In recent years,a large number of intelligent sensing devices have been deployed in the physical world,which brings great difficulties to the existing entity search.With the increase of the number of intelligent sensi...In recent years,a large number of intelligent sensing devices have been deployed in the physical world,which brings great difficulties to the existing entity search.With the increase of the number of intelligent sensing devices,the accuracy of the search system in querying the entities to match the user’s request is reduced,and the delay of entity search is increased.We use the mobile edge technology to alleviate this problem by processing user requests on the edge side and propose a similar physical entity matching strategy for the mobile edge search.First,the raw data collected by the sensor is lightly weighted and expressed to reduce the storage overhead of the observed data.Furthermore,a physical entity matching degree estimation method is proposed,in which the similarity between the sensor and the given sensor in the network is estimated,and the matching search of the user request is performed according to the similarity.Simulation results show that the proposed method can effectively reduce the data storage overhead and improve the precision of the sensor search system.展开更多
KNN set similarity search is a foundational operation in various realistic applications in cloud computing.However,for security consideration,sensitive data will always be encrypted before uploading to the cloud serve...KNN set similarity search is a foundational operation in various realistic applications in cloud computing.However,for security consideration,sensitive data will always be encrypted before uploading to the cloud servers,which makes the search processing a challenging task.In this paper,we focus on the problem of KNN set similarity search over the encrypted datasets.We use Yao’s garbled circuits and secret sharing as underlying tools.To achieve better querying efficiency,we construct a secure R-Tree index structure based on a novel secure grouping protocol,which enables grouping appropriate private values in an oblivious way.Along with several elaborately designed secure arithmetic subroutines,we propose an efficient secure and verifiable KNN set similarity search framework over outsourced clouds.Theoretically,we analyze the complexity of our schemes in detail,and prove the security in the presence of semi-honest adversaries.Finally,we evaluate the performance and feasibility of our proposed methods by extensive experiments.展开更多
负载预测的精度是影响云平台弹性资源管理的主要因素之一。而云平台中存在着大量的短任务负载序列,其历史信息不足和不平滑的特性导致难以选择合适的模型进行精准预测。对此提出了一种领域对抗自适应的短任务负载预测模型。该模型采用...负载预测的精度是影响云平台弹性资源管理的主要因素之一。而云平台中存在着大量的短任务负载序列,其历史信息不足和不平滑的特性导致难以选择合适的模型进行精准预测。对此提出了一种领域对抗自适应的短任务负载预测模型。该模型采用奇异谱分析(singular spectrum analysis,SSA)对样本进行平滑处理;联合第四版本的Mueen相似度搜索算法(the fourth version of Mueen’s algorithm for similarity search,MASS_V4)与时间特征进行域间相似性计算,获得合适的源域数据来辅助迁移预测;将门控循环单元(gated recurrent unit,GRU)作为基准器构建网络,并利用Y差异定义新的损失函数,通过对抗过程建立出表征能力强的短任务负载预测模型。将所提方法在两个真实的云平台数据集上与其他常用的云负载预测算法对比,均表现出较高的预测精度。展开更多
基金This work was supported by the National Natural Science Foundation of China(61871062,61771082,61901071)Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN201800615)General Project of Natural Science Foundation of Chongqing(cstc2019jcyj-msxmX0303).
文摘In recent years,a large number of intelligent sensing devices have been deployed in the physical world,which brings great difficulties to the existing entity search.With the increase of the number of intelligent sensing devices,the accuracy of the search system in querying the entities to match the user’s request is reduced,and the delay of entity search is increased.We use the mobile edge technology to alleviate this problem by processing user requests on the edge side and propose a similar physical entity matching strategy for the mobile edge search.First,the raw data collected by the sensor is lightly weighted and expressed to reduce the storage overhead of the observed data.Furthermore,a physical entity matching degree estimation method is proposed,in which the similarity between the sensor and the given sensor in the network is estimated,and the matching search of the user request is performed according to the similarity.Simulation results show that the proposed method can effectively reduce the data storage overhead and improve the precision of the sensor search system.
基金This work was supported by the Natural Science Foundation of China(61602400)Jiangsu Provincial Department of Education(16KJB520043).
文摘KNN set similarity search is a foundational operation in various realistic applications in cloud computing.However,for security consideration,sensitive data will always be encrypted before uploading to the cloud servers,which makes the search processing a challenging task.In this paper,we focus on the problem of KNN set similarity search over the encrypted datasets.We use Yao’s garbled circuits and secret sharing as underlying tools.To achieve better querying efficiency,we construct a secure R-Tree index structure based on a novel secure grouping protocol,which enables grouping appropriate private values in an oblivious way.Along with several elaborately designed secure arithmetic subroutines,we propose an efficient secure and verifiable KNN set similarity search framework over outsourced clouds.Theoretically,we analyze the complexity of our schemes in detail,and prove the security in the presence of semi-honest adversaries.Finally,we evaluate the performance and feasibility of our proposed methods by extensive experiments.
文摘负载预测的精度是影响云平台弹性资源管理的主要因素之一。而云平台中存在着大量的短任务负载序列,其历史信息不足和不平滑的特性导致难以选择合适的模型进行精准预测。对此提出了一种领域对抗自适应的短任务负载预测模型。该模型采用奇异谱分析(singular spectrum analysis,SSA)对样本进行平滑处理;联合第四版本的Mueen相似度搜索算法(the fourth version of Mueen’s algorithm for similarity search,MASS_V4)与时间特征进行域间相似性计算,获得合适的源域数据来辅助迁移预测;将门控循环单元(gated recurrent unit,GRU)作为基准器构建网络,并利用Y差异定义新的损失函数,通过对抗过程建立出表征能力强的短任务负载预测模型。将所提方法在两个真实的云平台数据集上与其他常用的云负载预测算法对比,均表现出较高的预测精度。
