In the Cloud Computing paradigm there is an emerging Green Computing awareness, aimed at increasing the cost-efficiency of the underlying infrastructure. The pursued objective is to find the right compromise between the energy consumption and the perceived quality of service of the applications running in the cloud. Big data centers, along with the adoption of the virtualization technology, are increasingly experiencing the need to reduce consumption, because of both the environmental pollution and the economic concern. Among the novel techniques used to minimize the energy consumption wastefulness there is the Virtual Machines Consolidation, which leverages the VM live migration. The goal is to increase the overall cost-efficiency by reducing the number of active nodes. The main contributions of this paper are the proposal of a novel model for the consolidation problem and a Simulated Annealing based algorithm which solves it by evaluating the attractiveness of the possible VM migrations.
Next generation 5G networks will rely on virtualized Data Centers (vDC) to host virtualized network functions on commodity servers. Such Network Function Virtualization (NFV) will lead to significant savings in terms of infrastructure cost and reduced management complexity. However, green strategies for networking and computing inside data centers, such as server consolidation or energy aware routing, should not negatively impact the quality and service level agreements expected from network operators. In this paper, we study how robust strategies that place virtual network functions (VNF) inside vDC impact the energy savings and the protection level against resource demand uncertainty. We propose novel optimization models that allow the minimization of the energy of the computing and network infrastructure which is hosting a set of service chains that implement the VNFs. The model explicitly provides for robustness to unknown or imprecisely formulated resource demand variations, powers down unused routers, switch ports and servers, and calculates the energy optimal VNF placement and network embedding also considering latency constraints on the service chains. We propose both exact and heuristic methods. Our experiments were carried out using the virtualized Evolved Packet Core (vEPC), which * Corresponding author allows us to quantitatively assess the trade-off between energy cost, robustness and the protection level of the solutions against demand uncertainty. Our heuristic is able to converge to a good solution in a very short time, in comparison to the exact solver, which is not able to output better results in a longer run as demonstrated by our numerical evaluation. We also study the degree of robustness of a solution for a given protection level and the cost of additional energy needed because of the usage of more computing and network elements.
Serum iron (sFe), and ferritin (sFert), transferrin saturation index (TSI), plasma zinc and copper (pZn, pCu), and erythrocyte zinc content (eZn) were measured in 55 obese children and adolescents (28 males and 27 females) before and after a 13-wk treatment with a hypocaloric balanced diet (HCBD, 22 subjects) or a 10-wk treatment with a protein sparing modified fast diet (PSMF, 33 subjects). The energy intake provided by the HCBD and PSMF diet was calculated to be 60 and 25%, respectively, of the recommended dietary allowances (RDAs) for age and sex. Neither diet was supplemented with trace elements or calcium. Using a visual memory system, all subjects had a 24-h dietary intake recall before starting the weight-loss program. Iron, zinc, and copper intakes from the 24-h recall were compared with those from prescribed diets. Both diets produced a significant (p < 0.001) weight reduction with a significant reduction in the arm muscle area of the PSMF group. After treatment, no significant change was observed in sFe, sFert, and TSI of either group, whereas eZn increased significantly in the HCBD and the PSMF groups (p = 0.001 and p < 0.006, respectively), with an improvement of the erythrocyte index (E.I.). A significant increase in pZn was also observed in the PSMF group (p = 0.007).
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