Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00033
Xiaonan Lv, Zongwei Huang, Liangyu Sun, M. Wu, Li Huang, Yehong Li
For multi-source heterogeneous complex data types of data cleaning and visual display, we proposed to build dynamic multimode visualization analysis tool, according to the different types of data designed by the user in accordance with the data model, and use visualization technology tools to build and use CQRS technology to design, external interface using a RESTFul architecture, The domain model and data query are completely separated, and the underlying data store adopts Hbase, ES and relational database. Drools is adopted in the data flow engine. According to the internal algorithm, three kinds of graphs can be output, namely, transaction relationship network analysis graph, capital flow analysis graph and transaction timing analysis graph, which can reduce the difficulty of analysis and help users to analyze data in a more friendly way
{"title":"Research and design of web-based capital transaction data dynamic multi-mode visual analysis tool","authors":"Xiaonan Lv, Zongwei Huang, Liangyu Sun, M. Wu, Li Huang, Yehong Li","doi":"10.1109/SmartCloud55982.2022.00033","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00033","url":null,"abstract":"For multi-source heterogeneous complex data types of data cleaning and visual display, we proposed to build dynamic multimode visualization analysis tool, according to the different types of data designed by the user in accordance with the data model, and use visualization technology tools to build and use CQRS technology to design, external interface using a RESTFul architecture, The domain model and data query are completely separated, and the underlying data store adopts Hbase, ES and relational database. Drools is adopted in the data flow engine. According to the internal algorithm, three kinds of graphs can be output, namely, transaction relationship network analysis graph, capital flow analysis graph and transaction timing analysis graph, which can reduce the difficulty of analysis and help users to analyze data in a more friendly way","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124873279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00011
Zhiyan Chen, Ligang He
With the rapid growth of the number of mobile devices and the increase of the corresponding computation demand, it has been considered that Mobile Cloud computing and Edge computing will play the significant roles in the upcoming IoT era. It has become an active research topic to develop the offloading schemes for mobile devices, in which the tasks arriving at the mobile devices may be offloaded to run in the cloud or the edge devices. In this paper, mobile edge cloud systems are considered, which consists of mobile devices, edge devices and the cloud server, and the three-tier offloading schemes are proposed to achieve the optimal task performance in MEC. In the three-tier offloading schemes, the computation tasks arriving at the mobile devices may be offloaded to run on the edge devices while the edge devices may further offload the tasks to the cloud when the edge devices are overwhelmed. In this paper, two task modes are considered: batch mode and streaming mode. For the batch mode (i.e., the tasks arriving at the systems and being processed in batches), the offloading optimization problem is modelled as a Mixed 0-1 Integer Programming problem, aiming to minimizing the makespan of the batch of tasks. For streaming mode (i.e., the tasks arriving at the system continuously), the offloading optimization problem is formulated as a non-linear optimization problem, aiming to minimizing the average response time of a task in the task stream. The extensive experiments have been conducted to demonstrate the effectiveness of the proposed offloading schemes, and the impact of various parameters in the MEC systems is also evaluated.
{"title":"Optimizing Offloading Strategies for Mobile Edge Cloud Systems","authors":"Zhiyan Chen, Ligang He","doi":"10.1109/SmartCloud55982.2022.00011","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00011","url":null,"abstract":"With the rapid growth of the number of mobile devices and the increase of the corresponding computation demand, it has been considered that Mobile Cloud computing and Edge computing will play the significant roles in the upcoming IoT era. It has become an active research topic to develop the offloading schemes for mobile devices, in which the tasks arriving at the mobile devices may be offloaded to run in the cloud or the edge devices. In this paper, mobile edge cloud systems are considered, which consists of mobile devices, edge devices and the cloud server, and the three-tier offloading schemes are proposed to achieve the optimal task performance in MEC. In the three-tier offloading schemes, the computation tasks arriving at the mobile devices may be offloaded to run on the edge devices while the edge devices may further offload the tasks to the cloud when the edge devices are overwhelmed. In this paper, two task modes are considered: batch mode and streaming mode. For the batch mode (i.e., the tasks arriving at the systems and being processed in batches), the offloading optimization problem is modelled as a Mixed 0-1 Integer Programming problem, aiming to minimizing the makespan of the batch of tasks. For streaming mode (i.e., the tasks arriving at the system continuously), the offloading optimization problem is formulated as a non-linear optimization problem, aiming to minimizing the average response time of a task in the task stream. The extensive experiments have been conducted to demonstrate the effectiveness of the proposed offloading schemes, and the impact of various parameters in the MEC systems is also evaluated.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130671513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00019
Peng Nie, Zhenwei Yang, Ziyuan Zhang
With the rapid rise of Internet of vehicles applications, a large number of time delay sensitive tasks, such as autonomous driving and virtual reality, have emerged. These tasks require the mobile terminal to have a lower transmission delay to the server. Offloading tasks to adjacent edge servers is an effective way to reduce latency, and it is also a common deployment constraint. How to optimize the allocation of edge computing resources under this constraint is a major challenge. This paper proposes a truthful dynamic online double auction mechanism, different from the traditional double auction mechanism, this paper considers multiple heterogeneous edge server nodes, each server node acts as an independent service provider, and also considers the deployment constraints of vehicles on different edge servers, that is, vehicle users only offload tasks to adjacent edge servers, and in the execution time of the task, it needs to maintain a continuous connection with the server. Then, according to the supply-demand relationship of the market, a monotonic approximate algorithm is designed to determine the winner in polynomial time. In terms of pricing, a critical-valuebased pricing strategy is proposed. Simulation results verify the effectiveness of the mechanism.
{"title":"Dynamic Online Double Auction Mechanism based on Deployment Constraints in the Internet of Vehicles","authors":"Peng Nie, Zhenwei Yang, Ziyuan Zhang","doi":"10.1109/SmartCloud55982.2022.00019","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00019","url":null,"abstract":"With the rapid rise of Internet of vehicles applications, a large number of time delay sensitive tasks, such as autonomous driving and virtual reality, have emerged. These tasks require the mobile terminal to have a lower transmission delay to the server. Offloading tasks to adjacent edge servers is an effective way to reduce latency, and it is also a common deployment constraint. How to optimize the allocation of edge computing resources under this constraint is a major challenge. This paper proposes a truthful dynamic online double auction mechanism, different from the traditional double auction mechanism, this paper considers multiple heterogeneous edge server nodes, each server node acts as an independent service provider, and also considers the deployment constraints of vehicles on different edge servers, that is, vehicle users only offload tasks to adjacent edge servers, and in the execution time of the task, it needs to maintain a continuous connection with the server. Then, according to the supply-demand relationship of the market, a monotonic approximate algorithm is designed to determine the winner in polynomial time. In terms of pricing, a critical-valuebased pricing strategy is proposed. Simulation results verify the effectiveness of the mechanism.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"260 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131519993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00038
B. Thuraisingham
This paper first describes the security and privacy challenges for the Internet of Things IoT) systems and then discusses some of the solutions that have been proposed. It also describes aspects of Trustworthy Machine Learning (TML) and then discusses how TML may be applied to handle some of the security and privacy challenges for IoT systems.
{"title":"Trustworthy Machine Learning for Securing IoT Systems","authors":"B. Thuraisingham","doi":"10.1109/SmartCloud55982.2022.00038","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00038","url":null,"abstract":"This paper first describes the security and privacy challenges for the Internet of Things IoT) systems and then discusses some of the solutions that have been proposed. It also describes aspects of Trustworthy Machine Learning (TML) and then discusses how TML may be applied to handle some of the security and privacy challenges for IoT systems.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117019343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous accumulation of large-scale power grid data, the traditional centralized data analysis method is more and more expensive for data transmission. Based on this, we designed a grid big data monitoring and analysis system and transferred the computation process to the edge node close to the data source through an edge computing strategy. On the one hand, data processing and data analysis algorithms are encapsulated by container technology, and the algorithm is mirrored to the edge nodes of the power network through the system to complete the computation. On the other hand, the computing clusters are deployed at the edge nodes of the power network, which is responsible for the scheduling, execution, and status monitoring of computing tasks. Computing tasks can be flexibly managed in a cluster by extending user-defined resources. Through the reserved parameters, users can intervene in task execution policies, and tasks can be configured. The edge node sends the calculation result or early warning information to the central monitoring service through the asynchronous message. Compared with the traditional centralized data analysis system, the proposed method relieves the problem of the overhead of massive data transmission in the network, reduces the application cost, helps to apply the data analysis to more edge side nodes, and fully excavates the potential value of grid data.
{"title":"Power Grid Data Monitoring and Analysis System based on Edge Computing","authors":"Tianyou Wang, Yuanze Qin, Yu Huang, Yiwei Lou, Chongyou Xu, Lei Chen","doi":"10.1109/SmartCloud55982.2022.00012","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00012","url":null,"abstract":"With the continuous accumulation of large-scale power grid data, the traditional centralized data analysis method is more and more expensive for data transmission. Based on this, we designed a grid big data monitoring and analysis system and transferred the computation process to the edge node close to the data source through an edge computing strategy. On the one hand, data processing and data analysis algorithms are encapsulated by container technology, and the algorithm is mirrored to the edge nodes of the power network through the system to complete the computation. On the other hand, the computing clusters are deployed at the edge nodes of the power network, which is responsible for the scheduling, execution, and status monitoring of computing tasks. Computing tasks can be flexibly managed in a cluster by extending user-defined resources. Through the reserved parameters, users can intervene in task execution policies, and tasks can be configured. The edge node sends the calculation result or early warning information to the central monitoring service through the asynchronous message. Compared with the traditional centralized data analysis system, the proposed method relieves the problem of the overhead of massive data transmission in the network, reduces the application cost, helps to apply the data analysis to more edge side nodes, and fully excavates the potential value of grid data.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127209222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00013
Zhenwei Yang, Ziyuan Zhang, Peng Nie
The vehicular edge computing technology extends the Internet of Vehicles(IoV) from cloud computing to edge computing, enabling IoV to support in-vehicle applications such as autonomous driving, high-definition video, and navigation planning with low latency and low bandwidth consumption costs. Due to the high deployment cost and maintenance cost of edge computing nodes, to improve the revenue of service providers and encourage edge computing service providers to deploy computing nodes, it is necessary to design an incentive mechanism for edge computing service providers. Auctions are an effective incentive design solution. This paper designs an optimal auction mechanism to maximize the revenue of edge computing service providers, which ensures the two important attributes of individual rationality and incentive compatibility and ensures the feasibility of allocation and efficient use of resources. Specifically, we designed a system model for pricing and allocating edge computing service providers in the Internet of Vehicles environment, and transformed the optimal auction problem of resources under the Internet of Vehicles into a mathematical programming model of the optimal auction with constraints. And designed a matching algorithm, allocation algorithm, and price calculation algorithm based on a neural network. Finally, we experiment and analyze the algorithm. The simulation results show that the proposed scheme is superior to the VCG algorithm in terms of revenue and resource utilization.
{"title":"A Deep-Learning-Based Optimal Auction for Vehicular Edge Computing Resource Allocation","authors":"Zhenwei Yang, Ziyuan Zhang, Peng Nie","doi":"10.1109/SmartCloud55982.2022.00013","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00013","url":null,"abstract":"The vehicular edge computing technology extends the Internet of Vehicles(IoV) from cloud computing to edge computing, enabling IoV to support in-vehicle applications such as autonomous driving, high-definition video, and navigation planning with low latency and low bandwidth consumption costs. Due to the high deployment cost and maintenance cost of edge computing nodes, to improve the revenue of service providers and encourage edge computing service providers to deploy computing nodes, it is necessary to design an incentive mechanism for edge computing service providers. Auctions are an effective incentive design solution. This paper designs an optimal auction mechanism to maximize the revenue of edge computing service providers, which ensures the two important attributes of individual rationality and incentive compatibility and ensures the feasibility of allocation and efficient use of resources. Specifically, we designed a system model for pricing and allocating edge computing service providers in the Internet of Vehicles environment, and transformed the optimal auction problem of resources under the Internet of Vehicles into a mathematical programming model of the optimal auction with constraints. And designed a matching algorithm, allocation algorithm, and price calculation algorithm based on a neural network. Finally, we experiment and analyze the algorithm. The simulation results show that the proposed scheme is superior to the VCG algorithm in terms of revenue and resource utilization.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115123055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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