Energy-Efficient Virtual Network Function Reconfiguration Strategy Based on Short-Term Resources Requirement Prediction

In Network Function Virtualization, the resource demand of the network service evolves with the change of network traffic. VNF dynamic migration has become an effective method to improve network performance. However, for the time-varying resource demand, how to minimize the long-term energy consumption of the network while guaranteeing the Service Level Agreement (SLA) is the key issue that lacks previous research. To tackle this dilemma, this paper proposes an energy-efficient reconfiguration algorithm for VNF based on short-term resource requirement prediction (RP-EDM). Our algorithm uses LSTM to predict VNF resource requirements in advance to eliminate the lag of dynamic migration and determines the timing of migration. RP-EDM eliminates SLA violations by performing VNF separation on potentially overloaded servers and consolidates low-load servers timely to save energy. Meanwhile, we consider the power consumption of servers when booting up, which is existing objectively, to avoid switching on/off the server frequently. The simulation results suggest that RP-EDM has a good performance and stability under machine learning models with different accuracy. Moreover, our algorithm increases the total service traffic by about 15% while ensuring a low SLA interruption rate. The total energy cost is reduced by more than 20% compared with the existing algorithms

Matching State Estimation Scheme for Content-Based Sensor Search in the Web of Things

More recently, an increasing number of object-attached sensors are publishing their real-time state on the Internet by using state-of-the-art Web technologies, which make the sensor search service extremely important for the Web of Things (WoT). However, the existing issues that the sensor search service is facing bring huge challenges to the design of matching state estimation scheme. In this paper, an architecture of high-efficiency content-based sensor search system is depicted to provide a prototype system for sensor search. And then a matching state estimation scheme is proposed in detail, including a sensor state prediction approach to accurately estimate future sensor readings and a match estimating and verifying approach to effectively classify and verify candidate sensors, in order to enhance the performance of our search system. Simulation results show that our matching state estimation scheme dramatically reduces the communication overhead of search system and achieves excellent performance in terms of recall ratio and precision ratio

Fertilization Practices: Optimization in Greenhouse Vegetable Cultivation with Different Planting Years

Cucumber plant growth and the fate of N in the plant-soil system are influenced by fertilization practices, the strengths of which may vary among soils. Three soils with different years of greenhouse vegetable cultivation (0, 2, and 18 years) were fertilized differently (CK, no N fertilizer applied; CF, chemical NPK fertilizers applied; RCF, reduced chemical NPK fertilizers applied, with N, P, and K reduced by 46.5%, 68.6%, and 54.7%; RCF+CM, 75% of the total N derived from chemical fertilizer and the rest from chicken manure in the case of reduced fertilization) in a pot experiment to study the changes in cucumber (Cucumis sativus L.) growth, N uptake, residue, and losses. The original N in soil was insufficient to maintain leaf growth and chlorophyll synthesis at later growth stages, even in soil with 18 years of greenhouse vegetable cultivation, where the original N content was the highest (total N 1.73 g kg−1). However, the CF treatment with excessive N fertilization inhibited leaf growth at the early growing stage and accelerated leaf senescence later, especially in soil with longer years of greenhouse vegetable cultivation. Therefore, reduced fertilizer application (RCF and RCF+CM) is appropriate to improve cucumber growth and productivity in greenhouse cultivation with different planting years. Although the same amount of N was applied, the RCF+CM treatment performed better than the RCF treatment in terms of increasing plant N uptake (by 30.5%) and soil N pool storage (by 25.0%) while decreasing N losses (by 16.6%) in soil with 0 years of greenhouse vegetable cultivation. In soil with 2 and 18 years of greenhouse vegetable cultivation, the soil itself functions much better in exogenous N retention and supply, with the N storage and losses not significantly different between the RCF and RCF+CM treatments. We conclude that reduced fertilization with the co-application of chicken manure is optimal for plant growth promotion, output-input ratio increase, soil N fertility improvement, and environmental risk mitigation

Fertilization Practices: Optimization in Greenhouse Vegetable Cultivation with Different Planting Years

Cucumber plant growth and the fate of N in the plant-soil system are influenced by fertilization practices, the strengths of which may vary among soils. Three soils with different years of greenhouse vegetable cultivation (0, 2, and 18 years) were fertilized differently (CK, no N fertilizer applied; CF, chemical NPK fertilizers applied; RCF, reduced chemical NPK fertilizers applied, with N, P, and K reduced by 46.5%, 68.6%, and 54.7%; RCF+CM, 75% of the total N derived from chemical fertilizer and the rest from chicken manure in the case of reduced fertilization) in a pot experiment to study the changes in cucumber (Cucumis sativus L.) growth, N uptake, residue, and losses. The original N in soil was insufficient to maintain leaf growth and chlorophyll synthesis at later growth stages, even in soil with 18 years of greenhouse vegetable cultivation, where the original N content was the highest (total N 1.73 g kg−1). However, the CF treatment with excessive N fertilization inhibited leaf growth at the early growing stage and accelerated leaf senescence later, especially in soil with longer years of greenhouse vegetable cultivation. Therefore, reduced fertilizer application (RCF and RCF+CM) is appropriate to improve cucumber growth and productivity in greenhouse cultivation with different planting years. Although the same amount of N was applied, the RCF+CM treatment performed better than the RCF treatment in terms of increasing plant N uptake (by 30.5%) and soil N pool storage (by 25.0%) while decreasing N losses (by 16.6%) in soil with 0 years of greenhouse vegetable cultivation. In soil with 2 and 18 years of greenhouse vegetable cultivation, the soil itself functions much better in exogenous N retention and supply, with the N storage and losses not significantly different between the RCF and RCF+CM treatments. We conclude that reduced fertilization with the co-application of chicken manure is optimal for plant growth promotion, output-input ratio increase, soil N fertility improvement, and environmental risk mitigation

Recombinant osteopontin attenuates brain injury after intracerebral hemorrhage in mice

BACKGROUND: Osteopontin (OPN), an extracellular matrix glycoprotein, has been reported to inhibit inducible nitric oxide synthase (iNOS). We examined if recombinant OPN (r-OPN) inhibits iNOS and prevents brain injury in a mouse collagenase-induced intracerebral hemorrhage (ICH) model. METHODS: One hundred one mice were randomly assigned to five groups: sham, ICH + vehicle, ICH + r-OPN (10, 50, or 100 ng per mouse) groups. Vehicle or r-OPN was administered via an intracerebroventricular infusion 20 min pre-ICH. Neurological scores and brain water content were evaluated at 24 and 72 h, and hemoglobin assay, Nissl staining and Western blot for iNOS, Stat1, matrix metalloproteinase (MMP)-9 and zonula occludens (ZO)-1 were performed at 24 h post-ICH. RESULTS: r-OPN did not affect hematoma formation. Middle (50 ng)- and high (100 ng)-dose, but not low (10 ng)-dose of r-OPN treatment significantly improved neurological scores and brain water content compared with the vehicle group. The protective effect of r-OPN was associated with significantly rescued neuronal cells in the peri-hematoma region as well as a decrease in the Stat1 phosphorylation, iNOS induction, MMP-9 activation, and ZO-1 degradation. CONCLUSIONS: This study suggests that r-OPN may down-regulate iNOS expression by the inhibition of Stat1 phosphorylation, and therefore suppressing the MMP-9 activation, preventing ICH-induced brain injury in mice