dc.creator | Sellaro, Daniela F. | es |
dc.creator | Frantz, Rafael Z. | es |
dc.creator | Hernández Salmerón, Inmaculada Concepción | es |
dc.creator | Roos Frantz, Fabricia | es |
dc.creator | Sawicki, Sandro | es |
dc.date.accessioned | 2022-04-29T08:11:07Z | |
dc.date.available | 2022-04-29T08:11:07Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Sellaro, D.F., Frantz, R.Z., Hernández Salmerón, I.C., Roos Frantz, F. y Sawicki, S. (2017). Task Scheduling Optimization on Enterprise Application Integration Platforms Based on the Meta-heuristic Particle Swarm Optimization. En SBES 2017 : 31st Brazilian Symposium on Software Engineering (273-278), Fortaleza, CE, Brazil: Association for Computing Machinery (ACM). | |
dc.identifier.isbn | 978-1-4503-5326-7 | es |
dc.identifier.uri | https://hdl.handle.net/11441/132884 | |
dc.description.abstract | Companies seek technological alternatives that provide competiti veness for their business processes. Among these alternatives, there
are integration platforms that allow you to connect applications to
your software ecosystems. These ecosystems are often composed
of local applications and cloud computing services, such as SaaS
and PaaS, and still, interact with social media. Integration platforms
are specialized software that allows you to design, execute and
monitor integration solutions, which connect functionality and
data from different applications. Integration platforms typically
provide a specific domain language, development toolkit, runtime
engine, and monitoring tool. The efficiency of the engine in sche duling and performing integration tasks has a direct impact on
the performance of a solution and this is one of the challenges
faced by integration platforms. Our literature review has identified
that integration engines adopt task scheduling algorithms based
on the textit First-In-First-Out discipline, which may be inefficient.
Therefore, it is appropriate to seek a task scheduling algorithm
that optimizes engine performance, providing a positive impact on
the performance of the integration solution in different scenarios.
This article proposes an algorithm for task scheduling based on the
meta-heuristic optimization technique, which assigns the tasks to
the computational resources, considering the waiting time in the
queue of ready tasks and the computational complexity of Each task
in order to optimize the performance of the integration solution. | es |
dc.format | application/pdf | es |
dc.format.extent | 6 | es |
dc.language.iso | por | es |
dc.publisher | Association for Computing Machinery (ACM) | es |
dc.relation.ispartof | SBES 2017 : 31st Brazilian Symposium on Software Engineering (2017), pp. 273-278. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Integrating Enterprise Applications | es |
dc.subject | Optimization | es |
dc.subject | Cloud computing | es |
dc.subject | Runtime System | es |
dc.subject | Task Scheduling Algorithm | es |
dc.title | Task Scheduling Optimization on Enterprise Application Integration Platforms Based on the Meta-heuristic Particle Swarm Optimization | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.publisherversion | https://dl.acm.org/doi/10.1145/3131151.3131191 | es |
dc.identifier.doi | 10.1145/3131151.3131191 | es |
dc.publication.initialPage | 273 | es |
dc.publication.endPage | 278 | es |
dc.eventtitle | SBES 2017 : 31st Brazilian Symposium on Software Engineering | es |
dc.eventinstitution | Fortaleza, CE, Brazil | es |
dc.relation.publicationplace | New York, USA | es |