Nowadays, most positioning systems carry out locational calculation based on the accurate location information of some devices in the network. However there is a deviation in the locational information of the part of ...Nowadays, most positioning systems carry out locational calculation based on the accurate location information of some devices in the network. However there is a deviation in the locational information of the part of the device, we need to reduce it in order to obtain higher positioning accuracy. In this paper, we proposed a new centralized D2D(Device-to-Device) co-location algorithm. This algorithm uses DBSACN(Density-Based Spatial Clustering of Applications with Noise) clustering to reduce the deviation of device location information. Numerical results show that the positioning accuracy of the centralized D2D co-localization algorithm is improved by 62.7% compared with the SPAWN algorithm, which positioning performance superior to the traditional co-localization algorithm.展开更多
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
基金financially supported by the National Key Research&Development Program under Grant No.2018YFC0809702。
文摘Nowadays, most positioning systems carry out locational calculation based on the accurate location information of some devices in the network. However there is a deviation in the locational information of the part of the device, we need to reduce it in order to obtain higher positioning accuracy. In this paper, we proposed a new centralized D2D(Device-to-Device) co-location algorithm. This algorithm uses DBSACN(Density-Based Spatial Clustering of Applications with Noise) clustering to reduce the deviation of device location information. Numerical results show that the positioning accuracy of the centralized D2D co-localization algorithm is improved by 62.7% compared with the SPAWN algorithm, which positioning performance superior to the traditional co-localization algorithm.
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.
