Mixed integer linear programming for quality of service optimization in Clouds

Guérout, Tom; Gaoua, Yacine; Artigues, Christian; Costa, Georges Da; López, Pierre; Monteil, Thierry

doi:10.1016/j.future.2016.12.034

Cited by 24 publications

(8 citation statements)

References 30 publications

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“…MILP p lays an important role in many real-wo rld problems, including resource sharing [46], service optimization [47], and so on. In this section, we present a greedy approach with lo w time co mplexity for MILP problem.…”

Section: Wo-based Greedy Approachmentioning

confidence: 99%

Security and Energy-aware Collaborative Task Offloading in D2D communication

et al. 2021

Future Generation Computer Systems

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Section: Wo-based Greedy Approachmentioning

confidence: 99%

Security and Energy-aware Collaborative Task Offloading in D2D communication

et al. 2021

Future Generation Computer Systems

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“…In reality, malleable jobs cannot be perfect, and their reconfiguration necessarily induces overheads such as communication between nodes and time for data to be transferred. Another work [10] addressing cloud computing services focuses more on the optimisation aspect of providing high QoS and the improvement of energy consumption. It also provides a comparison of the Mixed Integer Linear Programming (MILP) approach against the Genetic Algorithm approach for the same objectives.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy-aware scheduling of malleable HPC applications using a Particle Swarm optimised greedy algorithm

Mejri

Dupont

Costa

2020

Sustainable Computing: Informatics and Systems

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The scheduling of parallel tasks is a topic that has received a lot of attention in recent years, in particular, due to the development of larger HPC clusters. It is regarded as an interesting problem because when combined with performant hardware, it ensures fast and efficient computing. However, it comes with a cost. The growing number of HPC clusters entails a greater global energy consumption which has a clear negative environmental impact. A green solution is thus required to find a compromise between energy-saving and high-performance computing within those clusters. In this paper, we evaluate the use of malleable jobs and idle servers powering off as a way to reduce both jobs mean stretch time and servers average power consumption. Malleable jobs have the particularity that the number of allocated servers can be changed during runtime. We present an energy-aware greedy algorithm with Particle Swarm Optimized parameters as a possible solution to schedule malleable jobs. An in-depth evaluation of the approach is then outlined using results from a simulator that was developed to handle malleable jobs. The results show that the use of malleable tasks can lead to an improved performance in terms of power consumption. We believe that our results open the door for further investigations on using malleable jobs models coupled with the energy-saving aspect.

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“…They conclude on the fact that the current works are preliminary and insufficient to create a dedicated metric to the specific field of big data. Guérout et al [18] define multiple QoS metrics for Software as a Service Cloud platforms, which they used in [19] as a base for objective formulation of a scheduling approach. They are organized in four categories: performance, dependability (such as reliability or availability), security and cost (including both the cost for the consumer, the energy cost for the provider and the carbon emission cost).…”

Section: Related Workmentioning

confidence: 99%