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Languages: English
Types: Doctoral thesis
Subjects:
This research focuses on automatically adapting a search engine size in response to fluctuations in query workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computer resources to or from the engine. Our solution is to contribute an adaptive search engine that will repeatedly re-evaluate its load and, when appropriate, switch over to a dierent number of active processors. We focus on three aspects and break them out into three sub-problems as follows: Continually determining the Number of Processors (CNP), New Grouping Problem (NGP) and Regrouping Order Problem (ROP). CNP means that (in the light of the changes in the query workload in the search engine) there is a problem of determining the ideal number of processors p active at any given time to use in the search engine and we call this problem CNP. NGP happens when changes in the number of processors are determined and it must also be determined which groups of search data will be distributed across the processors. ROP is how to redistribute this data onto processors while keeping the engine responsive and while also minimising the switchover time and the incurred network load. We propose solutions for these sub-problems. For NGP we propose an algorithm for incrementally adjusting the index to t the varying number of virtual machines. For ROP we present an ecient method for redistributing data among processors while keeping the search engine responsive. Regarding the solution for CNP, we propose an algorithm determining the new size of the search engine by re-evaluating its load. We tested the solution performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud. Our experiments show that when we compare our NGP solution with computing the index from scratch, the incremental algorithm speeds up the index computation 2{10 times while maintaining a similar search performance. The chosen redistribution method is 25% to 50% faster than other methods and reduces the network load around by 30%. For CNP we present a deterministic algorithm that shows a good ability to determine a new size of search engine. When combined, these algorithms give an adapting algorithm that is able to adjust the search engine size with a variable workload.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • [48] T.F. Abdelzaher, J.A. Stankovic, C. Lu, R. Zhang, and Y. Lu, Feedback Performance Control in Software Services, IEEE Control Systems, vol. 23, no. 3, June 2003.
    • [49] C. Lu, J.A. Stankovic, G. Tao, and S.H. Son, Feedback Control Real-Time Scheduling: Framework, Modeling, and Algorithms, Real-Time Systems J., vol. 23, no. 1/2, pp. 85-126, 2002.
    • [50] Lim, H. C., Babu, S., Chase, J. S., and Parekh, S. S. (2009, June). Automated control in cloud computing: challenges and opportunities. Proceedings of the 1st workshop on Automated control for datacenters and clouds (pp. 13-18). ACM.
    • [51] S. Parekh, N. Gandhi, J. Hellerstein, D. Tilbury, T. Jayram, and J. Bigus. Using control theory to achieve service level objectives in performance management. In Proc. of IM, 2002.
    • [52] G. Soundararajan, C. Amza, and A. Goel. Database replication policies for dynamic content applications. In Proc. of EuroSys, 2006.
    • [53] B. Urgaonkar, P. Shenoy, A. Chandra, and P. Goyal. Dynamic provisioning of multitier internet applications. In Proc. of ICAC, 2005.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

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