Optimization assisted frequent pattern mining for data replication in cloud: Combining sealion and grey wolf algorithm. (February 2023) - D, Rambabu; A, Govardhan
Highlights: Introduces an optimization-assisted frequent pattern mining approach for data replication. Introduces a new hybrid optimization referred as greywolves updated exploration and exploitation with sealion behaviour (GUEES), by blending the concept of SLnO and GWO algorithms. Undergoes mining based on dual-constraints: prioritization and cost. To optimize high priority queues with greywolves updated exploration and exploitation with sealion behaviour (GUEES) for better mining outcomes. Abstract: It is critical in cloud computing to have excellent data accessibility and system performance. To improve system availability, commonly used data should be duplicated to many places, allowing users to access it from a nearby site. Deciding on a sensible number and location for replicas is a difficult problem in cloud computing. Therefore, a novel Data Replication system based on data mining techniques is being proposed in this research work. The data replication is done here by locating commonly utilized data patterns in a node's massive database. This will be accomplished using an optimization-assisted frequent pattern mining approach, with a novel hybrid algorithm performing the best threshold selection. The proposed hybrid algorithm referred to as Greywolves Updated Exploration and Exploitation with Sealion Behaviour (GUEES), hybrids the concept of Sealion Optimization Model (SLnO) and Grey wolf optimization (GWO) algorithms. Apart from this, the mining will be carried out under the defining dual constraints such as (i) Prioritization and (ii) Cost. The prioritization falls under two cases: queuing both high and low-priority data, and the cost relies on the evaluation of storage demand. The high-priority queues are optimized with the GUEES model. Finally, a comparative validation is carried out to validate the efficiency of the adopted model. Accordingly, when the number of requests=1000, the network usage of the proposed model is 35.07%, 34.9%, 30.5%, 29.23%, 24.57%, 16.8%, and 16.85% higher than the existing methods like SMO, LA, ROA, GWO, SLnO, PSO, HCS, respectively.