At the end of last year, TK researchers presented their paper titled “BigMEC: Scalable Service Migration for Mobile Edge Computing” at the Symposium on Edge Computing. The paper presents an innovative service displacement mechanism that substantially increases the decision quality of decentralized service placement for mobile edge computing. This displacement mechanism is integrated with reinforcement learning to provide adaptivity and proactive decision making at scale.
Florian Brandherm, Julien Gedeon, Osama Abboud, and Max Mühlhäuser. 2022. BigMEC: Scalable Service Migration for Mobile Edge Computing. In 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC), IEEE, 136–148. DOI: https://doi.org/10.1109/SEC54971.2022.00018
Also at the end of last year, TK researchers presented their paper titled “Distributed DNN serving in the network data plane” at EuroP4. In this work, they extended in-network computing to an important class of applications called deep neural network (DNN) serving. In particular, they proposed to run DNN inferences in the network data plane in a distributed fashion and make a programmable network a powerful accelerator for DNN serving.
Kamran Razavi, George Karlos, Vinod Nigade, Max Mühlhäuser, and Lin Wang. 2022. Distributed DNN serving in the network data plane. In Proceedings of the 5th International Workshop on P4 in Europe (EuroP4 ’22), Association for Computing Machinery, 67–70. DOI: https://doi.org/10.1145/3565475.3569079