Abstract | ||
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This paper is concerned with data provisioning services (information search, retrieval, storage, etc.) dealing with a large and heterogeneous information repository. Increasingly, this class of services is being hosted and delivered through Cloud infrastructures. Although such systems are becoming popular, existing resource management methods (e.g. load-balancing techniques) do not consider workload patterns nor do they perform well when subjected to non-uniformly distributed datasets. If these problems can be solved, this class of services can be made to operate in more a scalable, efficient, and reliable manner.The main contribution of this paper is a approach that combines proprietary cloud-based load balancing techniques and density-based partitioning for efficient range query processing across relational database-as-a-service in cloud computing environments. The study is conducted over a real-world data provisioning service that manages a large historical news database from Thomson Reuters. The proposed approach has been implemented and tested as a multi-tier web application suite consisting of load-balancing, application, and database layers. We have validated our approach by conducting a set of rigorous performance evaluation experiments using the Amazon EC2 infrastructure. The results prove that augmenting a cloud-based load-balancing service (e.g. Amazon Elastic Load Balancer) with workload characterization intelligence (density and distribution of data; composition of queries) offers significant benefits with regards to the overall system's performance (i.e. query latency and database service throughput). |
Year | DOI | Venue |
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2013 | 10.1007/s11227-011-0717-y | The Journal of Supercomputing |
Keywords | Field | DocType |
Range query processing,Load balancing,Data density,Cloud computing | Load balancing (computing),Workload,Computer science,Range query (data structures),Parallel computing,Provisioning,Information repository,Web application,Database,Scalability,Cloud computing,Distributed computing | Journal |
Volume | Issue | ISSN |
63 | 3 | 0920-8542 |
Citations | PageRank | References |
5 | 0.44 | 16 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Adnene Guabtni | 1 | 118 | 8.61 |
Rajiv Ranjan | 2 | 4747 | 267.72 |
Fethi Rabhi | 3 | 427 | 50.68 |