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PhD Thesis defense: Structural Issues and Energy Efficiency in Data Centers

Jordi Arjona Aroca, PhD Student, IMDEA Networks Institute
13 February 2015 - 10:00am

 “Sala Adoración de Miguel”, Room 1.2.C.16, University Carlos III of Madrid, Avda. Universidad 30, 28911 Leganes - Madrid 

With the rise of cloud computing, data centers have been called to play a main role in the Internet scenario nowadays. Despite this relevance, they are probably far from their zenith yet due to the ever increasing demand of contents to be stored in and distributed by the cloud, the need of computing power or the larger and larger amounts of data being analyzed by top companies such as Google, Microsoft or Amazon.

However, everything is not always a bed of roses. Having a data center entails two major issues: they are terribly expensive to build, and they consume huge amounts of power being, therefore, terribly expensive to maintain. For this reason, cutting down the cost of building and increasing the energy efficiency (and hence reducing the carbon footprint) of data centers has been one of the hottest research topics during the last years.
In this thesis we propose different techniques that can have an impact in both the building and the maintenance costs of data centers of any size, from small scale to large flagship data centers.

The first part of the thesis is devoted to structural issues. We start by analyzing the bisection (band)width of a topology, of product graphs in particular, a useful parameter to compare and choose among different data center topologies. In that same part we describe the problem of deploying the servers in a data center as a Multidimensional Arrangement Problem (MAP) and propose a heuristic to reduce the deployment and wiring costs.

We target energy efficiency in data centers in the second part of the thesis. We first propose a method to reduce the energy consumption in the data center network: rate adaptation. Rate adaptation is based on the idea of energy proportionality and aims to consume power on network devices proportionally to the load on their links. Our analysis proves that just using rate adaptation we may achieve average energy savings in the order of a 30-40% and up to a 60% depending on the network topology.

We continue by characterizing the power requirements of a data center server given that, in order to properly increase the energy efficiency of a data center, we first need to understand how energy is being consumed. We present an exhaustive empirical characterization of the power requirements of multiple components of data center servers, namely, the CPU, the disks, and the network card. To do so, we devise different experiments to stress these components, taking into account the multiple available frequencies as well as the fact that we are working with multicore servers. In these experiments, we measure their energy consumption and identify their optimal operational points.
Our study proves that the curve that defines the minimal power consumption of the CPU, as a function of the load in Active Cycles Per Second (ACPS), is neither concave nor purely convex. Moreover, it definitively has a superlinear dependence on the load. We also validate the accuracy of the model derived from our characterization by running different Hadoop applications in diverse scenarios obtaining an error below 4.1% on average.

The last topic we study is the Virtual Machine Assignment problem (VMA), i.e., optimizing how virtual machines (VMs) are assigned to physical machines (PMs) in data centers. Our optimization target is to minimize the power consumed by all the PMs when considering that power consumption depends superlinearly on the load.
We study four different VMA problems, depending on whether the number of PMs and their capacity are bounded or not. We study their complexity and perform an offline and online analysis of these problems. The online analysis is complemented with simulations that show that the online algorithms we propose consume substantially less power than other state of the art assignment algorithms.
 

About Jordi Arjona

Jordi Arjona graduated from his studies in Telecommunications Engineering at the Polytechnic University of Valencia, Spain, in 2008. He worked on his senior thesis defense in the Mechanical Engineering Department at the University of Maryland, where he stayed with a university grant during 8 months as a student and, later, as a visiting researcher (2008). His work there led to the publication Analyzing the Process of Installing Rogue Software (Berthier, R.; Arjona, J.; Cukier M. DSN2009: 560-565).

Later on he was employed as a research assistant at the Polytechnic University of Valencia and started a Masters on automatics and industrial computer systems. At this period he was involved in another minor publication: Arquitectura y desarrollo del robot humanoide microBIRO-II (Muñoz, M.; Arjona, J.; XXX Jornadas de Automática 2009).

Before joining Institute IMDEA NETWORKS, he spent a year working as a teacher and as a Java developer at Indra Systems, Valencia (Spain).

 

The thesis defense will be conducted in English

More Info: 

PhD Thesis Advisors: Prof. Dr. Antonio Fernández Anta, IMDEA Networks Institute