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
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 -