TY - GEN
T1 - Online Container Scheduling for Data-intensive Applications in Serverless Edge Computing
AU - Shang, Xiaojun
AU - Mao, Yingling
AU - Liu, Yu
AU - Huang, Yaodong
AU - Liu, Zhenhua
AU - Yang, Yuanyuan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Introducing the emerging serverless paradigm into edge computing could avoid over- and under-provisioning of limited edge resources and make complex edge resource management transparent to application developers, which largely facilitates the cost-effectiveness, portability, and short time-to-market of edge applications. However, the computation/data dispersion and device/network heterogeneity of edge environments prevent current serverless computing platforms from acclimating to the network edge. In this paper, we address such challenges by formulating a container placement and data flow routing problem, which fully considers the heterogeneity of edge networks and the overhead of operating serverless platforms on resource-limited edge servers. We design an online algorithm to solve the problem. We further show its local optimum for each arriving container and prove its theoretical guarantee to the optimal offline solution. We also conduct extensive simulations based on practical experiment results to show the advantages of the proposed algorithm over existing baselines.
AB - Introducing the emerging serverless paradigm into edge computing could avoid over- and under-provisioning of limited edge resources and make complex edge resource management transparent to application developers, which largely facilitates the cost-effectiveness, portability, and short time-to-market of edge applications. However, the computation/data dispersion and device/network heterogeneity of edge environments prevent current serverless computing platforms from acclimating to the network edge. In this paper, we address such challenges by formulating a container placement and data flow routing problem, which fully considers the heterogeneity of edge networks and the overhead of operating serverless platforms on resource-limited edge servers. We design an online algorithm to solve the problem. We further show its local optimum for each arriving container and prove its theoretical guarantee to the optimal offline solution. We also conduct extensive simulations based on practical experiment results to show the advantages of the proposed algorithm over existing baselines.
UR - https://www.scopus.com/pages/publications/85171630180
U2 - 10.1109/INFOCOM53939.2023.10229034
DO - 10.1109/INFOCOM53939.2023.10229034
M3 - Conference contribution
AN - SCOPUS:85171630180
T3 - Proceedings - IEEE INFOCOM
BT - INFOCOM 2023 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd IEEE International Conference on Computer Communications, INFOCOM 2023
Y2 - 17 May 2023 through 20 May 2023
ER -