Graduate Thesis Or Dissertation
 

Resource allocation optimization in large scale distributed systems

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/ff365874s

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  • We studied the problem of resource allocation in large scale distributed applications such as Online Social Networks (OSN) and Cloud Computing. In such settings, resource allocation schemes need to efficient as well as adaptive to the time-varying environments. The abstract resource allocation problem concerns with how to optimally use resources for different tasks. In the context of this dissertation, the resources are servers and the tasks are (a) the virtual machines in the cloud computing setting, and or users for on-line social network applications. It is well-known that the general resource allocation problem is NP-hard. Therefore, in this dissertation, we study a number of heuristic algorithms designed for two primary objectives: 1) achieve reliability via load balancing among resource providers and 2) minimizing the energy consumption by reducing unnecessary intercommunication loads among the servers. Specifically, the dissertation has three main components. The first component deals with optimal assignment of user data to servers to maximize load balance and minimize power consumption. In this component, we propose a novel Distributed Perturbed Greedy Search (DPGS) algorithm which combine both deterministic search and random search to speed the convergence while avoiding local optimum. The empirical shows that the DPGS has a fast convergence rate to the near optimal solution even when the environment changes. The second component deals with the analysis on the convergence rates of a general simulated annealing algorithm via the notion of adiabatic time. We then apply the results to characterize the convergence rates for simulated annealing algorithm when applied to the optimal assignment in the component one. Finally, the third component of the dissertation is concerned with optimal assignment of virtual machines to servers in the context of cloud computing, in order to minimize the energy subject to a given performance requirement. We show that the problem can be approximated well as a convex problem, and propose convex relaxation technique to find the optimal solution.
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