Scalable cooperative caching algorithm based on bloom filters

This thesis presents the design, implementation and evaluation of a novel cooperative caching algorithm based on the bloom filter data structure. The new algorithm uses a decentralized approach to resolve the problems that prevent the existing solutions from being scalable. The problems consist of an overloaded manager, a communication overhead among clients, and a memory overhead on the global cache. The new solution reduces the manager load and the communication overhead by distributing the global cache information among cooperating clients. Thus, the manager no longer maintains the global cache. Furthermore, the memory overhead is decreased due to a bloom filter data structure. The bloom filter saves memory space in the global cache and makes the new algorithm scalable. The correctness of the research hypothesis is verified by running experiments on the caching algorithms. The experiment results demonstrate that the new caching algorithm maintains a low block access time as existing algorithms. In addition, the new algorithm decreases the manager load by the factor of nine. Moreover, the communication overhead is reduced by nearly a factor of six as a result of distributing the global cache to clients. Finally, the results show a significant reduction in the memory overhead which also contributes to the scalability of the new algorithm