CAV1/Caveolin1, an integral membrane protein, is involved in caveolae function and cellular signaling pathways. Here, we report that CAV1 is a positive regulator of autophagy under oxidative stress and cerebral ischemic injury. Treatment with hydrogen peroxide enhanced autophagy flux and caused the localization of BECN1 to the mitochondria, whereas these changes were impaired in the absence of CAV1. Among many autophagy signals, only LC3 foci formation in response to hydrogen peroxide was abolished by CAV1 deficiency. Under oxidative stress, CAV1 interacted with a complex of BECN1/VPS34 through its scaffolding domain, and this interaction facilitated autophagosome formation. Interestingly, the phosphorylation of CAV1 at tyrosine-14 was essential for the interaction with BECN1 and their localization to the mitochondria, and the activation of autophagy in response to hydrogen peroxide. In addition, the expression of a phosphatase PTPN1 reduced the phosphorylation of CAV1 and inhibited autophagy. Further, compared to that in wild-type mice, autophagy was impaired and cerebral infarct damage was aggravated in the brain of Cav1 knockout mice. These results suggest that the phosphorylated CAV1 functions to activate autophagy through binding to the BECN1/VPS34 complex under oxidative stress and to protect against ischemic damage.

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Balancing the performance and the energy consumption of the servers is one of the important issues in large-scale computing infrastructure such as data centers. Measuring or accurately estimating power consumption of a server is one of the most fundamental and enabling technologies for enhancing energy efficiency of a server because how the server consumes the supplied power is essential for constructing a power management policy. For the purpose, power models for server systems have been extensively studied. However, most of existing works are too complex to be used real-time, because gathering the data for estimating the power consumption causes much overhead. In this paper, we propose a simple power model for a multicore server. Our model is simple enough to gather only four parameters: operating frequency, the number of active cores, the number of cache accesses and the number of the last level cache misses. We show our model is simple but relatively accurate by experiments that show the model has over 90% accuracy.

Minimizing energy consumption with guaranteeing realtime constraints in low-power embedded systems is gaining more importance as real-time applications become more widely used in embedded systems. Dynamic voltage scaling is a technique to reduce energy consumption by lowering supply voltage. However, lowering supply voltage may interfere with scheduling algorithms, so that tasks may not be successfully scheduled. In this paper, we formulate the problem of minimizing energy consumption for Pre-scheduling as an optimization problem, and show that the problem is a nonlinear convex optimization with linear constraints which can be solved by sequential quadratic programming. By solving the problem, we can obtain the optimal supply voltage and successful scheduling of all tasks is guaranteed.