A Survey of Virtual Machine Placement Techniques and VM Selection Policies in Cloud Datacenter

The large scale virtualized data centers have been established due to the requirement of rapid growth in computational power driven by cloud computing model . The high energy consumption of such data centers is becoming more and more serious problem .In order to reduce the energy consumption, server consolidation techniques are used .But aggressive consolidation of VMs can lead to performance degradation. Hence another problem arise that is, the Service Level Agreement(SLA) violation. The optimized consolidation is achieved through optimized VM placement and VM selection policies . A comparative study of virtual machine placement and VM selection policies are presented in this paper for improving the energy efficiency

Restricted Adaptivity in Stochastic Scheduling

We consider the stochastic scheduling problem of minimizing the expected makespan on m parallel identical machines. While the (adaptive) list scheduling policy achieves an approximation ratio of 2, any (non-adaptive) fixed assignment policy has performance guarantee ?((log m)/(log log m)). Although the performance of the latter class of policies are worse, there are applications in which non-adaptive policies are desired. In this work, we introduce the two classes of ?-delay and ?-shift policies whose degree of adaptivity can be controlled by a parameter. We present a policy - belonging to both classes - which is an ?(log log m)-approximation for reasonably bounded parameters. In other words, an exponential improvement on the performance of any fixed assignment policy can be achieved when allowing a small degree of adaptivity. Moreover, we provide a matching lower bound for any ?-delay and ?-shift policy when both parameters, respectively, are in the order of the expected makespan of an optimal non-anticipatory policy

Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem

[EN] Mathematical programming models are invaluable tools at decision making, assisting managers to uncover otherwise unattainable means to optimize their processes. However, the value they provide is only as good as their capacity to capture the process domain. This information can only be obtained from stakeholders, i.e., clients or users, who can hardly communicate the requirements clearly and completely. Besides, existing conceptual models of mathematical programming models are not standardized, nor is the process of deriving the mathematical programming model from the concept model, which remains ad hoc. In this paper, we propose an agile methodology to construct mathematical programming models based on two techniques from requirements engineering that have been proven effective at requirements elicitation: the language extended lexicon (LEL) and scenarios. Using the pair of LEL + scenarios allows to create a conceptual model that is clear and complete enough to derive a mathematical programming model that effectively captures the business domain. We also define an ontology to describe the pair LEL + scenarios, which has been implemented with a semantic mediawiki and allows the collaborative construction of the conceptual model and the semi-automatic derivation of mathematical programming model elements. The process is applied and validated in a known fresh tomato packing optimization problem. This proposal can be of high relevance for the development and implementation of mathematical programming models for optimizing agriculture and supply chain management related processes in order to fill the current gap between mathematical programming models in the theory and the practice.This work was supported by the European Commission, project RUC-APS, grant number 691249, funded by the European Union's research and innovation programme under the H2020 Marie SklodowskaCurie Actions; and the Argentinian National Agency for Scientific and Technical Promotion (ANPCyT), grant number PICT-2015-3000.Garrido, A.; Antonelli, L.; Martin, J.; Alemany Díaz, MDM.; Mula, J. (2020). Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem. Computers and Electronics in Agriculture. 170:1-14. https://doi.org/10.1016/j.compag.2020.105242S114170Alemany, M., Ortiz, A., & Fuertes-Miquel, V. S. (2018). 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