TY - GEN
T1 - EA-Market
T2 - 32nd International Conference on Computer Communications and Networks, ICCCN 2023
AU - Yu, Ruozhou
AU - Gu, Huayue
AU - Wang, Xiaojian
AU - Zhou, Fangtong
AU - Xue, Guoliang
AU - Yang, Dejun
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Edge computing promises to bring low-latency and high-throughput computing, but the limited edge resources may cause frequent congestion and lead to unstable and unpredictable performance. To ensure performance guarantee, application owners can establish Service-Level Agreements (SLAs) with the edge provider for resource reservation or priority usage. But it is cost-inefficient for application owners to lease long-term SLAs based on peak demands, as demands can fluctuate, and the leased resources may be idle or underutilized at most times. This paper studies market mechanism design for short-term edge SLA leases, focusing on real-time big data applications with throughput and latency goals. Applications submit short-term SLA requests to serve users with guaranteed performance during peak hours. As SLA requests arrive over time, the edge provider dynamically provisions edge resources to fulfill the requests, while charging application owners based on the current demands. We design EA-Market, an online combinatorial auction mechanism that achieves a competitive social welfare, while guaranteeing truthfulness, budget balance, individual rationality, and computational efficiency. Notably, our mechanism enables each application owner to bid without knowledge of the edge infrastructure, and gives edge provider full control over resource provisioning to fulfill the requests. We perform theoretical analysis and simulations to evaluate the efficacy of our mechanism.
AB - Edge computing promises to bring low-latency and high-throughput computing, but the limited edge resources may cause frequent congestion and lead to unstable and unpredictable performance. To ensure performance guarantee, application owners can establish Service-Level Agreements (SLAs) with the edge provider for resource reservation or priority usage. But it is cost-inefficient for application owners to lease long-term SLAs based on peak demands, as demands can fluctuate, and the leased resources may be idle or underutilized at most times. This paper studies market mechanism design for short-term edge SLA leases, focusing on real-time big data applications with throughput and latency goals. Applications submit short-term SLA requests to serve users with guaranteed performance during peak hours. As SLA requests arrive over time, the edge provider dynamically provisions edge resources to fulfill the requests, while charging application owners based on the current demands. We design EA-Market, an online combinatorial auction mechanism that achieves a competitive social welfare, while guaranteeing truthfulness, budget balance, individual rationality, and computational efficiency. Notably, our mechanism enables each application owner to bid without knowledge of the edge infrastructure, and gives edge provider full control over resource provisioning to fulfill the requests. We perform theoretical analysis and simulations to evaluate the efficacy of our mechanism.
KW - Competitive Analysis
KW - Edge Computing
KW - Network Economics
KW - Resource Provisioning
UR - http://www.scopus.com/inward/record.url?scp=85173577400&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85173577400&partnerID=8YFLogxK
U2 - 10.1109/ICCCN58024.2023.10230160
DO - 10.1109/ICCCN58024.2023.10230160
M3 - Conference contribution
AN - SCOPUS:85173577400
T3 - Proceedings - International Conference on Computer Communications and Networks, ICCCN
BT - ICCCN 2023 - 2023 32nd International Conference on Computer Communications and Networks
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 July 2023 through 27 July 2023
ER -