Despite being Asia’s fastest-growing economy, as of 2015, the Asian Development Bank (ADB) ranked the Philippines 33rd out of 48 countries in terms of water security. This verifies that economic development does not always lead to better provisions of basic needs. This study attempts to discover the fundamental issues that decrease water security in Metro Manila, the capital region of the Philippines. With El Niño disrupting the optimal weather conditions, Metro Manila is facing the lasting impacts of a water shortage crisis, which is the worst in the past decade. This research inspects the role of climate change in exacerbating El Niño, and its threat to the water security of the developing city. Furthermore, other factors that influence Metro Manila’s water security are discussed. Upon establishing a correlation between climate change and El Niño, Metro Manila’s general water management strategy is evaluated to better assess the multiple factors that have led to the current water shortage crisis. This paper is intended to recommend necessary and feasible proactive measures that are geared towards water security in Metro Manila, and possibly other cities with similar circumstances.
The United Nations (UN) expectations for 2030 account for a renewable, affordable, and eco-friendly energy future. The 2030 agenda includes 17 different Sustainable Development Goals (SDGs) for countries worldwide. In...
This paper proposes Hermes, a container-based preemptive GPU scheduling framework for accelerating hyper-parameter optimization in deep learning (DL) clusters. Hermes accelerates hyper-parameter optimization by time-sharing between DL jobs and prioritizing jobs with more promising hyper-parameter combinations. Hermes’s scheduling policy is grounded on the observation that good hyper-parameter combinations converge quickly in the early phases of training. By giving higher priority to fast-converging containers, Hermes’s GPU preemption mechanism can accelerate training. This enables users to find optimal hyper-parameters faster without losing the progress of a container. We have implemented Hermes over Kubernetes and compared its performance against existing scheduling frameworks. Experiments show that Hermes reduces the time for hyper-parameter optimization up to 4.04 times against previously proposed scheduling policies such as FIFO, round-robin (RR), and SLAQ, with minimal time-sharing overhead.
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