Attributed Graph Rewriting for Complex Event Processing Self-Management

Citation data:

ACM Transactions on Autonomous and Adaptive Systems, ISSN: 1556-4665, Vol: 11, Issue: 3, Page: 1-39

Publication Year:
2016
Usage 34
Full Text Views 18
Abstract Views 13
Link-outs 3
Captures 20
Readers 20
Social Media 37
Shares, Likes & Comments 37
Citations 1
Citation Indexes 1
Repository URL:
https://ir.lib.uwo.ca/electricalpub/103
DOI:
10.1145/2967499
Author(s):
Higashino, Wilson A.; Eichler, Cédric; Capretz, Miriam A. M.; Bittencourt, Luiz F.; Monteil, Thierry
Publisher(s):
Association for Computing Machinery (ACM)
Tags:
Engineering; Computer Science; Complex event processing; autonomic computing; self-management; attributed graph; graph rewriting; Computer Engineering; Databases and Information Systems; Electrical and Computer Engineering
article description
The use of Complex Event Processing (CEP) and Stream Processing (SP) systems to process high-volume, high-velocity Big Data has renewed interest in procedures for managing these systems. In particular, selfmanagement and adaptation of runtime platforms have been common research themes, as most of these systems run under dynamic conditions. Nevertheless, the research landscape in this area is still young and fragmented. Most research is performed in the context of specific systems, and it is difficult to generalize the results obtained to other contexts. To enable generic and reusable CEP/SP system management procedures and self-management policies, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management (AGeCEP) formalism. AGeCEP represents queries in a language- and technologyagnostic fashion using attributed graphs. Query reconfiguration capabilities are expressed through standardized attributes, which are defined based on a novel classification of CEP query operators. By leveraging this representation, AGeCEP also proposes graph rewriting rules to define consistent reconfigurations of queries. To demonstrate AGeCEP feasibility, this research has used it to design an autonomic manager and to define a selected set of self-management policies. Finally, experiments demonstrate that AGeCEP can indeed be used to develop algorithms that can be integrated into diverse CEP systems.