Logically Clustered Architectures for Networked Databases
Pages 161 - 198
Abstract
By effectively harnessing networked computing resources, the two-tier client-server model has been used to support shared data access. In systems based on this approach, the database servers often become performance bottlenecks when the number of concurrent users is large. Client data caching techniques have been proposed in order to ease resource contention at the servers. The key theme of these techniques is the exploitation of user data access locality. In this paper, we propose a three-tiered model that takes advantage of such data access locality to furnish a much more scalable system. Groups of clients that demonstrate similarities in their data access behavior are logically clustered together. Each such group of clients is handled by an Intermediate Cluster Manager (ICM) that acts as a cluster-wide directory service and cache manager. Clients within the same cluster are now capable of sharing data among themselves without interacting with the server(s). This results in reduced server load and allows the support of a much larger number of clients. Through prototyping and experimentation, we show that the logical clustering of clients, and the introduction of the ICM layer, significantly improve system scalability as well as transaction response times. Logical clusters, consisting of clients with similar data access patterns, are identified with the help of both a greedy algorithm and a genetic algorithm. For the latter, we have developed an encoding scheme and its corresponding operators.
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Index Terms
- Logically Clustered Architectures for Networked Databases
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Reviews
Athena Vakali
Database systems have been introduced in domains that involve complex information processing. These systems must handle high volumes of data between sites in networked environments. Contemporary databases, which are based on the client-server model, are widely used to meet these challenges. In this paper, two alternative architectures to the client-sever model are proposed. The key feature of these configurations is logical client clustering. By analyzing the data access patterns of involved sites, clients that access similar segments of a database are grouped together. Each cluster of clients is managed by intermediate cluster managers (ICMs), which are connected to the existing database server or servers. The paper begins by describing the two network database architectures: logically clustered client-server database (LC-CS), and its extended version (extended LC-CS). In the extended version, the ICMs support caching techniques. The authors then perform an analysis of the two types of configurations, based on the probability of object request satisfaction at clients, and within the clusters. Two clustering algorithms (greedy and genetic), which are used to cluster clients with the same database access patterns, are described next. Readers should have a background in such algorithms, since the paper itself doesn't provide enough detail to understand them. More specifically, the example for the genetic algorithm is not very clearly explained (Figure 7 has some missing points). The authors then describe the experimental evaluation of the two architectures, which are compared with the client-server model. The performance of the proposed environment is examined by varying the number of clients and the workload. The experiment section of the paper is very well organized, and includes a complete view of the experiments carried out. Finally, the authors provide a good overview of related work on client clustering in distributed file systems. This paper presents well-organized and important work. The paper is intended for database specialists, particularly those interested in the application of artificial intelligence technology to database systems. Online Computing Reviews Service
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Published: 01 September 2001
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