In this dissertation we study the problem of allocating computational resources and
managing applications in a data center to serve incoming requests in such a way that the
energy usage, reliability and quality of service considerations are balanced. The problem is
motivated by the growing energy consumption by data centers in the world and their overall
inefficiency. This work is focused on designing flexible and robust strategies to manage the
resources in such a way that the system is able to meet the service agreements even when
the load conditions change. As a first step, we study the control of a Markovian queueing
system with controllable number of servers and service rates (M=Mt=kt ) to minimize
effort and holding costs. We present structural properties of the optimal policy and suggest
an algorithm to find good performance policies even for large cases. Then we present
a reactive/proactive approach, and a tailor-made wavelet-based forecasting procedure to
determine the resource allocation in a single application setting; the method is tested by
simulation with real web traces. The main feature of this method is its robustness and flexibility
to meet QoS goals even when the traffic behavior changes. The system was tested
by simulating a system with a time service factor QoS agreement. Finally, we consider
the multi-application setting and develop a novel load consolidation strategy (of combining
applications that are traditionally hosted on different servers) to reduce the server-load
variability and the number of booting cycles in order to obtain a better capacity allocation.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-3182 |
| Date | 15 May 2009 |
| Creators | Rincon Mateus, Cesar Augusto |
| Contributors | Gautam, Natarajan |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, application/pdf, born digital |
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