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RISS 인기검색어
- 검색키워드
- 저자 : ( Kiran Nahida ) (검색결과 3 건)
- 무료
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Handover Based on AP Load in Software Defined Wi-Fi Systems
Nahida Kiran,Changchuan Yin,Ying Hu,Zulfiqar Ali Arain,Chunyu Pan,Israr Khan,Yanbin Zhang,G. M. Shafiqur Rahman 한국통신학회 2017 Journal of communications and networks Vol.19 No.6
Existing wireless systems do not work efficiently underchanging environment. Due to its inflexible and proprietary hardwarebased architectural limitations it’s not easy for an operatorto change the network strategy under heavy loads, which enablesvendors to try and implement new networking protocols. The softwaredefined network (SDN) is proposed to bring flexibility andprogrammability, which allows the control plane of switch to becontrolled and managed remotely using open-flow channels. Takingthe advantage of SDN in wireless networks, a new SDN basedWi-Fi architecture is introduced and an access point (AP) load balancebased handover algorithm is proposed. Mininet Wi-Fi emulatoris used to construct the desired topology for experiments andperformance analysis. Simulation results show a successful handoverfrom an overloaded AP to a lightly loaded AP. A significantimprovement observed in the throughput with low latency.
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Nahida Kiran,Chunyu Pan,Sihua Wang,Chang-chuan Yin 한국통신학회 2020 Journal of communications and networks Vol.22 No.1
The rapid growth of the internet usage and the distributedcomputing resources of edge devices create a necessity tohave a reasonable controller to ensure efficient utilization of distributedcomputing resources in mobile edge computing (MEC). We envision the future MEC services, where quality of experience(QoE) of the services is further enhanced by software definednetworks (SDNs) capabilities to reduce the application-levelresponse time without service disruptions. SDN, which is not proposedspecifically for edge computing, can in fact serve as an enablerto lower the complexity barriers involved and let the realpotential of edge computing be achieved. In this paper, we investigatethe task offloading and resource allocation problem in wirelessMEC aiming to minimize the delay while saving the battery powerof user device simultaneously. However, it is challenging to obtainan optimal policy in such a dynamic task offloading system. Learningfrom experience plays a vital role in time variant dynamic systemswhere reinforcement learning (RL) takes a long term goal intoconsideration besides immediate reward, which is very importantfor a dynamic environment. A novel software defined edge cloudlet(SDEC) based RL optimization framework is proposed to tacklethe task offloading and resource allocation in wireless MEC. Specifically,Q-learning and cooperative Q-learning based reinforcementlearning schemes are proposed for the intractable problem. Simulationresults show that the proposed scheme achieves 31.39% and62.10% reduction on the sum delay compared to other benchmarkmethods such as traditional Q-learning with a random algorithmand Q-learning with epsilon greedy.
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( Zulfiqar A. Arain ),( Xuesong Qiu ),( Lujie Zhong ),( Mu Wang ),( Xingyan Chen ),( Yongping Xiong ),( Kiran Nahida ),( Changqiao Xu ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.1
Multipath Transport Control Protocol (MPTCP) is a transport layer protocol that enables multiple TCP connections across various paths. Due to path heterogeneity, it incurs more energy in a multipath wireless network. Recent work presents a set of approaches described in the literature to support systems for energy consumption in terms of their performance, objectives and address issues based on their design goals. The existing solutions mainly focused on the primary system model but did not discourse the overall system performance. Therefore, this paper capitalized a novel stochastically multipath scheduling scheme for data and path capacity variations. The scheduling problem formulated over MPTCP as a stochastic optimization, whose objective is to maximize the average throughput, avoid network congestion, and makes the system more stable with greater energy efficiency. To design an online algorithm that solves the formulated problem over the time slots by considering its min-drift-plus penalty form. The proposed solution was examined under extensive simulations to evaluate the anticipated stochastic optimized MPTCP (so-MPTCP) outcome and compared it with the base MPTCP and the energy-efficient MPTCP (eMPTCP) protocols. Simulation results justify the proposed algorithm's credibility by achieving remarkable improvements, higher throughput, reduced energy costs, and lower-end to end delay.
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