Zhiyuan Yao, Yoann Desmouceaux, Juan Antonio Cordero, M. Townsley, T. Clausen
{"title":"Efficient Data-Driven Network Functions","authors":"Zhiyuan Yao, Yoann Desmouceaux, Juan Antonio Cordero, M. Townsley, T. Clausen","doi":"10.1109/MASCOTS56607.2022.00028","DOIUrl":"https://doi.org/10.1109/MASCOTS56607.2022.00028","url":null,"abstract":"Cloud environments require dynamic and adaptive networking policies. It is preferred to use heuristics over advanced learning algorithms in Virtual Network Functions (VNFs) in production because of high-performance constraints. This paper proposes Aquarius to passively yet efficiently gather observations and enable the use of machine learning to collect, infer, and supply accurate networking state information - without incurring additional signaling and management overhead. This paper illustrates the use of Aquarius with a traffic classifier, an auto-scaling system, and a load balancer - and demonstrates the use of three different machine learning paradigms - unsupervised, supervised, and reinforcement learning, within Aquarius, for inferring network state. Testbed evaluations show that Aquarius increases network state visibility and brings notable performance gains with low overhead.","PeriodicalId":220026,"journal":{"name":"2022 30th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129885234","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}
K. R. Jayaram, Ashish Verma, Gegi Thomas, Vinod Muthusamy
{"title":"Just-in-Time Aggregation for Federated Learning","authors":"K. R. Jayaram, Ashish Verma, Gegi Thomas, Vinod Muthusamy","doi":"10.1109/MASCOTS56607.2022.00009","DOIUrl":"https://doi.org/10.1109/MASCOTS56607.2022.00009","url":null,"abstract":"The increasing number and scale of federated learning (FL) jobs necessitates resource efficient scheduling and management of aggregation to make the economics of cloud-hosted aggregation work. Existing FL research has focused on the design of FL algorithms and optimization, and less on aggregation efficacy. In this paper, we propose a new FL aggregation paradigm - “just-in-time” (JIT) aggregation that leverages unique properties of FL jobs, especially the periodicity of model updates, to defer aggregation as much as possible and free compute resources for other FL jobs or other datacenter workloads. We describe a novel way to prioritize FL jobs for aggregation, and demonstrate using multiple datasets, models and FL aggregation algorithms that our techniques can reduce resource usage by 60+% when compared to eager aggregation used in existing FL platforms. We demonstrate that using JIT aggregation has negligible overhead and impact on the latency of the FL job.","PeriodicalId":220026,"journal":{"name":"2022 30th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)","volume":"285 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121829462","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}
{"title":"NorBERT: NetwOrk Representations Through BERT for Network Analysis & Management","authors":"Franck Le, Davis Wertheimer, S. Calo, E. Nahum","doi":"10.1109/MASCOTS56607.2022.00012","DOIUrl":"https://doi.org/10.1109/MASCOTS56607.2022.00012","url":null,"abstract":"Deep neural network models have been very successfully applied to Natural Language Processing (NLP) and Image based tasks. Their application to network analysis and management tasks is just recently being pursued. Our interest is in producing deep models that can be effectively generalized to perform well on multiple network tasks in different environments. A major challenge is that traditional deep models often rely on categorical features, but cannot handle unseen categorical values. One method for dealing with such problems is to learn contextual embeddings for categorical variables used by deep networks to improve their performance. In this paper, we adapt the NLP pre-training technique and associated deep model BERT to learn semantically meaningful numerical representations (embeddings) for Fully Qualified Domain Names (FQDNs) used in communication networks. We show through a series of experiments that such an approach can be used to generate models that maintain their effectiveness when applied to environments other than the one in which they were trained.","PeriodicalId":220026,"journal":{"name":"2022 30th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128316588","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}