Person:
Salinas Hilburg, Juan Carlos

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First Name
Juan Carlos
Last Name
Salinas Hilburg
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https://hdl.handle.net/20.500.14352/82592
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Universidad Complutense de Madrid
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    Rapid runtime power and performance profiling of large scale applications
    (2021) Salinas Hilburg, Juan Carlos; Ayala Rodrigo, José Luis; Zapater Sancho, Marina; Moya Fernádez, José Manuel
    Data centers are one of the most power hungry sections of the Information and Communications Technologies (ICT) sector. In the U.S. in 2014, data centers consumed around the 1.8% of the total U.S. electricity consumption. Worldwide data centers consumed in 2015 around 200 TWh of the global electricity usage. This electricity consumption is expected to increase to around 1200 TWh in 2025, which would represent 4.% of the global electricity usage. One of the mejor contributors to the overall data center power is the IT or computing power, therefore there is a special interest to imporve its energy efficiency. Scientific community has developed energy efficient techniques to reduce the energy consumption of IT equipment, such as resource management, power budgeting or power capping...
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    Energy-aware task scheduling in data centers using an application signature
    (Computers & Electrical Engineering, 2021) Salinas Hilburg, Juan Carlos; Zapater, Marina; Moya, José M.; Ayala Rodrigo, José Luis
    Data centers are power hungry facilities. Energy-aware task scheduling approaches are of utmost importance to improve energy savings in data centers, although they need to know beforehand the energy consumption of the applications that will run in the servers. This is usually done through a full profiling of the applications, which is not feasible in long-running application scenarios due to the long execution times. In the present work we use an application signature that allows to estimate the energy without the need to execute the application completely. We use different scheduling approaches together with the information of the application signature to improve the makespan of the scheduling process and therefore improve the energy savings in data centers. We evaluate the accuracy of using the application signature by means of comparing against an oracle method obtaining an error below 1.5%, and Compression Ratios around 39.7 to 45.8.