ZTE Communications ›› 2021, Vol. 19 ›› Issue (2): 11-19.DOI: 10.12142/ZTECOM.202102003

• Special Topic • Previous Articles     Next Articles

Cost-Effective Task Scheduling for Collaborative Cross-Edge Analytics

ZHAO Kongyange1, GAO Bin2, ZHOU Zhi1()   

  1. 1.Sun Yat-sen University, Guangzhou 510275, China
    2.National University of Singapore, Singapore 119077, Singapore
  • Received:2021-04-09 Online:2021-06-25 Published:2021-07-27
  • About author:ZHAO Kongyange received the B.E. degree from the South China University of Technology, China in 2020. He is currently pursuing his master’s degree in Sun Yat-sen University, China. His research interests include edge computing, edge intelligence, and serverless computing.|GAO Bin is now a research assistant of School of Computing in National University of Singapore (NUS). Before this, he received the master’s degree and bachelor’s degree from Huazhong University of Science and Technology (HUST), China in 2017 and 2020, respectively. His research interests include operation system, mobile edge computing, cloud computing, and geo-distributed data analytics.|ZHOU Zhi (zhouzhi9@mail.sysu.edu.cn) received the B.S., M.E., and Ph.D. degrees in 2012, 2014, and 2017, respectively, all from the School of Computer Science and Technology at Huazhong University of Science and Technology (HUST), China. He is currently an associate professor in the School of Computer Science and Engineering at Sun Yat-sen University, China. In 2016, he was a visiting scholar at University of Goettingen, Germany. He was nominated for the 2019 CCF Outstanding Doctoral Dissertation Award, the sole recipient of the 2018 ACM Wuhan & Hubei Computer Society Doctoral Dissertation Award, and a recipient of the Best Paper Award of IEEE UIC 2018. His research interests include edge computing, cloud computing, and distributed systems.
  • Supported by:
    the National Natural Science Foundation of China(61802449);the Guangdong Natural Science Funds(2021A1515011912)


Collaborative cross-edge analytics is a new computing paradigm in which Internet of Things (IoT) data analytics is performed across multiple geographically dispersed edge clouds. Existing work on collaborative cross-edge analytics mostly focuses on reducing either analytics response time or wide-area network (WAN) traffic volume. In this work, we empirically demonstrate that reducing either analytics response time or network traffic volume does not necessarily minimize the WAN traffic cost, due to the price heterogeneity of WAN links. To explicitly leverage the price heterogeneity for WAN cost minimization, we propose to schedule analytic tasks based on both price and bandwidth heterogeneities. Unfortunately, the problem of WAN cost minimization underperformance constraint is shown non-deterministic polynomial (NP)-hard and thus computationally intractable for large inputs. To address this challenge, we propose price- and performance-aware geo-distributed analytics (PPGA) , an efficient task scheduling heuristic that improves the cost-efficiency of IoT data analytic jobs across edge datacenters. We implement PPGA based on Apache Spark and conduct extensive experiments on Amazon EC2 to verify the efficacy of PPGA.

Key words: collaborative cross-edge analytics, Internet of Things, task scheduling