Paper Info
Title
An Optimal Model for Optimizing the Placement and Parallelism of Data Stream Processing Applications on Cloud-Edge Computing
Abstract
The Internet of Things has enabled many application scenarios where a large number of connected devices generate unbounded streams of data, often processed by data stream processing frameworks deployed in the cloud. Edge computing enables offloading processing from the cloud and placing it close to where the data is generated, thereby reducing the time to process data events and deployment costs. However, edge resources are more computationally constrained than their cloud counterparts, raising two interrelated issues, namely deciding on the parallelism of processing tasks (a.k.a. operators) and their mapping onto available resources. In this work, we formulate the scenario of operator placement and parallelism as an optimal mixed-integer linear programming problem. The proposed model is termed as Cloud-Edge data Stream Placement (CESP). Experimental results using discrete-event simulation demonstrate that CESP can achieve an end-to-end latency at least ≃ 80% and monetary costs at least ≃ 30% better than traditional cloud deployment.
Year
DOI
Venue
2020
10.1109/SBAC-PAD49847.2020.00019
2020 IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)
Keywords
DocType
ISSN
Data Stream Processing, Operator Placement, Operator Parallelism, End-to-end Latency, Edge Computing
Conference
1550-6533
ISBN
Citations 
PageRank 
978-1-7281-9925-2
0
0.34
References 
Authors
13
4
Name
Order
Citations
PageRank
Felipe Rodrigo de Souza100.34
Marcos Dias de Assunção233725.23
Eddy Caron385966.80
Alexandre Da Silva Veith4342.98