Repeated auction mechanisms for multi-access edge computing
| dc.contributor.author | Habiba, Ummy | |
| dc.contributor.examiningcommittee | Eskicioglu, Rasit (Computer Science) | en_US |
| dc.contributor.examiningcommittee | McLeod, Robert D. (Electrical and Computer Engineering) | en_US |
| dc.contributor.supervisor | Hossain, Ekram | |
| dc.date.accessioned | 2023-01-11T18:58:04Z | |
| dc.date.available | 2023-01-11T18:58:04Z | |
| dc.date.copyright | 2022-12-23 | |
| dc.date.issued | 2022-12-16 | |
| dc.date.submitted | 2022-12-23T16:55:02Z | en_US |
| dc.degree.discipline | Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Mobile edge computing (MEC) is one of the promising technologies that ensures high data rate and ultra low service latency with the computational capabilities at the edge of 5G wireless/cellular networks and beyond. Due to the scarcity of the computing resources at the edge servers, it is crucial to develop an efficient resource allocation mechanism that benefits both the resource sellers and offloading mobile users with heterogeneous QoE requirements. Despite extensive studies on MEC offloading, existing research lacks efficient resource allocation mechanism addressing the stochastic nature in resource demands as well as computational capacities of the servers. Besides, there is a significant research gap reflecting the economical efficiency in a computation offloading service market, which supports the growing market size of MEC-enabled applications, and the competition among different business rivals, e.g., mobile network operator, wireless or computing service providers, etc. In a competitive market scenario, the auction game theory has been widely popular for designing efficient resource allocation mechanisms, as it particularly focuses on regulating the strategic interactions among the self-interested players. In this thesis, I investigate auction-based approaches to model a dynamic MEC offloading service market model, addressing the resource allocation problem with the goal of ensuring consistent QoE for offloading users as well as maximizing the auction revenue. To achieve this research goal, I develop repeated auction mechanism considering the network dynamics in computation offloading, and design a novel generalized second price (GSP) mechanism to obtain efficient offloading task assignment and resource allocation pricing decisions. Furthermore, I study adaptive best-response bidding strategies that maximize the profits of the resource sellers, and guarantee the stability and effectiveness of the auction by satisfying desired economic properties. To this end, I validate the performance of the proposed repeated auction mechanisms and bidding strategies through numerical result analysis. | en_US |
| dc.description.note | February 2023 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/37087 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | MEC | en_US |
| dc.subject | Edge Computing | en_US |
| dc.subject | Auction | en_US |
| dc.subject | Computation Offloading | en_US |
| dc.subject | Dynamic Auction | en_US |
| dc.title | Repeated auction mechanisms for multi-access edge computing | en_US |
| dc.type | master thesis | en_US |
| local.subject.manitoba | no | en_US |
| oaire.awardTitle | University of Manitoba Graduate Fellowship | en_US |
| project.funder.identifier | https://doi.org/10.13039/100010318 | en_US |
| project.funder.name | University of Manitoba | en_US |