RCD: Rapid Close to Deadline Scheduling for Datacenter Networks

Datacenter-based Cloud Computing services provide a flexible, scalable and yet economical infrastructure to host online services such as multimedia streaming, email and bulk storage. Many such services perform geo-replication to provide necessary quality of service and reliability to users resulting in frequent large inter- datacenter transfers. In order to meet tenant service level agreements (SLAs), these transfers have to be completed prior to a deadline. In addition, WAN resources are quite scarce and costly, meaning they should be fully utilized. Several recently proposed schemes, such as B4, TEMPUS, and SWAN have focused on improving the utilization of inter-datacenter transfers through centralized scheduling, however, they fail to provide a mechanism to guarantee that admitted requests meet their deadlines. Also, in a recent study, authors propose Amoeba, a system that allows tenants to define deadlines and guarantees that the specified deadlines are met, however, to admit new traffic, the proposed system has to modify the allocation of already admitted transfers. In this paper, we propose Rapid Close to Deadline Scheduling (RCD), a close to deadline traffic allocation technique that is fast and efficient. Through simulations, we show that RCD is up to 15 times faster than Amoeba, provides high link utilization along with deadline guarantees, and is able to make quick decisions on whether a new request can be fully satisfied before its deadline.Comment: World Automation Congress (WAC), IEEE, 201

Intelligent Sensing in Dynamic Environments Using Markov Decision Process

In a network of low-powered wireless sensors, it is essential to capture as many environmental events as possible while still preserving the battery life of the sensor node. This paper focuses on a real-time learning algorithm to extend the lifetime of a sensor node to sense and transmit environmental events. A common method that is generally adopted in ad-hoc sensor networks is to periodically put the sensor nodes to sleep. The purpose of the learning algorithm is to couple the sensor’s sleeping behavior to the natural statistics of the environment hence that it can be in optimal harmony with changes in the environment, the sensors can sleep when steady environment and stay awake when turbulent environment. This paper presents theoretical and experimental validation of a reward based learning algorithm that can be implemented on an embedded sensor. The key contribution of the proposed approach is the design and implementation of a reward function that satisfies a trade-off between the above two mutually contradicting objectives, and a linear critic function to approximate the discounted sum of future rewards in order to perform policy learning

In-vivo Kinetics of Silymarin (Milk Thistle) on Healthy Male Volunteers

Purpose: The study was aimed at evaluating the in vivo kinetics of silymarin tablets, a product with anti-hepatotoxic and free radical scavenging activities. Methods: Silimarin® (Amson Vaccines & Pharma Pvt Ltd) was used as the test product while another silymarin tablet brand, Silliver® (Abbott Laboratories Pak Ltd) was the reference product. The tablets were administered to healthy male volunteers orally at a dose of 200 mg following an overnight fast according to a randomized cross-over design. Scheduled blood samples were collected, centrifuged and the plasma assayed using a sensitive and validated reversed phase high performance liquid chromatographic (RP-HPLC) method. Various pharmacokinetic parameters were calculated based on the non-compartmental model. Results: Non-significant difference (p < 0.05) was observed in the area under the curve (AUC) of the two brands with values of 10.8 ± 0.4 µg h/ml and 11.2 ± 0.7 µg h/ml, respectively. There was, however, a significant difference (p < 0.05) in the Cmax of the two brands. Other pharmacokinetic parameters evaluated did not show any statistical difference (p < 0.05) between the two products except for mean residence time Conclusion: The test product can be used as an alternative to the brand, Silliver®-Abbot (reference), only in conditions where maximum plasma concentration (Cmax) is not an important consideration

Why climate change is urgent

Summarization: Carbon dioxide (CO2) is the main greenhouse gas (GHG) in the atmo- sphere responsible for long-term global warming, and scientific evidence indicates that the current CO2 concentration is proba- bly the highest in the last 15 million years (World Bank 2012)—more than 391 parts per million (ppm), com- pared to the preindustrial level of 278 ppm. CO2 emis- sions grew 1.1 percent per year from 1990 to 1999 but since 2000 they have been growing by more than 3 per- cent per year (Gowdy 2010). The National Oceanic and Atmospheric Administration (NOAA) reported a reading of CO2 at Mauna Loa of 400.03 ppm on May 9, 2013, crossing for the first time the 400 ppm mark.1 Global warming due to past anthropogenic CO2 emis- sions is irreversible for at least 1,000 years, and current and future CO2 emissions will result in additional warm- ing (Matthews and Solomon 2013). The international community has set the goal of stabilizing global warm- ing at no more than 2°C above preindustrial levels by 2100, while the Small Island Developing States (SIDS; www.sidsnet.org) have set it at 1.5°C. But given cur- rent emission levels and minimal international action to mitigate climate change, “there is roughly a 20 per- cent likelihood of exceeding 4°C by 2100” (World Bank 2012, p. 1). It is still possible, however, to keep global warming within tolerable limits through the use of appropriate technologies to replace fossil fuel consumption with other energy sources and the application of interna- tional political will to change course and control cli- mate change. Any delay of such action will commit the planet to higher and higher temperatures that will become irreversible in the foreseeable future. The likely consequences will be dire.Παρουσιάστηκε στο: The Bridg