Yao, Zhiyuan; Desmouceaux, Yoann; Cordero, Juan Antonio; Townsley, Mark; Clausen, Thomas Heide
Aquarius-Enable Fast, Scalable, Data-Driven Service Management in the Cloud Journal Article
In: IEEE Transactions on Network and Service Management, 2022, ISSN: 1932-4537.
@article{nokeyi,
title = {Aquarius-Enable Fast, Scalable, Data-Driven Service Management in the Cloud},
author = {Zhiyuan Yao and Yoann Desmouceaux and Juan Antonio Cordero and Mark Townsley and Thomas Heide Clausen},
url = {https://ieeexplore.ieee.org/abstract/document/9852806},
doi = {10.1109/TNSM.2022.3197130},
issn = {1932-4537},
year = {2022},
date = {2022-12-01},
urldate = {2022-12-01},
journal = {IEEE Transactions on Network and Service Management},
abstract = {In order to dynamically manage and update networking policies in cloud data centers, Virtual Network Functions (VNFs) use, and therefore actively collect, networking state information -and in the process, incur additional control signaling and management overhead, especially in larger data centers. In the meantime, VNFs in production prefer distributed and straightforward heuristics over advanced learning algorithms to avoid intractable additional processing latency under high-performance and low-latency networking constraints. This paper identifies the challenges of deploying learning algorithms in the context of cloud data centers, and proposes Aquarius to bridge the application of machine learning (ML) techniques on distributed systems and service management. Aquarius passively yet efficiently gathers reliable observations, and enables the use of ML techniques to collect, infer, and supply accurate networking state information -without incurring additional signaling and management overhead. It offers fine-grained and programmable visibility to distributed VNFs, and enables both open-and close-loop control over networking systems. 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 ML paradigms -unsupervised, supervised, and reinforcement learning, within Aquarius, for network state inference and service management. Testbed evaluations show that Aquarius suitably improves network state visibility and brings notable performance gains for various scenarios with low overhead.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In order to dynamically manage and update networking policies in cloud data centers, Virtual Network Functions (VNFs) use, and therefore actively collect, networking state information -and in the process, incur additional control signaling and management overhead, especially in larger data centers. In the meantime, VNFs in production prefer distributed and straightforward heuristics over advanced learning algorithms to avoid intractable additional processing latency under high-performance and low-latency networking constraints. This paper identifies the challenges of deploying learning algorithms in the context of cloud data centers, and proposes Aquarius to bridge the application of machine learning (ML) techniques on distributed systems and service management. Aquarius passively yet efficiently gathers reliable observations, and enables the use of ML techniques to collect, infer, and supply accurate networking state information -without incurring additional signaling and management overhead. It offers fine-grained and programmable visibility to distributed VNFs, and enables both open-and close-loop control over networking systems. 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 ML paradigms -unsupervised, supervised, and reinforcement learning, within Aquarius, for network state inference and service management. Testbed evaluations show that Aquarius suitably improves network state visibility and brings notable performance gains for various scenarios with low overhead.
Yao, Zhiyuan; Desmouceaux, Yoann; Cordero, Juan Antonio; Townsley, Mark; Clausen, Thomas Heide
Efficient Data-Driven Network Functions Proceedings Article
In: 30th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2022), 2022.
@inproceedings{nokeyg,
title = {Efficient Data-Driven Network Functions},
author = {Zhiyuan Yao and Yoann Desmouceaux and Juan Antonio Cordero and Mark Townsley and Thomas Heide Clausen},
url = {https://arxiv.org/pdf/2208.11385},
year = {2022},
date = {2022-10-18},
urldate = {2022-10-18},
booktitle = {30th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2022)},
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 becuase 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 signalling and management overhead. This paper illustrates the use of Aquarius with a traffic classifier, an autoscaling 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.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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 becuase 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 signalling and management overhead. This paper illustrates the use of Aquarius with a traffic classifier, an autoscaling 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.