Proactive and reactive thermal aware optimization techniques to minimize the environmental impact of data centers

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  author =       "Marina {Zapater Sancho}",
  title =        "Proactive and reactive thermal aware optimization
                 techniques to minimize the environmental impact of data
  school =       "Ingenieria Electronica, Universidad Politecnica de
  year =         "2015",
  address =      "Madrid, Spain",
  keywords =     "genetic algorithms, genetic programming, Energy,
                 Energy-efficiency, Data Centres, Green Computing, Power
                 modelling, Temperature prediction, Cooling, Resource
                 management, Optimization",
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "149 pages",
  abstract =     "Data centres are easily found in every sector of the
                 worldwide economy. They consist of tens of thousands of
                 servers, serving millions of users globally and 24-7.
                 In the last years, e-Science applications such e-Health
                 or Smart Cities have experienced a significant
                 development. The need to deal efficiently with the
                 computational needs of next-generation applications
                 together with the increasing demand for higher
                 resources in traditional applications has facilitated
                 the rapid proliferation and growing of data centers. A
                 drawback to this capacity growth has been the rapid
                 increase of the energy consumption of these facilities.
                 In 2010, data centre electricity represented 1.3percent
                 of all the electricity use in the world. In year 2012
                 alone, global data centre power demand grew 63percent
                 to 38GW. A further rise of 17percent to 43GW was
                 estimated in 2013. Moreover, data centres are
                 responsible for more than 2percent of total carbon
                 dioxide emissions. This PhD Thesis addresses the energy
                 challenge by proposing proactive and reactive thermal
                 and energy-aware optimization techniques that
                 contribute to place data centres on a more scalable
                 curve. This work develops energy models and uses the
                 knowledge about the energy demand of the workload to be
                 executed and the computational and cooling resources
                 available at data centre to optimize energy
                 consumption. Moreover, data centres are considered as a
                 crucial element within their application framework,
                 optimizing not only the energy consumption of the
                 facility, but the global energy consumption of the
                 application. The main contributors to the energy
                 consumption in a data centre are the computing power
                 drawn by IT equipment and the cooling power needed to
                 keep the servers within a certain temperature range
                 that ensures safe operation. Because of the cubic
                 relation of fan power with fan speed, solutions based
                 on over-provisioning cold air into the server usually
                 lead to inefficiencies. On the other hand, higher chip
                 temperatures lead to higher leakage power because of
                 the exponential dependence of leakage on temperature.
                 Moreover, workload characteristics as well as
                 allocation policies also have an important impact on
                 the leakage-cooling tradeoffs. The first key
                 contribution of this work is the development of power
                 and temperature models that accurately describe the
                 leakage-cooling tradeoffs at the server level, and the
                 proposal of strategies to minimize server energy via
                 joint cooling and workload management from a
                 multivariate perspective. When scaling to the data
                 centre level, a similar behaviour in terms of
                 leakage-temperature tradeoffs can be observed. As room
                 temperature raises, the efficiency of data room cooling
                 units improves. However, as we increase room
                 temperature, CPU temperature raises and so does leakage
                 power. Moreover, the thermal dynamics of a data room
                 exhibit unbalanced patterns due to both the workload
                 allocation and the heterogeneity of computing
                 equipment. The second main contribution is the proposal
                 of thermal- and heterogeneity-aware workload management
                 techniques that jointly optimize the allocation of
                 computation and cooling to servers. These strategies
                 need to be backed up by flexible room level models,
                 able to work on runtime, that describe the system from
                 a high level perspective. Within the framework of
                 next-generation applications, decisions taken at this
                 scope can have a dramatical impact on the energy
                 consumption of lower abstraction levels, i.e. the data
                 center facility. It is important to consider the
                 relationships between all the computational agents
                 involved in the problem, so that they can cooperate to
                 achieve the common goal of reducing energy in the
                 overall system. The third main contribution is the
                 energy optimization of the overall application by
                 evaluating the energy costs of performing part of the
                 processing in any of the different abstraction layers,
                 from the node to the data center, via workload
                 management and off-loading techniques. In summary, the
                 work presented in this PhD Thesis, makes contributions
                 on leakage and cooling aware server modeling and
                 optimization, data centre thermal modelling and
                 heterogeneity aware data center resource allocation,
                 and develops mechanisms for the energy optimization for
                 next-generation applications from a multi-layer
  notes =        "Item ID 38700 Supervisors: Jose Manuel Moya Fernandez
                 and Jose Luis Ayala Rodrigo",

Genetic Programming entries for Marina Zapater