Paper Info
Title
Efficient Operator Placement for Distributed Data Stream Processing Applications
Abstract
In the last few years, a large number of real-time analytics applications rely on the Data Stream Processing (DSP) so to extract, in a timely manner, valuable information from distributed sources. Moreover, to efficiently handle the increasing amount of data, recent trends exploit the emerging presence of edge/Fog computing resources so to decentralize the execution of DSP applications. Since determining the Optimal DSP Placement (for short, ODP) is an NP-hard problem, we need efficient heuristics that can identify a good application placement on the computing infrastructure in a feasible amount of time, even for large problem instances. In this paper, we present several DSP placement heuristics that consider the heterogeneity of computing and network resources; we divide them in two main groups: model-based and model-free. The former employ different strategies for efficiently solving the ODP model. The latter implement, for the problem at hand, some of the well-known meta-heuristics, namely greedy first-fit, local search, and tabu search. By leveraging on ODP, we conduct a thorough experimental evaluation, aimed to assess the heuristics’ efficiency and efficacy under different configurations of infrastructure size, application topology, and optimization objectives.
Year
DOI
Venue
2019
10.1109/tpds.2019.2896115
IEEE Transactions on Parallel and Distributed Systems
Keywords
Field
DocType
Computational modeling,Quality of service,Search problems,Delays,Optimization,Storms
Digital signal processing,Computer science,Quality of service,Exploit,Heuristics,Operator (computer programming),Local search (optimization),Analytics,Tabu search,Distributed computing
Journal
Volume
Issue
ISSN
30
8
1045-9219
Citations 
PageRank 
References 
2
0.37
0
Authors
4
Name
Order
Citations
PageRank
Matteo Nardelli1777.95
Valeria Cardellini21514106.12
Vincenzo Grassi3174681.24
Francesco Lo Presti4107378.83