{"title":"虚拟执行环境中的参与者分析","authors":"Andrea Rosà, L. Chen, Walter Binder","doi":"10.1145/2993236.2993241","DOIUrl":null,"url":null,"abstract":"Nowadays, many virtual execution environments benefit from concurrency offered by the actor model. Unfortunately, while actors are used in many applications, existing profiling tools are not much effective in analyzing the performance of applications using actors. In this paper, we present a new instrumentation-based technique to profile actors in virtual execution environments. Our technique adopts platform-independent profiling metrics that minimize the perturbations induced by the instrumentation logic and allow comparing profiling results across different platforms. In particular, our technique measures the initialization cost, the amount of executed computations, and the messages sent and received by each actor. We implement our technique within a profiling tool for Akka actors on the Java platform. Evaluation results show that our profiling technique helps performance analysis of actor utilization and communication between actors in large-scale computing frameworks.","PeriodicalId":405898,"journal":{"name":"Proceedings of the 2016 ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Actor profiling in virtual execution environments\",\"authors\":\"Andrea Rosà, L. Chen, Walter Binder\",\"doi\":\"10.1145/2993236.2993241\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, many virtual execution environments benefit from concurrency offered by the actor model. Unfortunately, while actors are used in many applications, existing profiling tools are not much effective in analyzing the performance of applications using actors. In this paper, we present a new instrumentation-based technique to profile actors in virtual execution environments. Our technique adopts platform-independent profiling metrics that minimize the perturbations induced by the instrumentation logic and allow comparing profiling results across different platforms. In particular, our technique measures the initialization cost, the amount of executed computations, and the messages sent and received by each actor. We implement our technique within a profiling tool for Akka actors on the Java platform. Evaluation results show that our profiling technique helps performance analysis of actor utilization and communication between actors in large-scale computing frameworks.\",\"PeriodicalId\":405898,\"journal\":{\"name\":\"Proceedings of the 2016 ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2016 ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2993236.2993241\",\"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 2016 ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2993236.2993241","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nowadays, many virtual execution environments benefit from concurrency offered by the actor model. Unfortunately, while actors are used in many applications, existing profiling tools are not much effective in analyzing the performance of applications using actors. In this paper, we present a new instrumentation-based technique to profile actors in virtual execution environments. Our technique adopts platform-independent profiling metrics that minimize the perturbations induced by the instrumentation logic and allow comparing profiling results across different platforms. In particular, our technique measures the initialization cost, the amount of executed computations, and the messages sent and received by each actor. We implement our technique within a profiling tool for Akka actors on the Java platform. Evaluation results show that our profiling technique helps performance analysis of actor utilization and communication between actors in large-scale computing frameworks.