An Adaptive Query Search and Data Retrieval Techniques for Content Distribution in Distributed Computing Network
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Object-On-Demand in Distributed computing has been an active area of research for the past few years. Replication techniques have greatly improved the availability of popular objects, enhancing the performance of query latency, fault tolerant, reliability and load balancing. However, caching, replication and searching objects on a large-scale distributed system is very challenging tasks due to nodes dynamicity that can compromise data availability according to the popularity of object. The main problem is to determine where to replicate? When to replicate? And how many copies to create replica? In order to solve this problem, An Adaptive query search and data retrieval algorithm for content distribution in distributed computing network is proposed. The solution proposed is done in two phases based on the cost model which could be taken account for several factors such as processor speed of nodes, access latency, and the flow of bandwidth and storage capacity of nodes. Routing is performed hierarchically by broadcasting the query to the strong and medium clusters respectively. From wide simulations using NS2 simulator, the proposed algorithm achieves less bandwidth consumption, reduced latency, increased system performance, high throughput, and strong connectivity and query coverage than the existing method.
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