards,we gather the logs and generate the content requests using an extracted video request pattern.These logs are pre-processed to create a dataset divided into three-time slots per day.A Long short-term memory(LSTM)model is trained on this dataset to forecast the video at the next time interval.The proposed optimized caching policy is evaluated on our CDN architecture deployed on the Korean Advanced Research Network(KOREN)infrastructure.Our findings demonstrate how add-ing time-based request patterns impacts the system by increasing the cache hit rate.To show the effectiveness of the proposed model,we compare the results with state-of-the-art techniques.展开更多
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF2016R1D1A1B01016322).
文摘The scope of the 5G network is not only limited to the enhancements in the form of the quality of service(QoS),but it also includes a wide range of services with various requirements.Besides this,many approaches and platforms are under the umbrella of 5G to achieve the goals of endto-end service provisioning.However,the management of multiple services over heterogeneous platforms is a complex task.Each platform and service have various requirements to be handled by domain experts.Still,if the next-generation network management is dependent on manual updates,it will become impossible to provide seamless service provisioning in runtime.Since the traffic for a particular type of service varies significantly over time,automatic provisioning of resources and orchestration in runtime need to be integrated.Besides,with the increase in the number of devices,amount,and variety of traffic,the management of resources with optimization becomes a challenging task.To this end,this manuscript provides a solution that automates the management and service provisioning through multiple platforms while assuring various aspects,including automation,resource management and service assurance.The solution consists of an intent-based system that automaticallymanages different orchestrators,and eliminates manual control by abstracting the complex configuration requirements into simple and generic contracts.The proposed systemconsiders handling the scalability of resources in runtime by usingMachine Learning(ML)to automate and optimize service resource utilization.
基金This research was supported by the 2022 scientific promotion program funded by Jeju National University.
文摘Data is growing quickly due to a significant increase in social media applications.Today,billions of people use an enormous amount of data to access the Internet.The backbone network experiences a substantial load as a result of an increase in users.Users in the same region or company frequently ask for similar material,especially on social media platforms.The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user.Applications that require relatively low latency can use Content Delivery Network(CDN)technology to meet their requirements.An edge and the data center con-stitute the CDN architecture.To fulfill requests from the edge and minimize the impact on the network,the requested content can be buffered closer to the user device.Which content should be kept on the edge is the primary concern.The cache policy has been optimized using various conventional and unconventional methods,but they have yet to include the timestamp beside a video request.The 24-h content request pattern was obtained from publicly available datasets.The popularity of a video is influenced by the time of day,as shown by a time-based video profile.We present a cache optimization method based on a time-based pat-tern of requests.The problem is described as a cache hit ratio maximization pro-blem emphasizing a relevance score and machine learning model accuracy.A model predicts the video to be cached in the next time stamp,and the relevance score identifies the video to be removed from the cache.Afterwards,we gather the logs and generate the content requests using an extracted video request pattern.These logs are pre-processed to create a dataset divided into three-time slots per day.A Long short-term memory(LSTM)model is trained on this dataset to forecast the video at the next time interval.The proposed optimized caching policy is evaluated on our CDN architecture deployed on the Korean Advanced Research Network(KOREN)infrastructure.Our findings demonstrate how add-ing time-based request patterns impacts the system by increasing the cache hit rate.To show the effectiveness of the proposed model,we compare the results with state-of-the-art techniques.
