eprintid: 3635
rev_number: 6
eprint_status: archive
userid: 69
dir: disk0/00/00/36/35
datestamp: 2017-01-24 13:07:45
lastmod: 2017-01-24 13:07:45
status_changed: 2017-01-24 13:07:45
type: article
metadata_visibility: show
creators_name: Sebastio, Stefano
creators_name: Gnecco, Giorgio
creators_name: Bemporad, Alberto
creators_id: 
creators_id: giorgio.gnecco@imtlucca.it
creators_id: alberto.bemporad@imtlucca.it
title: Optimal distributed task scheduling in volunteer clouds
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
keywords: Cloud computing; Distributed optimization; Integer programming; Combinatorial optimization; ADMM
abstract: Abstract The ever increasing request of computational resources has shifted the computing paradigm towards solutions where less computation is performed locally. The most widely adopted approach nowadays is represented by cloud computing. With the cloud, users can transparently access to virtually infinite resources with the same aptitude of using any other utility. Next to the cloud, the volunteer computing paradigm has gained attention in the last decade, where the spared resources on each personal machine are shared thanks to the users’ willingness to cooperate. Cloud and volunteer paradigms have been recently seen as companion technologies to better exploit the use of local resources. Conversely, this scenario places complex challenges in managing such a large-scale environment, as the resources available on each node and the presence of the nodes online are not known a-priori. The complexity further increases in presence of tasks that have an associated Service Level Agreement specified, e.g., through a deadline. Distributed management solutions have then be advocated as the only approaches that are realistically applicable. In this paper, we propose a framework to allocate tasks according to different policies, defined by suitable optimization problems. Then, we provide a distributed optimization approach relying on the Alternating Direction Method of Multipliers (ADMM) for one of these policies, and we compare it with a centralized approach. Results show that, when a centralized approach can not be adopted in a real environment, it could be possible to rely on the good suboptimal solutions found by the ADMM.
date: 2017
date_type: published
publication: Computers and operations research
volume: 81
publisher: Elsevier
pagerange: 231 - 246
id_number: 10.1016/j.cor.2016.11.004
refereed: TRUE
issn: 0305-0548
official_url: http://www.sciencedirect.com/science/article/pii/S0305054816302660
projects: HOME/2013/CIPS/AG/4000005013 project CI2C
citation:   Sebastio, Stefano and Gnecco, Giorgio and Bemporad, Alberto  Optimal distributed task scheduling in volunteer clouds.  Computers and operations research, 81.  231 - 246.  ISSN 0305-0548      (2017)