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.},
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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.
Herberg, Ulrich; Clausen, Thomas; Cole, Robert G.
MANET Network Management and Performance Monitoring for NHDP and OLSRv2 Proceedings Article
In: Proceedings of the 6th International Conference on Network and Services Management, 2010.
@inproceedings{Herberg2010,
title = {MANET Network Management and Performance Monitoring for NHDP and OLSRv2},
author = {Ulrich Herberg and Thomas Clausen and Robert G. Cole},
url = {http://www.thomasclausen.net/wp-content/uploads/2015/12/2010-CNSM-MANET-Network-Management-and-Performance-Monitoring-for-NHDP-and-OLSRv2.pdf},
doi = {10.1109/CNSM.2010.5691209},
year = {2010},
date = {2010-10-01},
publisher = {Proceedings of the 6th International Conference on Network and Services Management},
abstract = {Mobile Ad Hoc NETworks (MANETs) are gener-ally thought of as infrastructureless and largely “un-managed” network deployments, capable of accommodating highly dynamic network topologies. Yet, while the network infrastructure may be “un-managed”, monitoring the network performance and setting configuration parameters once deployed, remains important in order to ensure proper “tuning” and maintenance of a MANET. This paper describes a management framework for the MANET routing protocol OLSRv2, and its constituent protocol NHDP. It does so by presenting considerations for “what to monitor and manage” in an OLSRv2 network, and how. The approach developed is based on the Simple Network Management Protocol (SNMP), and thus this paper details the various Management Information Bases (MIBs) for router status monitoring and control – as well as a novel approach to history-based perfor-mance monitoring. While SNMP may not be optimally designed for MANETs, it is chosen due to it being the predominant protocol for IP network management – and thus, efforts are made in this paper to “adapt” the management tools within the SNMP framework for reasonable behavior also in a MANET environment.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Mobile Ad Hoc NETworks (MANETs) are gener-ally thought of as infrastructureless and largely “un-managed” network deployments, capable of accommodating highly dynamic network topologies. Yet, while the network infrastructure may be “un-managed”, monitoring the network performance and setting configuration parameters once deployed, remains important in order to ensure proper “tuning” and maintenance of a MANET. This paper describes a management framework for the MANET routing protocol OLSRv2, and its constituent protocol NHDP. It does so by presenting considerations for “what to monitor and manage” in an OLSRv2 network, and how. The approach developed is based on the Simple Network Management Protocol (SNMP), and thus this paper details the various Management Information Bases (MIBs) for router status monitoring and control – as well as a novel approach to history-based perfor-mance monitoring. While SNMP may not be optimally designed for MANETs, it is chosen due to it being the predominant protocol for IP network management – and thus, efforts are made in this paper to “adapt” the management tools within the SNMP framework for reasonable behavior also in a MANET environment.
Clausen, Thomas; Herberg, Ulrich
MANET Network Management and Performance Monitoring for NHDP and OLSRv2 Technical Report
2010.
@techreport{Clausen2010bbd,
title = {MANET Network Management and Performance Monitoring for NHDP and OLSRv2},
author = {Thomas Clausen and Ulrich Herberg},
year = {2010},
date = {2010-06-01},
publisher = {INRIA Research Report 7311},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}