Risk-sensitive task fetching and offloading for vehicular edge computing
Batewela, Sadeep; Liu, Chen-Feng; Bennis, Mehdi; Suraweera, Himal A.; Hong, Choong Seon (2019-12-19)
S. Batewela, C. Liu, M. Bennis, H. A. Suraweera and C. S. Hong, "Risk-Sensitive Task Fetching and Offloading for Vehicular Edge Computing," in IEEE Communications Letters, vol. 24, no. 3, pp. 617-621, March 2020.
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe202002195812
Tiivistelmä
Abstract
This letter studies an ultra-reliable low latency communication problem focusing on a vehicular edge computing network in which vehicles either fetch and synthesize images recorded by surveillance cameras or acquire the synthesized image from an edge computing server. The notion of risk-sensitive in financial mathematics is leveraged to define a reliability measure, and the studied problem is formulated as a risk minimization problem for each vehicle’s end-to-end (E2E) task fetching and offloading delays. Specifically, by resorting to a joint utility and policy estimation-based learning algorithm, a distributed risk-sensitive solution for task fetching and offloading is proposed. Simulation results show that our proposed solution achieves performance improvements up to 40% variance reduction and steeper distribution tail of the E2E delay over an averaged-based baseline.
Kokoelmat
- Avoin saatavuus [37647]