@inproceedings{24b1318ad48645229f32d39f2c81351a,
title = "Scalable Resource Allocation Techniques for Edge Computing Systems",
abstract = "Edge computing has become a very popular service that enables mobile devices to run complex tasks with the help of network-based computing resources. However, edge clouds are often resource-constrained, which makes resource allocation a challenging issue. We focus on a distributed resource allocation method in which servers operate independently and do not communicate with each other, but interact with clients (tasks) to make allocation decisions. This provides robustness and does not require service providers to share information about their configurations or workloads. We utilize a two-round bidding approach of assigning tasks to edge cloud servers. We consider a preemption-enabled system in which servers may stop a previous task in order to run a more useful one. We evaluate the performance of our system using realistic simulations and real-world trace data from a high-performance computing cluster. Results show that our approach is reasonably close to optimal assignment, while saving 50-70 \% of the original computation time.",
author = "Caroline Rublein and Fidan Mehmeti and Gunes, \{Taha D.\} and Sebastian Stein and \{La Porta\}, \{Thomas F.\}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 31st International Conference on Computer Communications and Networks, ICCCN 2022 ; Conference date: 25-07-2022 Through 27-07-2022",
year = "2022",
doi = "10.1109/ICCCN54977.2022.9868909",
language = "English (US)",
series = "Proceedings - International Conference on Computer Communications and Networks, ICCCN",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICCCN 2022 - 31st International Conference on Computer Communications and Networks",
address = "United States",
}