Balanced and Energy Efficient Multi Core-Aware Virtual Machine Placement Algorithm Using Multi-Dimensional Resource Space Partition Model

Optimal resource management is required in a data center to allocate the resources to users in a balanced manner. Balanced resource allocation is one of the key challenges in the data center. The multi-dimensional resources of a data center must be allocated in a balanced manner in all the dimensions of physical machines. The unbalanced resource allocation leads to unused residual resource fragments. The unused residual resource fragments leads to resource wastage. If the multi-dimensional data center resources are allocated in a balanced manner, the resource wastage does not occur. Also, the balanced allocation improves the power consumption. The balanced resource allocation reduces the resource wastage as well as reduces the power consumption. In this paper, we have designed a Balanced Energy Efficient Multi-Core Aware Virtual Machine Placement algorithm (MCA-BEE-VMP) using multi-dimensional resource space partition model to balance the resources like CPU and memory and also to reduce the power consumption. We used Google Cloud Jobs (GoCJ) dataset for the simulation. In our simulation of MCA-BEE-VMP using Cloud Sim simulation tool we have achieved balanced CPU and memory resources allocation in two dimensions of a physical machine. The resource wastage and power consumption is improved and the simulation results were analyzed.

Y-90 SIRT: evaluation of TCP variation across dosimetric models

Introduction Much progress has been made in implementing selective internal radiation therapy (SIRT) as a viable treatment option for hepatic malignancies. However, there is still much need for improved options for calculating the amount of activity to be administered. To make advances towards this goal, this study examines the relationship between predicted biological outcomes of liver tumors via tumor control probabilities (TCP) and parenchyma via normal tissue complication probabilities (NTCP) given variations in absorbed dose prescription methodologies. Methods Thirty-nine glass microsphere treatments in 35 patients with hepatocellular carcinoma or metastatic liver disease were analyzed using 99mTc-MAA SPECT/CT and 90Y PET/CT scans. Predicted biological outcomes corresponding to the single compartment (standard) model and multi-compartment (partition) dosimetry model were compared using our previously derived TCP dose-response curves over a range of 80–150 Gy prescribed absorbed dose to the perfused volume, recommended in the package insert for glass microspheres. Retrospective planning dosimetry was performed on the MAA SPECT/CT; changes from the planned infused activity due to selection of absorbed dose level and dosimetry model (standard or partition) were used to scale absorbed doses reported from 90Y PET/CT including liver parenchyma and lesions (N = 120) > 2 ml. A parameterized charting system was developed across all potential prescription options to enable a clear relationship between standard prescription vs. the partition model-based prescription. Using a previously proposed NTCP model, the change in prescribed dose from a standard model prescription of 120 Gy to the perfused volume to a 15% NTCP prescription to the normal liver was explored. Results Average TCP predictions for the partition model compared with the standard model varied from a 13% decrease to a 32% increase when the prescribed dose was varied across the range of 80–150 Gy. In the parametrized chart comparing absorbed dose prescription ranges across the standard model and partition models, a line of equivalent absorbed dose to a tumor was identified. TCP predictions on a per lesion basis varied between a 26% decrease and a 81% increase for the most commonly chosen prescription options when comparing the partition model with the standard model. NTCP model was only applicable to a subset of patients because of the small volume fraction of the liver that was targeted in most cases. Conclusion Our retrospective analysis of patient imaging data shows that the choice of prescribed dose and which model to prescribe potentially contribute to a wide variation in average tumor efficacy. Biological response data should be included as one factor when looking to improve patient care in the clinic. The use of parameterized charting, such as presented here, will help direct physicians when transitioning to newer prescription methods.

Adjustment for energy intake in nutritional research: a causal inference perspective

ABSTRACTBackgroundFour models are commonly used to adjust for energy intake when estimating the causal effect of a dietary component on an outcome; (1) the ‘standard model’ adjusts for total energy intake, (2) the ‘energy partition model’ adjusts for remaining energy intake, (3) the ‘nutrient density model’ rescales the exposure as a proportion of total energy, and (4) the ‘residual model’ indirectly adjusts for total energy by using a residual. It remains underappreciated that each approach evaluates a different estimand and only partially accounts for proxy confounding by common dietary causes.ObjectiveTo clarify the implied causal estimand and interpretation of each model and evaluate their performance in reducing dietary confounding.DesignSemi-parametric directed acyclic graphs and Monte Carlo simulations were used to identify the estimands and interpretations implied by each model and explore their performance in the absence or presence of dietary confounding.ResultsThe ‘standard model’ and the mathematically identical ‘residual model’ estimate the average relative causal effect (i.e., a ‘substitution’ effect) but provide biased estimates even in the absence of confounding. The ‘energy partition model’ estimates the total causal effect but only provides unbiased estimates in the absence of confounding or when all other nutrients have equal effects on the outcome. The ‘nutrient density model’ has an obscure interpretation but attempts to estimate the average relative causal effect rescaled as a proportion of total energy intake. Accurate estimates of both the total and average relative causal effects may instead be estimated by simultaneously adjusting for all dietary components, an approach we term the ‘all-components model’.ConclusionLack of awareness of the estimand differences and accuracy of the four modelling approaches may explain some of the apparent heterogeneity among existing nutritional studies and raise serious questions regarding the validity of meta-analyses where different estimands have been inappropriately pooled.

Impact of the dosimetry approach on the resulting 90Y radioembolization planned absorbed doses based on 99mTc-MAA SPECT-CT: is there agreement between dosimetry methods?

Background Prior radioembolization, a simulation using 99mTc-macroaggregated albumin as 90Y-microspheres surrogate is performed. Gamma scintigraphy images (planar, SPECT, or SPECT-CT) are acquired to evaluate intrahepatic 90Y-microspheres distribution and detect possible extrahepatic and lung shunting. These images may be used for pre-treatment dosimetry evaluation to calculate the 90Y activity that would get an optimal tumor response while sparing healthy tissues. Several dosimetry methods are available, but there is still no consensus on the best methodology to calculate absorbed doses. The goal of this study was to retrospectively evaluate the impact of using different dosimetry approaches on the resulting 90Y-radioembolization pre-treatment absorbed dose evaluation based on 99mTc-MAA images. Methods Absorbed doses within volumes of interest resulting from partition model (PM) and 3D voxel dosimetry methods (3D-VDM) (dose-point kernel convolution and local deposition method) were evaluated. Additionally, a new “Multi-tumor Partition Model” (MTPM) was developed. The differences among dosimetry approaches were evaluated in terms of mean absorbed dose and dose volume histograms within the volumes of interest. Results Differences in mean absorbed dose among dosimetry methods are higher in tumor volumes than in non-tumoral ones. The differences between MTPM and both 3D-VDM were substantially lower than those observed between PM and any 3D-VDM. A poor correlation and concordance were found between PM and the other studied dosimetry approaches. DVH obtained from either 3D-VDM are pretty similar in both healthy liver and individual tumors. Although no relevant global differences, in terms of absorbed dose in Gy, between both 3D-VDM were found, important voxel-by-voxel differences have been observed. Conclusions Significant differences among the studied dosimetry approaches for 90Y-radioembolization treatments exist. Differences do not yield a substantial impact in treatment planning for healthy tissue but they do for tumoral liver. An individual segmentation and evaluation of the tumors is essential. In patients with multiple tumors, the application of PM is not optimal and the 3D-VDM or the new MTPM are suggested instead. If a 3D-VDM method is not available, MTPM is the best option. Furthermore, both 3D-VDM approaches may be indistinctly used.