该文根据流媒体系统中缓存空间不足及服务延迟的问题,提出一种基于P2P协作的代理缓存流媒体调度算法PCSPC(Proxy-Caching Scheduler based on P2P Cooperation)。首先按照流行度高的数据占用较大存储空间的原则,利用媒体文件的存储效率...该文根据流媒体系统中缓存空间不足及服务延迟的问题,提出一种基于P2P协作的代理缓存流媒体调度算法PCSPC(Proxy-Caching Scheduler based on P2P Cooperation)。首先按照流行度高的数据占用较大存储空间的原则,利用媒体文件的存储效率为每个前缀分配相应的存储空间。然后按传输成本将前缀降序排列,代理服务器升序排列,将前缀依次分配到代理服务器上,并且通过理论证明该方法能够有效地减少传输成本。PCSPC算法能够兼顾存储效率与传输成本。仿真实例说明了算法的有效性。展开更多
One of the challenges of Informationcentric Networking(ICN)is finding the optimal location for caching content and processing users’requests.In this paper,we address this challenge by leveraging Software-defined Netw...One of the challenges of Informationcentric Networking(ICN)is finding the optimal location for caching content and processing users’requests.In this paper,we address this challenge by leveraging Software-defined Networking(SDN)for efficient ICN management.To achieve this,we formulate the problem as a mixed-integer nonlinear programming(MINLP)model,incorporating caching,routing,and load balancing decisions.We explore two distinct scenarios to tackle the problem.Firstly,we solve the problem in an offline mode using the GAMS environment,assuming a stable network state to demonstrate the superior performance of the cacheenabled network compared to non-cache networks.Subsequently,we investigate the problem in an online mode where the network state dynamically changes over time.Given the computational complexity associated with MINLP,we propose the software-defined caching,routing,and load balancing(SDCRL)algorithm as an efficient and scalable solution.Our evaluation demonstrates that the SDCRL algorithm significantly reduces computational time while maintaining results that closely resemble those achieved by GAMS.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
Recently, content-centric networking （CCN） has become a hot research topic for the diffusion of contents over the Internet. Most existing works on CCN focus on the improvement of network resource utilization. Conseq...Recently, content-centric networking （CCN） has become a hot research topic for the diffusion of contents over the Internet. Most existing works on CCN focus on the improvement of network resource utilization. Consequently, the energy consumption aspect of CCN is largely ignored. In this paper, we propose a distributed energyefficient in-network caching scheme for CCN, where each content router only needs locally available information to make caching decisions considering both caching energy consumption and transport energy consumption. We formulate the in-network caching problem as a non-cooperative game. Through rigorous mathematical analysis, we prove that pure strategy Nash equilibria exist in the proposed scheme, and it always has a strategy profile that implements the socially optimal configuration, even if the touters are self-interested in nature. Simulation results are presented to show that the distributed solution is competitive to the centralized scheme, and has superior performance compared to other popular caching schemes in CCN. Besides, it exhibits a fast convergence speed when the capacity of content routers varies.展开更多
The emerging mobile edge networks with content caching capability allows end users to receive information from adjacent edge servers directly instead of a centralized data warehouse,thus the network transmission delay...The emerging mobile edge networks with content caching capability allows end users to receive information from adjacent edge servers directly instead of a centralized data warehouse,thus the network transmission delay and system throughput can be improved significantly.Since the duplicate content transmissions between edge network and remote cloud can be reduced,the appropriate caching strategy can also improve the system energy efficiency of mobile edge networks to a great extent.This paper focuses on how to improve the network energy efficiency and proposes an intelligent caching strategy according to the cached content distribution model for mobile edge networks based on promising deep reinforcement learning algorithm.The deep neural network(DNN)and Q-learning algorithm are combined to design a deep reinforcement learning framework named as the deep-Q neural network(DQN),in which the DNN is adopted to represent the approximation of action-state value function in the Q-learning solution.The parameters iteration strategies in the proposed DQN algorithm were improved through stochastic gradient descent method,so the DQN algorithm could converge to the optimal solution quickly,and the network performance of the content caching policy can be optimized.The simulation results show that the proposed intelligent DQN-based content cache strategy with enough training steps could improve the energy efficiency of the mobile edge networks significantly.展开更多
文摘该文根据流媒体系统中缓存空间不足及服务延迟的问题,提出一种基于P2P协作的代理缓存流媒体调度算法PCSPC(Proxy-Caching Scheduler based on P2P Cooperation)。首先按照流行度高的数据占用较大存储空间的原则,利用媒体文件的存储效率为每个前缀分配相应的存储空间。然后按传输成本将前缀降序排列,代理服务器升序排列,将前缀依次分配到代理服务器上,并且通过理论证明该方法能够有效地减少传输成本。PCSPC算法能够兼顾存储效率与传输成本。仿真实例说明了算法的有效性。
文摘One of the challenges of Informationcentric Networking(ICN)is finding the optimal location for caching content and processing users’requests.In this paper,we address this challenge by leveraging Software-defined Networking(SDN)for efficient ICN management.To achieve this,we formulate the problem as a mixed-integer nonlinear programming(MINLP)model,incorporating caching,routing,and load balancing decisions.We explore two distinct scenarios to tackle the problem.Firstly,we solve the problem in an offline mode using the GAMS environment,assuming a stable network state to demonstrate the superior performance of the cacheenabled network compared to non-cache networks.Subsequently,we investigate the problem in an online mode where the network state dynamically changes over time.Given the computational complexity associated with MINLP,we propose the software-defined caching,routing,and load balancing(SDCRL)algorithm as an efficient and scalable solution.Our evaluation demonstrates that the SDCRL algorithm significantly reduces computational time while maintaining results that closely resemble those achieved by GAMS.
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supported under the National Basic Research Program(973) of China(Project Number: 2012CB315801)the National Natural Science Fund(Project Number:61300184)the fundamental research funds for the Central Universities(Project Number:2013RC0113)
文摘Recently, content-centric networking （CCN） has become a hot research topic for the diffusion of contents over the Internet. Most existing works on CCN focus on the improvement of network resource utilization. Consequently, the energy consumption aspect of CCN is largely ignored. In this paper, we propose a distributed energyefficient in-network caching scheme for CCN, where each content router only needs locally available information to make caching decisions considering both caching energy consumption and transport energy consumption. We formulate the in-network caching problem as a non-cooperative game. Through rigorous mathematical analysis, we prove that pure strategy Nash equilibria exist in the proposed scheme, and it always has a strategy profile that implements the socially optimal configuration, even if the touters are self-interested in nature. Simulation results are presented to show that the distributed solution is competitive to the centralized scheme, and has superior performance compared to other popular caching schemes in CCN. Besides, it exhibits a fast convergence speed when the capacity of content routers varies.
基金This work was supported by the National Natural Science Foundation of China(61871058,WYF,http://www.nsfc.gov.cn/).
文摘The emerging mobile edge networks with content caching capability allows end users to receive information from adjacent edge servers directly instead of a centralized data warehouse,thus the network transmission delay and system throughput can be improved significantly.Since the duplicate content transmissions between edge network and remote cloud can be reduced,the appropriate caching strategy can also improve the system energy efficiency of mobile edge networks to a great extent.This paper focuses on how to improve the network energy efficiency and proposes an intelligent caching strategy according to the cached content distribution model for mobile edge networks based on promising deep reinforcement learning algorithm.The deep neural network(DNN)and Q-learning algorithm are combined to design a deep reinforcement learning framework named as the deep-Q neural network(DQN),in which the DNN is adopted to represent the approximation of action-state value function in the Q-learning solution.The parameters iteration strategies in the proposed DQN algorithm were improved through stochastic gradient descent method,so the DQN algorithm could converge to the optimal solution quickly,and the network performance of the content caching policy can be optimized.The simulation results show that the proposed intelligent DQN-based content cache strategy with enough training steps could improve the energy efficiency of the mobile edge networks significantly.
