Authors:
Lucia De Marco
1
;
Filomena Ferrucci
2
;
M-Tahar Kechadi
3
;
Gennaro Napoli
2
and
Pasquale Salza
2
Affiliations:
1
University of Salerno and University College Dublin, Italy
;
2
University of Salerno, Italy
;
3
University College Dublin, Ireland
Keyword(s):
Cloud Computing, Service Level Agreements, Natural Language Processing, Information Extraction.
Related
Ontology
Subjects/Areas/Topics:
Cloud Computing
;
Cloud Computing Enabling Technology
;
Monitoring of Services, Quality of Service, Service Level Agreements
;
Service Monitoring and Control
;
Services Science
Abstract:
Service Level Agreements (SLAs) are contracts co-signed by an Application Service Provider (ASP) and the
end user(s) to regulate the services delivered through the Internet. They contain several clauses establishing
for example the level of the services to guarantee, also known as quality of service (QoS) parameters and
the penalties to apply in case the requirements are not met during the SLA validity time. SLAs use legal
jargon, indeed they have legal validity in case of court litigation between the parties. A dedicated contract
management facility should be part of the service provisioning because of the contractual importance and
contents. Some work in literature about these facilities rely on a structured language representation of SLAs in
order to make them machine-readable. The majority of these languages are the result of private stipulation and
not available for public services where SLAs are expressed in common natural language instead. In order to
automate the SLAs managem
ent, in this paper we present an investigation towards SLAs text recognition. We
devised an approach to identify the definitions and the constraints included in the SLAs using different machine
learning techniques and provide a preliminary assessment of the approach on a set of 36 publicly available SLA
documents.
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