Certain scientific application domains, such as High-Energy Physics or Earth Observation, are expected to produce several Petabytes (220 Gigabyes) of data that is analyzed and evaluated by the scientists all over the world. In the context of data grid technology, data replication is mostly used to reduce access latency and bandwidth consumption. In this thesis, we adopt the typical Data Grid architecture, three kinds of nodes: server, cache, and client nodes. A server node represents a main storage site. A client node represents a site where data access requests are generated, and a cache node represents an intermediate storage site. However, the access latency of the hierarchical storage system may be of the order of seconds up to hours. The static replication strategy can be used to improve such long delay; however, it cannot adapt to changes of users¡¦ behaviors. Therefore, the dynamic data replication strategy is used in Data Grids. Three fundamental design issues in a dynamic replication strategy are: (1) when to create the replicas, (2) which files to be replicated, and (3) where the replicas to be placed. Two of well known replication strategies are Fast-Spread and Cascading, which can work well for different kinds of access patterns individually. For example, the Fast-Spread strategy works well for random access patterns, and the Cascading strategy works well for the patterns with the properties of localities. However, for so many different access patterns, if we use a strategy for one kind of access patterns and another strategy for another kind of access patterns, the system may become too complex. Therefore, in this thesis, we propose one strategy which can work for any kind of access patterns. We propose a replication approach, a Different Threshold (DT) approach to data replication in Data Grids, which can be dynamically adapted to several kinds of access patterns and provide even better performance than Cascading and Fast-Spread strategies. In our approach, there are different thresholds for different layers. Based on this approach, first, we propose a static DT strategy in which the threshold at each layer is fixed. So, by carefully adjusting the difference between the thresholds Ti, where i is the i-th layer of the tree structure, we can even provide the better performance than the above two well-known strategies. Moreover, among large amount of different data files, there may exist some hot data files. Those files which have been mostly requested are hot data files. To reduce the number of requests for the hot files, next, we propose the dynamic DT strategy. In the dynamic DT strategy, each data file even has its own threshold. We let data replication of hot files occur earlier than others by decreasing the thresholds of hot files earlier than the normal ones. From our simulation results, we show that the response time in our static DT strategy is less than that in the Cascading and the Fast-Spread strategies. Moreover, we can show that the performance of the dynamic DT strategy is better than that of the static DT strategy.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0121108-024702 |
| Date | 21 January 2008 |
| Creators | Huang, Yen-Wei |
| Contributors | Chien-I Lee, Ye-In Chang, San-Yi Huang |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0121108-024702 |
| Rights | not_available, Copyright information available at source archive |
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