Edge Server Activation Control Method with Early Congestion Avoidance by Using Deep Q Network
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초록
Edge networks have emerged as a solution to provide high-speed and localized data processing by utilizingdistributed computing models with numerous edge servers (ESs). However, the increasing deployment of ESshas significantly escalated energy consumption, raising critical concerns regarding operational costs,environmental sustainability, and economic efficiency. A dynamic network topology management approachhas been proposed to address this issue by adaptively switching the operation mode of each ES according toload condition. Even though various methods have been proposed to address this issue, there is still room forperformance improvement. To fill the performance gap, in this paper, we take a deep Q network-based approachand devise a novel ES activation control method that dynamically manages ES states, balancing energyefficiency and service quality through a specially designed reward function. Simulation results demonstrate theeffectiveness of the proposed approach in reducing the amount of consumed energy while increasing the servicequality across diverse scenarios, compared to existing methods.
Abstract
Edge networks have emerged as a solution to provide high-speed and localized data processing by utilizingdistributed computing models with numerous edge servers (ESs). However, the increasing deployment of ESshas significantly escalated energy consumption, raising critical concerns regarding operational costs,environmental sustainability, and economic efficiency. A dynamic network topology management approachhas been proposed to address this issue by adaptively switching the operation mode of each ES according toload condition. Even though various methods have been proposed to address this issue, there is still room forperformance improvement. To fill the performance gap, in this paper, we take a deep Q network-based approachand devise a novel ES activation control method that dynamically manages ES states, balancing energyefficiency and service quality through a specially designed reward function. Simulation results demonstrate theeffectiveness of the proposed approach in reducing the amount of consumed energy while increasing the servicequality across diverse scenarios, compared to existing methods.