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The P-ART framework for placement of virtual network services in a multi-cloud environment
Washington Univ, Dept Comp Sci & Engn, St Louis, MO 63110 USA..
Washington Univ, Dept Comp Sci & Engn, St Louis, MO 63110 USA..
Qatar Univ, Dept Comp Sci & Engn, Doha, Qatar..
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). Karlstad Univ, Dept Math & Comp Sci, Karlstad, Sweden..
2019 (English)In: Computer Communications, ISSN 0140-3664, E-ISSN 1873-703X, Vol. 139, p. 103-122Article in journal (Refereed) Published
Abstract [en]

Carriers' network services are distributed, dynamic, and investment intensive. Deploying them as virtual network services (VNS) brings the promise of low-cost agile deployments, which reduce time to market new services. If these virtual services are hosted dynamically over multiple clouds, greater flexibility in optimizing performance and cost can be achieved. On the flip side, when orchestrated over multiple clouds, the stringent performance norms for carrier services become difficult to meet, necessitating novel and innovative placement strategies. In selecting the appropriate combination of clouds for placement, it is important to look ahead and visualize the environment that will exist at the time a virtual network service is actually activated. This serves multiple purposes - clouds can be selected to optimize the cost, the chosen performance parameters can be kept within the defined limits, and the speed of placement can be increased. In this paper, we propose the P-ART (Predictive-Adaptive Real Time) framework that relies on predictive-deductive features to achieve these objectives. With so much riding on predictions, we include in our framework a novel concept-drift compensation technique to make the predictions closer to reality by taking care of long-term traffic variations. At the same time, near real-time update of the prediction models takes care of sudden short-term variations. These predictions are then used by a new randomized placement heuristic that carries out a fast cloud selection using a least-cost latency-constrained policy. An empirical analysis carried out using datasets from a queuing-theoretic model and also through implementation on CloudLab, proves the effectiveness of the PART framework. The placement system works fast, placing thousands of functions in a sub-minute time frame with a high acceptance ratio, making it suitable for dynamic placement. We expect the framework to be an important step in making the deployment of carrier-grade VNS on multi-cloud systems, using network function virtualization (NFV), a reality.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2019. Vol. 139, p. 103-122
Keywords [en]
Virtual network services, Network function virtualization, Service function chain, Virtual network function, Multi-cloud systems, Machine learning, Dynamic placement
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-72408DOI: 10.1016/j.comcom.2019.03.003ISI: 000468709900008OAI: oai:DiVA.org:kau-72408DiVA, id: diva2:1323126
Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-06-28Bibliographically approved

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Bhamare, Deval

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