TY - GEN
T1 - Predictive online server provisioning for cost-efficient IoT data streaming across collaborative edges
AU - Zhou, Zhi
AU - Chen, Xu
AU - Wu, Weigang
AU - Wu, Di
AU - Zhang, Junshan
N1 - Funding Information:
This work was supported in part by the National Science Foundation of China under Grants U1711265 and 61802449, the National Key Research & Development (R&D) Plan under grant 2017YFB1001703, the Program for Guangdong Introducing Innovative and Enterpreneurial Teams under Grant 2017ZT07X355, the Guangdong Special Support Program under Grant 2017TX04X148, and the Guangdong Natural Science Funds under Grant 2018A030313032. The corresponding author is Xu Chen.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7/2
Y1 - 2019/7/2
N2 - Edge computing is envisioned to be the de-facto paradigm of hosting emerging low latency Internet-of-Things (IoT) data streaming services.For IoT data streaming in edge computing, cost management is of strategic significance, due to the low cost-efficiency of edge servers. While existing literature adopts a reactive approach to dynamically provisioning edge servers to reduce cost, the delay of server activation and instantiation has been mostly ignored. In this paper, we target a proactive approach to dynamic edge server provisioning for real-time IoT data streaming across edge nodes, which adjusts server provisioning ahead of time, based on prediction of the upcoming workload. To effectively predict upcoming workload, a learning-based method online gradient descent is applied. We further combine the online learning method with an online optimization algorithm for server provisioning in a joint online optimization framework, through (1) minimizing of the regret incurred by inaccurate workload prediction, and (2) minimizing the cost incurred by near-optimal online decisions. The resulting predictive online algorithm can well leverage the power of prediction and achieve a good performance guarantee, as verified by both rigorous theoretical analysis and extensive trace-driven evaluations.
AB - Edge computing is envisioned to be the de-facto paradigm of hosting emerging low latency Internet-of-Things (IoT) data streaming services.For IoT data streaming in edge computing, cost management is of strategic significance, due to the low cost-efficiency of edge servers. While existing literature adopts a reactive approach to dynamically provisioning edge servers to reduce cost, the delay of server activation and instantiation has been mostly ignored. In this paper, we target a proactive approach to dynamic edge server provisioning for real-time IoT data streaming across edge nodes, which adjusts server provisioning ahead of time, based on prediction of the upcoming workload. To effectively predict upcoming workload, a learning-based method online gradient descent is applied. We further combine the online learning method with an online optimization algorithm for server provisioning in a joint online optimization framework, through (1) minimizing of the regret incurred by inaccurate workload prediction, and (2) minimizing the cost incurred by near-optimal online decisions. The resulting predictive online algorithm can well leverage the power of prediction and achieve a good performance guarantee, as verified by both rigorous theoretical analysis and extensive trace-driven evaluations.
KW - Edge computing
KW - IoT data streaming
KW - Online learning
KW - Online optimization
KW - Server provisioning
UR - http://www.scopus.com/inward/record.url?scp=85069786930&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069786930&partnerID=8YFLogxK
U2 - 10.1145/3323679.3326530
DO - 10.1145/3323679.3326530
M3 - Conference contribution
AN - SCOPUS:85069786930
T3 - Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)
SP - 321
EP - 330
BT - Mobihoc 2019 - Proceedings of the 2019 20th ACM International Symposium on Mobile Ad Hoc Networking and Computing
PB - Association for Computing Machinery
T2 - 20th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2019
Y2 - 2 July 2019 through 5 July 2019
ER -