Smart city pollution control is fundamental to urban sustainability,which relies extensively on physical infrastructure such as sensors and cameras for real-time monitoring.Generally,monitoring data needs to be transm...Smart city pollution control is fundamental to urban sustainability,which relies extensively on physical infrastructure such as sensors and cameras for real-time monitoring.Generally,monitoring data needs to be transmitted to centralized servers for pollution control service determination.In order to achieve highly efficient service quality,edge computing is involved in the smart city pollution control system(SCPCS)as it provides computational capabilities near the monitoring devices and low-latency pollution control services.However,considering the diversity of service requests,determination of offloading destination is a crucial challenge for SCPCS.In this paper,A Deep Q-Network(DQN)-based edge offloading method,called N-DEO,is proposed.Initially,N-DEO employs neural hierarchical interpolation for time series forecasting(N-HITS)to forecast pollution control service requests.Afterwards,an epsilon-greedy policy is designed to select actions.Finally,the optimal service offloading strategy is determined by the DQN algorithm.Experimental results demonstrate that N-DEO achieves the higher performance on service latency and system load compared with the current state-of-the-art methods.展开更多
Mobile Edge Computing(MEC)is a pivotal technology that provides agile-response services by deploying computation and storage resources in proximity to end-users.However,resource-constrained edge servers fall victim to...Mobile Edge Computing(MEC)is a pivotal technology that provides agile-response services by deploying computation and storage resources in proximity to end-users.However,resource-constrained edge servers fall victim to Denial-of-Service(DoS)attacks easily.Failures to mitigate DoS attacks effectively hinder the delivery of reliable and sustainable edge services.Conventional DoS mitigation solutions in cloud computing environments are not directly applicable in MEC environments because their design did not factor in the unique characteristics of MEC environments,e.g.,constrained resources on edge servers and requirements for low service latency.Existing solutions mitigate edge DoS attacks by transferring user requests from edge servers under attacks to others for processing.Furthermore,the heterogeneity in end-users’resource demands can cause resource fragmentation on edge servers and undermine the ability of these solutions to mitigate DoS attacks effectively.User requests often have to be transferred far away for processing,which increases the service latency.To tackle this challenge,this paper presents a fragmentationaware gaming approach called HEDMGame that attempts to minimize service latency by matching user requests to edge servers’remaining resources while making request-transferring decisions.Through theoretical analysis and experimental evaluation,we validate the effectiveness and efficiency of HEDMGame,and demonstrate its superiority over the state-of-the-art solution.展开更多
Large-scale service composition has become an important research topic in Service-Oriented Computing（SOC）. Quality of Service（Qo S） has been mostly applied to represent nonfunctional properties of web services and...Large-scale service composition has become an important research topic in Service-Oriented Computing（SOC）. Quality of Service（Qo S） has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of Qo S have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly,the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS FOA（Web Service composition based on Fruit Fly Optimization Algorithm） for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.展开更多
基金supported by the National Natural Science Foundation of China(No.92267104 and 62372242)in part by the Natural Science Foundation of Jiangsu Province of China(No.BK20211284)the NUIST Students’Platform for Innovation and Entrepreneurship Training Program(No.202410300059Z).
文摘Smart city pollution control is fundamental to urban sustainability,which relies extensively on physical infrastructure such as sensors and cameras for real-time monitoring.Generally,monitoring data needs to be transmitted to centralized servers for pollution control service determination.In order to achieve highly efficient service quality,edge computing is involved in the smart city pollution control system(SCPCS)as it provides computational capabilities near the monitoring devices and low-latency pollution control services.However,considering the diversity of service requests,determination of offloading destination is a crucial challenge for SCPCS.In this paper,A Deep Q-Network(DQN)-based edge offloading method,called N-DEO,is proposed.Initially,N-DEO employs neural hierarchical interpolation for time series forecasting(N-HITS)to forecast pollution control service requests.Afterwards,an epsilon-greedy policy is designed to select actions.Finally,the optimal service offloading strategy is determined by the DQN algorithm.Experimental results demonstrate that N-DEO achieves the higher performance on service latency and system load compared with the current state-of-the-art methods.
基金partially funded by the National Natural Science Foundation of China(No.62272001).
文摘Mobile Edge Computing(MEC)is a pivotal technology that provides agile-response services by deploying computation and storage resources in proximity to end-users.However,resource-constrained edge servers fall victim to Denial-of-Service(DoS)attacks easily.Failures to mitigate DoS attacks effectively hinder the delivery of reliable and sustainable edge services.Conventional DoS mitigation solutions in cloud computing environments are not directly applicable in MEC environments because their design did not factor in the unique characteristics of MEC environments,e.g.,constrained resources on edge servers and requirements for low service latency.Existing solutions mitigate edge DoS attacks by transferring user requests from edge servers under attacks to others for processing.Furthermore,the heterogeneity in end-users’resource demands can cause resource fragmentation on edge servers and undermine the ability of these solutions to mitigate DoS attacks effectively.User requests often have to be transferred far away for processing,which increases the service latency.To tackle this challenge,this paper presents a fragmentationaware gaming approach called HEDMGame that attempts to minimize service latency by matching user requests to edge servers’remaining resources while making request-transferring decisions.Through theoretical analysis and experimental evaluation,we validate the effectiveness and efficiency of HEDMGame,and demonstrate its superiority over the state-of-the-art solution.
基金supported by the National Natural Science Foundation of China (Nos. 61402006 and 61202227)the Natural Science Foundation of Anhui Province of China (No. 1408085MF132)+2 种基金the Science and Technology Planning Project of Anhui Province of China (No. 1301032162)the College Students Scientific Research Training Program (No. KYXL2014060)the 211 Project of Anhui University (No. 02303301)
文摘Large-scale service composition has become an important research topic in Service-Oriented Computing（SOC）. Quality of Service（Qo S） has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of Qo S have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly,the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS FOA（Web Service composition based on Fruit Fly Optimization Algorithm） for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.
