Pub Date : 2022-08-01DOI: 10.1109/JCC56315.2022.00019
Xuyang Zhao, Mingyu Li, Erhu Feng, Yubin Xia
As data security in public clouds attracts more attention and concerns, researchers and practitioners have proposed techniques to secure cloud computing. Confidential computing (CC) is a compelling approach that guarantees both privacy and integrity of data and code in public clouds. In this paper, we first survey the status of CC in today’s commercialized public clouds, including the cloud CC abstractions, infrastructures, metrics, third-party service vendors, and real-world cloud use cases. We also discover the limitations such as re-programming efforts, extra cost, limited availability, etc. We further take a step forward to prospect CC in the joint cloud scenario. We finally showcase the challenges of realizing a secure joint cloud and propose possible solutions.
{"title":"Towards A Secure Joint Cloud With Confidential Computing","authors":"Xuyang Zhao, Mingyu Li, Erhu Feng, Yubin Xia","doi":"10.1109/JCC56315.2022.00019","DOIUrl":"https://doi.org/10.1109/JCC56315.2022.00019","url":null,"abstract":"As data security in public clouds attracts more attention and concerns, researchers and practitioners have proposed techniques to secure cloud computing. Confidential computing (CC) is a compelling approach that guarantees both privacy and integrity of data and code in public clouds. In this paper, we first survey the status of CC in today’s commercialized public clouds, including the cloud CC abstractions, infrastructures, metrics, third-party service vendors, and real-world cloud use cases. We also discover the limitations such as re-programming efforts, extra cost, limited availability, etc. We further take a step forward to prospect CC in the joint cloud scenario. We finally showcase the challenges of realizing a secure joint cloud and propose possible solutions.","PeriodicalId":239996,"journal":{"name":"2022 IEEE International Conference on Joint Cloud Computing (JCC)","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124762687","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 : 2022-08-01DOI: 10.1109/JCC56315.2022.00017
Qingkun Wang, Yi Ren, Saqing Yang, Jianbo Guan, Bao Li, Jianfeng Zhang, Yusong Tan
Edge computing is booming as a promising paradigm to push the service and computation resources from the cloud to the edge of network. As the de-facto standard for container orchestration, Kubernetes is more and more widely used not only in cloud computing but also in edge computing. However, Kubernetes is designed for homogenous cloud data centers, and it does not take into account heterogeneous scenarios, which is ubiquitous is the edge. This will lead to load imbalance among containers with its default rough load balancing mechanism. To deal with this problem, we firstly propose a Dynamically Weighted Random Routing (DWRR) algorithm based on the default random algorithm in Kubernetes. Besides, we design and implement ProxyDWRR, a load balancing plugin for the Kubernetes cluster with heterogeneous CPU. It is fully compatible with the existing load balancing mechanism in Kubernetes. We validated our solution based on a cloud-native microservices application. The experimental results show that ProxyDWRR can effectively balance the load between containers in clusters with heterogeneous CPU. In our experiments, DWRR can improve the CPU utilization of the containers by about 25% and the throughput of the application by about 22.6% compared to the default load balancing algorithms, which enables the cluster to evacuate bursty load more effectively.
{"title":"ProxyDWRR: A Dynamic Load Balancing Approach for Heterogeneous-CPU Kubernetes Clusters","authors":"Qingkun Wang, Yi Ren, Saqing Yang, Jianbo Guan, Bao Li, Jianfeng Zhang, Yusong Tan","doi":"10.1109/JCC56315.2022.00017","DOIUrl":"https://doi.org/10.1109/JCC56315.2022.00017","url":null,"abstract":"Edge computing is booming as a promising paradigm to push the service and computation resources from the cloud to the edge of network. As the de-facto standard for container orchestration, Kubernetes is more and more widely used not only in cloud computing but also in edge computing. However, Kubernetes is designed for homogenous cloud data centers, and it does not take into account heterogeneous scenarios, which is ubiquitous is the edge. This will lead to load imbalance among containers with its default rough load balancing mechanism. To deal with this problem, we firstly propose a Dynamically Weighted Random Routing (DWRR) algorithm based on the default random algorithm in Kubernetes. Besides, we design and implement ProxyDWRR, a load balancing plugin for the Kubernetes cluster with heterogeneous CPU. It is fully compatible with the existing load balancing mechanism in Kubernetes. We validated our solution based on a cloud-native microservices application. The experimental results show that ProxyDWRR can effectively balance the load between containers in clusters with heterogeneous CPU. In our experiments, DWRR can improve the CPU utilization of the containers by about 25% and the throughput of the application by about 22.6% compared to the default load balancing algorithms, which enables the cluster to evacuate bursty load more effectively.","PeriodicalId":239996,"journal":{"name":"2022 IEEE International Conference on Joint Cloud Computing (JCC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127432843","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 : 2022-08-01DOI: 10.1109/JCC56315.2022.00010
Mingming Zhang, Yunjun Gao, Chuan He, Tianyu Tan
Data-driven innovation and optimization have become an important direction for the intelligent transformation of enterprises. Data processing tasks have been developed and orchestrated to extract data insights, creating direct or indirect data dependencies between tasks or between tasks and the presentation layer. Traditional ETL (Extract-Transformation-Load) solutions share data through persistent storage, which has certain performance bottlenecks in hybrid cloud and multisource data scenarios. In this paper, we propose MicroStream, a distributed data virtualization and caching middleware service. MicroStream shields the direct access of ETL tasks to the storage layer and converts batch access to the source database into microstream access. ETL jobs share data through the distributed in-memory caching of MicroStream. In resource-constrained scenarios, such a solution significantly improves the performance of data transformation while reducing the extra load that the transformation jobs imply on the source persistent layer. We present a detailed performance evaluation of MicroStream and show that its performance compares favorably with traditional database-oriented solutions.
{"title":"MicroStream: A Distributed In-memory Caching Service For Data Production","authors":"Mingming Zhang, Yunjun Gao, Chuan He, Tianyu Tan","doi":"10.1109/JCC56315.2022.00010","DOIUrl":"https://doi.org/10.1109/JCC56315.2022.00010","url":null,"abstract":"Data-driven innovation and optimization have become an important direction for the intelligent transformation of enterprises. Data processing tasks have been developed and orchestrated to extract data insights, creating direct or indirect data dependencies between tasks or between tasks and the presentation layer. Traditional ETL (Extract-Transformation-Load) solutions share data through persistent storage, which has certain performance bottlenecks in hybrid cloud and multisource data scenarios. In this paper, we propose MicroStream, a distributed data virtualization and caching middleware service. MicroStream shields the direct access of ETL tasks to the storage layer and converts batch access to the source database into microstream access. ETL jobs share data through the distributed in-memory caching of MicroStream. In resource-constrained scenarios, such a solution significantly improves the performance of data transformation while reducing the extra load that the transformation jobs imply on the source persistent layer. We present a detailed performance evaluation of MicroStream and show that its performance compares favorably with traditional database-oriented solutions.","PeriodicalId":239996,"journal":{"name":"2022 IEEE International Conference on Joint Cloud Computing (JCC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123823688","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 rapid development of cloud computing, accurate resource usage prediction has become a key technology for the efficient utilization of cloud data center resources. Aiming at the problems of low prediction accuracy and long prediction time of the current load prediction model, a combined prediction model BILSTM-GRU based on bidirectional long short-term memory network (BILSTM) and gated recurrent unit (GRU) is proposed, which effectively combines BILSTM network with high prediction accuracy and short prediction time of the GRU network. It is compared and verified with various classical time series prediction algorithms on the Google cloud computing data set. Experimental results show that the mean square error (MSE) of BILSTM-GRU combined prediction model is reduced by about 5, and the prediction time is shortened by about 5% compared with the existing combined prediction model. The experimental results verify that BILSTM-GRU combined model has higher prediction accuracy and shorter prediction time, which provides an important scientific basis for automatic expansion and shrinkage of cloud computing containers using the prediction results of resource usage.
{"title":"Resource Usage Prediction Based on BILSTM-GRU Combination Model","authors":"Xueting Li, Hongliang Wang, Pengfei Xiu, Xingyu Zhou, Fanhua Meng","doi":"10.1109/JCC56315.2022.00009","DOIUrl":"https://doi.org/10.1109/JCC56315.2022.00009","url":null,"abstract":"With the rapid development of cloud computing, accurate resource usage prediction has become a key technology for the efficient utilization of cloud data center resources. Aiming at the problems of low prediction accuracy and long prediction time of the current load prediction model, a combined prediction model BILSTM-GRU based on bidirectional long short-term memory network (BILSTM) and gated recurrent unit (GRU) is proposed, which effectively combines BILSTM network with high prediction accuracy and short prediction time of the GRU network. It is compared and verified with various classical time series prediction algorithms on the Google cloud computing data set. Experimental results show that the mean square error (MSE) of BILSTM-GRU combined prediction model is reduced by about 5, and the prediction time is shortened by about 5% compared with the existing combined prediction model. The experimental results verify that BILSTM-GRU combined model has higher prediction accuracy and shorter prediction time, which provides an important scientific basis for automatic expansion and shrinkage of cloud computing containers using the prediction results of resource usage.","PeriodicalId":239996,"journal":{"name":"2022 IEEE International Conference on Joint Cloud Computing (JCC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134490635","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 : 2022-08-01DOI: 10.1109/jcc56315.2022.00005
{"title":"Welcome Message from the General Chairs of IEEE JCC 2022","authors":"","doi":"10.1109/jcc56315.2022.00005","DOIUrl":"https://doi.org/10.1109/jcc56315.2022.00005","url":null,"abstract":"","PeriodicalId":239996,"journal":{"name":"2022 IEEE International Conference on Joint Cloud Computing (JCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131242449","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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