Efficient Storage Management for Social Network Events Based on Clustering and Hot / Cold Data in Cloud Classification

Abstract

Effectively managing the storage of extensive social network event data presents substantial challenges due to its sheer volume and diverse access patterns. This paper introduces a comprehensive approach that utilizes clustering techniques and hot/cold data classification to enhance storage efficiency for social network events. The proposed methodology integrates content-based, temporal, and graph-based clustering methods to organize events

nto cohesive clusters. A hot/cold data classification strategy distinguishes frequently accessed "hot" data from less accessed "cold" data, enabling their storage in respective tiers of high-speed access and cost-effective solutions.

Various strategies, including tiered storage, compression, automatic data movement, and caching mechanisms, are explored to efficiently handle the storage infrastructure. Additionally, the paper emphasizes scalability considerations and monitoring mechanisms to ensure adaptability to evolving data volumes and user behaviours. The combination of clustering methodologies and data classification not only improves accessibility and retrieval efficiency but also optimizes storage resources, striking a balance between performance and cost-effectiveness. This framework serves as a roadmap for designing robust storage architectures tailored for social network event data, guaranteeing optimized access and management while accommodatingscalability and adapting to evolving usage patterns.