Energy Efficient Big Data Infrastructure Management in Geo-Federated Cloud Data Centers

The hot-tempered development of hassle on big data processing make obligatory an intense load on computation, storage and networking in data centres. We suggested an approach of data centre node clustering for an efficient data placement and data retrieval which is unlike the routine in centralised architecture. The main objective for the proposed system is the shortcomings present in the conventional centralised server which is mainly the assumption that a single head is in the connectivity range of all other nodes. We proposed Hit Rate Geographical Locations Analysis Algorithm (HIRGLAA) for the dynamic election of cluster head based on the periodic hit rate analysis performance. We suggested candidate cluster heads containing redundant routing information to ensure data storage backup. Thus the proposed system assures Quality of Services (QoS) such as increased reliability, robustness, an energy efficient remote access and its efficiency can be validated by extensive simulation based studies.