http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Resource Allocation and Distributed Uplink Offloading Mechanism in Fog Environment
Linna Ruan,Zhoubin Liu,Xuesong Qiu,Zixiang Wang,Shaoyong Guo,Feng Qi 한국통신학회 2018 Journal of communications and networks Vol.20 No.3
Applying fog computing technology to the shared patternhas two problems to cope with. One is to formulate a rational mechanismfor resource allocation, and the other is to design computationoffloading strategy of tasks based on resource allocation result. For solving these problems, we construct a three-layer F-RAN architecturefirst, which consists of terminal layer, access layer andnetwork layer. Second, we adopt differential game and bipartitegraph multiple matching algorithm to solve bandwidth resource allocationproblemof fog node (FN)-access point (AP) and AP-sharedterminal (ST), respectively. Third, Lyapunov theory and proposeddeviation update decision algorithm (DUDA) are used to solve computationoffloading decision-making and offloading update ordermaking. At last, simulation results show that our strategy can save30%-60% system consumption, and the resource demand satisfactionrate can be guaranteed to reach 80% or more.
Resource Allocation and Distributed Uplink Offloading Mechanism in Fog Environment
Ruan, Linna,Liu, Zhoubin,Qiu, Xuesong,Wang, Zixiang,Guo, Shaoyong,Qi, Feng The Korean Institute of Communications and Informa 2018 Journal of communications and networks Vol.20 No.3
Applying fog computing technology to the shared pattern has two problems to cope with. One is to formulate a rational mechanism for resource allocation, and the other is to design computation offloading strategy of tasks based on resource allocation result. For solving these problems, we construct a three-layer F-RAN architecture first, which consists of terminal layer, access layer and network layer. Second, we adopt differential game and bipartite graph multiple matching algorithm to solve bandwidth resource allocation problem of fog node (FN)-access point (AP) and AP-shared terminal (ST), respectively. Third, Lyapunov theory and proposed deviation update decision algorithm (DUDA) are used to solve computation offloading decision-making and offloading update order-making. At last, simulation results show that our strategy can save 30%-60% system consumption, and the resource demand satisfaction rate can be guaranteed to reach 80% or more.