Multi-objective temporal bin packing problem: An application in cloud computing
•We tackle the virtual machine placement, an extension of the temporal bin-packing.•An optimization model minimizing the number of servers and fire-ups is proposed.•Proposed heuristic and column generation algorithms provide strong upper/lower bounds.•We observe that the number of fire-ups decreases...
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| Published in: | Computers & operations research Vol. 121; p. 104959 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
Elsevier Ltd
01.09.2020
Pergamon Press Inc |
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| ISSN: | 0305-0548, 1873-765X, 0305-0548 |
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| Abstract | •We tackle the virtual machine placement, an extension of the temporal bin-packing.•An optimization model minimizing the number of servers and fire-ups is proposed.•Proposed heuristic and column generation algorithms provide strong upper/lower bounds.•We observe that the number of fire-ups decreases with the number of servers.
Improving energy efficiency and lowering operational costs are the main challenges faced in systems with multiple servers. One prevalent objective in such systems is to minimize the number of servers required to process a given set of tasks under server capacity constraints. This objective leads to the well-known bin packing problem. In this study, we consider a generalization of this problem with a time dimension, where the tasks are to be performed with predefined start and end times. This new dimension brings about new performance considerations, one of which is the uninterrupted utilization of servers. This study is motivated by the problem of energy efficient assignment of virtual machines to physical servers in a cloud computing service. We address the virtual machine placement problem and present a binary integer programming model to develop different assignment policies. By analyzing the structural properties of the problem, we propose an efficient heuristic method based on solving smaller versions of the original problem iteratively. Moreover, we design a column generation algorithm that yields a lower bound on the objective value, which can be utilized to evaluate the performance of the heuristic algorithm. Our numerical study indicates that the proposed heuristic is capable of solving large-scale instances in a short time with small optimality gaps. |
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| AbstractList | Improving energy efficiency and lowering operational costs are the main challenges faced in systems with multiple servers. One prevalent objective in such systems is to minimize the number of servers required to process a given set of tasks under server capacity constraints. This objective leads to the well-known bin packing problem. In this study, we consider a generalization of this problem with a time dimension, where the tasks are to be performed with predefined start and end times. This new dimension brings about new performance considerations, one of which is the uninterrupted utilization of servers. This study is motivated by the problem of energy efficient assignment of virtual machines to physical servers in a cloud computing service. We address the virtual machine placement problem and present a binary integer programming model to develop different assignment policies. By analyzing the structural properties of the problem, we propose an efficient heuristic method based on solving smaller versions of the original problem iteratively. Moreover, we design a column generation algorithm that yields a lower bound on the objective value, which can be utilized to evaluate the performance of the heuristic algorithm. Our numerical study indicates that the proposed heuristic is capable of solving large-scale instances in a short time with small optimality gaps. •We tackle the virtual machine placement, an extension of the temporal bin-packing.•An optimization model minimizing the number of servers and fire-ups is proposed.•Proposed heuristic and column generation algorithms provide strong upper/lower bounds.•We observe that the number of fire-ups decreases with the number of servers. Improving energy efficiency and lowering operational costs are the main challenges faced in systems with multiple servers. One prevalent objective in such systems is to minimize the number of servers required to process a given set of tasks under server capacity constraints. This objective leads to the well-known bin packing problem. In this study, we consider a generalization of this problem with a time dimension, where the tasks are to be performed with predefined start and end times. This new dimension brings about new performance considerations, one of which is the uninterrupted utilization of servers. This study is motivated by the problem of energy efficient assignment of virtual machines to physical servers in a cloud computing service. We address the virtual machine placement problem and present a binary integer programming model to develop different assignment policies. By analyzing the structural properties of the problem, we propose an efficient heuristic method based on solving smaller versions of the original problem iteratively. Moreover, we design a column generation algorithm that yields a lower bound on the objective value, which can be utilized to evaluate the performance of the heuristic algorithm. Our numerical study indicates that the proposed heuristic is capable of solving large-scale instances in a short time with small optimality gaps. |
| ArticleNumber | 104959 |
| Author | İlker Birbil, Ş. Aydın, Nurşen Muter, İbrahim |
| Author_xml | – sequence: 1 givenname: Nurşen surname: Aydın fullname: Aydın, Nurşen email: nursen.aydin@wbs.ac.uk organization: Warwick Business School, University of Warwick, Coventry, CV4 7AL, UK – sequence: 2 givenname: İbrahim surname: Muter fullname: Muter, İbrahim email: i.muter@bath.ac.uk organization: School of Management, University of Bath, Claverton Down, Bath BA2 7AY, UK – sequence: 3 givenname: Ş. surname: İlker Birbil fullname: İlker Birbil, Ş. email: birbil@ese.eur.nl organization: Econometric Institute, Erasmus University Rotterdam, 3000 DR Rotterdam, The Netherlands |
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| Snippet | •We tackle the virtual machine placement, an extension of the temporal bin-packing.•An optimization model minimizing the number of servers and fire-ups is... Improving energy efficiency and lowering operational costs are the main challenges faced in systems with multiple servers. One prevalent objective in such... |
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| SubjectTerms | Algorithms Bin packing Cloud computing Column generation Exact methods Heuristic Heuristic methods Heuristics Integer programming Lower bounds Operations research Packing problem Performance evaluation Servers Virtual environments |
| Title | Multi-objective temporal bin packing problem: An application in cloud computing |
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