Bandit-based Delay-Aware Service Function Chain Orchestration at the Edge
| dc.contributor.advisor | Ghaderi, Majid | |
| dc.contributor.author | Wang, Lei | |
| dc.contributor.committeemember | Krishnamurthy, Diwakar | |
| dc.contributor.committeemember | Safavi-Naini, Rei | |
| dc.date | 2021-06 | |
| dc.date.accessioned | 2021-04-22T16:49:21Z | |
| dc.date.available | 2021-04-22T16:49:21Z | |
| dc.date.issued | 2021-04-21 | |
| dc.description.abstract | Mobile Edge Computing (MEC) enables both cloud computing and edge computing for mobile users, providing them with intensive computing resources and proximity to the data sources. When combined with network function virtualization (NFV), MEC provides users with promising end-to-end latency and management for mobile applications that requires multiple computing resources. Such applications are often handled in a fashion of service function chain (SFC), which designates a sequence of virtual network functions (VNF) for users’ traffic to traverse in order to realize their network application. In order to provide the user a tolerated perceived latency for a SFC-based application, many existing works have taken aim at optimal system-wide placement for SFC in heterogeneous scenarios yet fewer works have studied user-managed placement. In this paper, we formulate the user-managed SFC placement in MEC as a contextual combinatorial multi-arm bandit (C2MAB) problem and proposed BandEdge, a bandit-based algorithm for online SFC placement on edge, which consider user’s mobility and service preference while jointly optimizing their perceived latency and service migration delay, and then propose an offline exact approach for the role of performance benchmark. To fit the SFC placement problem in a bandit framework, we model the nodes and links to be arms by viewing them as delays and selects them according to a strategy that balances exploration and exploitation. Finally, we evaluate the proposed algorithm in extensive simulation and Mininet-WiFi emulation experiments, numeric simulation results show that the proposed algorithm can achieve close-to-optimum performance and outperform the greedy learning algorithms by at least 50 percent in terms of scalability. We further validate the superior performance of our proposed method in Mininet-WiFi emulation under different environmental parameters. | en_US |
| dc.identifier.citation | Wang, L. (2021). Bandit-based Delay-Aware Service Function Chain Orchestration at the Edge (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/38760 | |
| dc.identifier.uri | http://hdl.handle.net/1880/113276 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Science | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject | Mobile-edge computing, service function chain placement, online learning | en_US |
| dc.subject.classification | Computer Science | en_US |
| dc.title | Bandit-based Delay-Aware Service Function Chain Orchestration at the Edge | en_US |
| dc.type | master thesis | en_US |
| thesis.degree.discipline | Computer Science | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Master of Science (MSc) | en_US |
| ucalgary.item.requestcopy | true | en_US |