Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty

Cloud computing providers must support requests for resources in dynamic environments, considering service elasticity and overbooking of physical resources. Due to the randomness of requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. In this context, a renewed formulation of the VMP problem is presented, considering the optimization of four objective functions: (i) power consumption, (ii) economical revenue, (iii) resource utilization and (iv) reconfiguration time. To solve the presented formulation, a two-phase optimization scheme is considered, composed by an online incremental VMP phase (iVMP) and an offline VMP reconfiguration (VMPr) phase. An experimental evaluation of five algorithms taking into account 400 different scenarios was performed, considering three VMPr Triggering and two VMPr Recovering methods as well as three VMPr resolution alternatives. Experimental results indicate which algorithm outperformed the other evaluated algorithms, improving the quality of solutions in a scenario-based uncertainty model considering the following evaluation criteria: (i) average, (ii) maximum and (iii) minimum objective function costs.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty

Cloud computing providers must support requests for resources in dynamic environments, considering service elasticity and overbooking of physical resources. Due to the randomness of requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. In this context, a renewed formulation of the VMP problem is presented, considering the optimization of four objective functions: (i) power consumption, (ii) economical revenue, (iii) resource utilization and (iv) reconfiguration time. To solve the presented formulation, a two-phase optimization scheme is considered, composed by an online incremental VMP phase (iVMP) and an offline VMP reconfiguration (VMPr) phase. An experimental evaluation of five algorithms taking into account 400 different scenarios was performed, considering three VMPr Triggering and two VMPr Recovering methods as well as three VMPr resolution alternatives. Experimental results indicate which algorithm outperformed the other evaluated algorithms, improving the quality of solutions in a scenario-based uncertainty model considering the following evaluation criteria: (i) average, (ii) maximum and (iii) minimum objective function costs.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty

Cloud computing providers must support requests for resources in dynamic environments, considering service elasticity and overbooking of physical resources. Due to the randomness of requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. In this context, a renewed formulation of the VMP problem is presented, considering the optimization of four objective functions: (i) power consumption, (ii) economical revenue, (iii) resource utilization and (iv) reconfiguration time. To solve the presented formulation, a two-phase optimization scheme is considered, composed by an online incremental VMP phase (iVMP) and an offline VMP reconfiguration (VMPr) phase. An experimental evaluation of five algorithms taking into account 400 different scenarios was performed, considering three VMPr Triggering and two VMPr Recovering methods as well as three VMPr resolution alternatives. Experimental results indicate which algorithm outperformed the other evaluated algorithms, improving the quality of solutions in a scenario-based uncertainty model considering the following evaluation criteria: (i) average, (ii) maximum and (iii) minimum objective function costs.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Reproductive Biology of <i>Solanum orbiculatum</i> ssp. <i>orbiculatum</i>, an Australian Endemic Bush Tomato

The Australian tomato Solanum orbiculatum ssp. orbiculatum is an edible bush tomato endemic to the more arid areas of Western Australia, South Australia, and the Northern Territory. Breeding system data indicate that the plants are potentially self-compatible but are unable to carry out spontaneous autogamy or agamospermy. The flower is protogynous, as the stigma become receptive to pollen germination while still in bud condition and the anthers do not release pollen immediately after anthesis. This arrangement is a simple and common way to avoid too much self-pollination, favours cross pollination, and would allow forced bud pollination for hybrid development. The floral structure and morphology of this species can also encourage cross pollination, as the stigma is mostly exserted above the anther’s tips. In an attempt to examine the hypothesis of a positive correlation between pollen grain size and style length, we found a statistically significant difference between the pollen size of the long-styled and short-styled flowers. Pollen in vitro germination and viability tests have been optimised to facilitate effective breeding work on this species. A modified Brewbake and Kwack (BK) medium supplemented with 20% sucrose and 2.5% PEG 4000 has been found to be the most efficient media components for the in vitro germination of viable pollen grains. Alternatively, Alexander’s and acetocarmine (1%) stains have shown the highest positive correlation with the in vitro pollen germination test and, therefore, can be used as quick tests for checking pollen viability. Moreover, pollen grains stored for three months under 4 °C and dry conditions can be used efficiently to effect fertilisation in breeding programs, as it can maintain more than 50% of the original viability. This study will contribute to understanding the evolution and systematic relationships of species and for founding effective conservation programs. Furthermore, understanding the reproductive biology of this species is also of interest because of its potential for tomato breeding