Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competi...Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competition between batch jobs and online services,co-location frequently impairs the performance of online services.This study presents a quality of service(QoS)prediction-based schedulingmodel(QPSM)for co-locatedworkloads.The performance prediction of QPSM consists of two parts:the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on randomforest.On-line service QoS anomaly prediction is used to evaluate the influence of batch jobmix on on-line service performance,and batch job completion time prediction is utilized to reduce the total waiting time of batch jobs.When the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite,the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come,first-served strategy and by about 11 h compared with the random scheduling strategy.Compared with the non-co-located situation,QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on average.Experiments show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.展开更多
A model accounting for more than 30 parameters of drilling projects, and a computer program to enumerate groupings of the wells of a pad with consequent calculations of technical-economic characteristics, are develope...A model accounting for more than 30 parameters of drilling projects, and a computer program to enumerate groupings of the wells of a pad with consequent calculations of technical-economic characteristics, are developed and tested. Seven drilling scenarios for a 24-well pad with different starting oil flow rates for the wells are studied. Optimal well groupings in terms of Net Present Value (NPV) for three discount rates and five oil production decline rates have been found. The results show that: NPV-maximizing well pad designs with unequal (varying) numbers of wells in groups (clusters) may require only slight alterations of existing designs (changing the configurations of a couple of well clusters);relative NPV gain is inversely proportional to the absolute value of NPV;observed increases in NPV in groupings with varying numbers of wells reach up to 1% with respect to groupings with equal (constant) numbers of wells in groups for conventional projects, and could reach 2% and more for shale formation development projects, and up to 45% for marginal projects.展开更多
The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such...The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information transmission.Due to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security threats.This paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario.Each UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative domain.After successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for storage.There are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,respectively.The security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution attack.It also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)assumption.The performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.展开更多
Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized charact...Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.展开更多
基金supported by the NationalNatural Science Foundation of China(No.61972118)the Key R&D Program of Zhejiang Province(No.2023C01028).
文摘Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competition between batch jobs and online services,co-location frequently impairs the performance of online services.This study presents a quality of service(QoS)prediction-based schedulingmodel(QPSM)for co-locatedworkloads.The performance prediction of QPSM consists of two parts:the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on randomforest.On-line service QoS anomaly prediction is used to evaluate the influence of batch jobmix on on-line service performance,and batch job completion time prediction is utilized to reduce the total waiting time of batch jobs.When the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite,the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come,first-served strategy and by about 11 h compared with the random scheduling strategy.Compared with the non-co-located situation,QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on average.Experiments show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.
文摘A model accounting for more than 30 parameters of drilling projects, and a computer program to enumerate groupings of the wells of a pad with consequent calculations of technical-economic characteristics, are developed and tested. Seven drilling scenarios for a 24-well pad with different starting oil flow rates for the wells are studied. Optimal well groupings in terms of Net Present Value (NPV) for three discount rates and five oil production decline rates have been found. The results show that: NPV-maximizing well pad designs with unequal (varying) numbers of wells in groups (clusters) may require only slight alterations of existing designs (changing the configurations of a couple of well clusters);relative NPV gain is inversely proportional to the absolute value of NPV;observed increases in NPV in groupings with varying numbers of wells reach up to 1% with respect to groupings with equal (constant) numbers of wells in groups for conventional projects, and could reach 2% and more for shale formation development projects, and up to 45% for marginal projects.
基金supported in part by the National Key R&D Program of China under Project 2020YFB1006004the Guangxi Natural Science Foundation under Grants 2019GXNSFFA245015 and 2019GXNSFGA245004+2 种基金the National Natural Science Foundation of China under Projects 62162017,61862012,61962012,and 62172119the Major Key Project of PCL under Grants PCL2021A09,PCL2021A02 and PCL2022A03the Innovation Project of Guangxi Graduate Education YCSW2021175.
文摘The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information transmission.Due to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security threats.This paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario.Each UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative domain.After successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for storage.There are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,respectively.The security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution attack.It also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)assumption.The performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.
基金funded by National Natural Science Foundation of China(Grant Nos.42272333,42277147).
文摘Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.
