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    Autonomic Cloud Resource Management

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    Author
    Tunc, Cihan
    Issue Date
    2015
    Keywords
    cloud computing
    cloud resource management
    high performance computing
    power minimization
    ppw
    Electrical & Computer Engineering
    autonomic computing
    Advisor
    Hariri, Salim
    Akoglu, Ali
    
    Metadata
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    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Embargo
    Release after 27-Jul-2015
    Abstract
    The power consumption of data centers and cloud systems has increased almost three times between 2007 and 2012. The traditional resource allocation methods are typically designed for high performance as the primary objective to support peak resource requirements. However, it is shown that server utilization is between 12% and 18%, while the power consumption is close to those at peak loads. Hence, there is a pressing need for devising sophisticated resource management approaches. State of the art dynamic resource management schemes typically rely on only a single resource such as core number, core speed, memory, disk, and network. There is a lack of fundamental research on methods addressing dynamic management of multiple resources and properties with the objective of allocating just enough resources for each workload to meet quality of service requirements while optimizing for power consumption. The main focus of this dissertation is to simultaneously manage power and performance for large cloud systems. The objective of this research is to develop a framework of performance and power management and investigate a general methodology for an integrated autonomic cloud management. In this dissertation, we developed an autonomic management framework based on a novel data structure, AppFlow, used for modeling current and near-term future cloud application behavior. We have developed the following capabilities for the performance and power management of the cloud computing systems: 1) online modeling and characterizing the cloud application behavior and resource requirements; 2) predicting the application behavior to proactively optimize its operations at runtime; 3) a holistic optimization methodology for performance and power using number of cores, CPU frequency, and memory amount; and 4) an autonomic cloud management to support the dynamic change in VM configurations at runtime to simultaneously optimize multiple objectives including performance, power, availability, etc. We validated our approach using RUBiS benchmark (emulating eBay), on an IBM HS22 blade server. Our experimental results showed that our approach can lead to a significant reduction in power consumption upto 87% when compared to the static resource allocation strategy, 72% when compared to adaptive frequency scaling strategy, and 66% when compared to a multi-resource management strategy.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Electrical & Computer Engineering
    Degree Grantor
    University of Arizona
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