中文版 | English
CSpace
Joint Operator Scaling and Placement for Distributed Stream Processing Applications in Edge Computing
Peng, Qinglan1; Xia, Yunni1; Wang, Yan2; Wu, Chunrong1; Luo, Xin3; Lee, Jia1
2019
摘要Distributed Stream Processing (DSP) systems are well acknowledged to be potent in processing huge volume of real-time stream data with low latency and high throughput. Recently, the edge computing paradigm shows great potentials in supporting and boosting the DSP applications, especially the time-critical and latency-sensitive ones, over the Internet of Things (IoT) or mobile devices by means of offloading the computation from remote cloud to edge servers for further reduced communication latencies. Nevertheless, various challenges, especially the joint operator scaling and placement, are yet to be properly explored and addressed. Traditional efforts in this direction usually assume that the data-flow graph of a DSP application is pre-given and static. The resulting models and methods can thus be ineffective and show bad user-perceived quality-of-service (QoS) when dealing with real-world scenarios with reconfigurable data-flow graphs and scalable operator placement. In contrast, in this paper, we consider that the data-flow graphs are configurable and hence propose the joint operator scaling and placement problem. To address this problem, we first build a queuing-network-based QoS estimation model, then formulate the problem into an integer-programming one, and finally propose a two-stage approach for finding the near-optimal solution. Experiments based on real-world DSP test cases show that our method achieves higher cost effectiveness than traditional ones while meeting the user-defined QoS constraints. © Springer Nature Switzerland AG 2019.
语种英语
DOI10.1007/978-3-030-33702-5_36
会议(录)名称17th International Conference on Service-Oriented Computing, ICSOC 2019
页码461-476
收录类别EI
会议地点Toulouse, France
会议日期October 28, 2019 - October 31, 2019