{"title":"安全执行用户上传算法的框架","authors":"Toni Tan, René Weller, G. Zachmann","doi":"10.1145/3564533.3564560","DOIUrl":null,"url":null,"abstract":"In recent years, a trend has existed for an open benchmark aiming for reproducible and comparable benchmarking results. The best reproducibility can be achieved when performing the benchmarks in the same hard- and software environment. This can be offered as a web service. One challenge of such a web service is the integration of new algorithms into the existing benchmarking tool due to security concerns. In this paper, we present a framework that allows the safe execution of user-uploaded algorithms in such a benchmark-as-a-service web tool. To guarantee security as well as reproducibility and comparability of the service, we extend an existing system architecture to allow the execution of user-uploaded algorithms in a virtualization environment. Our results show that although the results from the virtualization environment are slightly slower by around 3.7% to 4.7% compared with the native environment, the results are consistent across all scenarios with different algorithms, object shapes, and object complexity. Moreover, we have automated the entire process from turning on/off a virtual machine, starting benchmark with intended parameters to communicating with the backend server when the benchmark has finished. Our implementation is based on Microsoft Hyper-V that allows us to benchmark algorithms that use Single Instruction, Multiple Data (SIMD) instruction sets as well as access to the Graphics Processing Unit (GPU).","PeriodicalId":277384,"journal":{"name":"Proceedings of the 27th International Conference on 3D Web Technology","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Framework for Safe Execution of User-Uploaded Algorithms\",\"authors\":\"Toni Tan, René Weller, G. Zachmann\",\"doi\":\"10.1145/3564533.3564560\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, a trend has existed for an open benchmark aiming for reproducible and comparable benchmarking results. The best reproducibility can be achieved when performing the benchmarks in the same hard- and software environment. This can be offered as a web service. One challenge of such a web service is the integration of new algorithms into the existing benchmarking tool due to security concerns. In this paper, we present a framework that allows the safe execution of user-uploaded algorithms in such a benchmark-as-a-service web tool. To guarantee security as well as reproducibility and comparability of the service, we extend an existing system architecture to allow the execution of user-uploaded algorithms in a virtualization environment. Our results show that although the results from the virtualization environment are slightly slower by around 3.7% to 4.7% compared with the native environment, the results are consistent across all scenarios with different algorithms, object shapes, and object complexity. Moreover, we have automated the entire process from turning on/off a virtual machine, starting benchmark with intended parameters to communicating with the backend server when the benchmark has finished. Our implementation is based on Microsoft Hyper-V that allows us to benchmark algorithms that use Single Instruction, Multiple Data (SIMD) instruction sets as well as access to the Graphics Processing Unit (GPU).\",\"PeriodicalId\":277384,\"journal\":{\"name\":\"Proceedings of the 27th International Conference on 3D Web Technology\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 27th International Conference on 3D Web Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3564533.3564560\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 27th International Conference on 3D Web Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3564533.3564560","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Framework for Safe Execution of User-Uploaded Algorithms
In recent years, a trend has existed for an open benchmark aiming for reproducible and comparable benchmarking results. The best reproducibility can be achieved when performing the benchmarks in the same hard- and software environment. This can be offered as a web service. One challenge of such a web service is the integration of new algorithms into the existing benchmarking tool due to security concerns. In this paper, we present a framework that allows the safe execution of user-uploaded algorithms in such a benchmark-as-a-service web tool. To guarantee security as well as reproducibility and comparability of the service, we extend an existing system architecture to allow the execution of user-uploaded algorithms in a virtualization environment. Our results show that although the results from the virtualization environment are slightly slower by around 3.7% to 4.7% compared with the native environment, the results are consistent across all scenarios with different algorithms, object shapes, and object complexity. Moreover, we have automated the entire process from turning on/off a virtual machine, starting benchmark with intended parameters to communicating with the backend server when the benchmark has finished. Our implementation is based on Microsoft Hyper-V that allows us to benchmark algorithms that use Single Instruction, Multiple Data (SIMD) instruction sets as well as access to the Graphics Processing Unit (GPU).