OPTIMIZING DATA REPLICATION AND PARTITIONING STRATEGIES IN DISTRIBUTED SYSTEMS TO BOOST EQUILIBRIUM AND SCALABILITY

Abstract

Modern distributed systems confront exabyte scale data challenges requiring sophisticated replication partitioning co optimization to achieve system equilibrium balanced throughput, latency, availability and cost. This comprehensive study employs mixed methods research (10,000+ simulations, 120hr bare metal benchmarks) across OLTP, analytics and streaming workloads demonstrating adaptive tiered replication (Hot:R=5/Warm:R=3/Cold:R=2) achieves 32% storage savings versus uniform R=3 while maintaining 99.99% uptime. ML driven intelligent partitioning reduces p99 tail latency 28% (8.2ms vs 11.4ms) and unlocks 2.1x throughput (847K vs 402K QPS). Cross shard TPC-C joins accelerate 4.7x (43ms vs 202ms) making distributed SQL viable for transactional OLTP. Equilibrium surface analysis reveals workload specific optima challenging R=3 dogma.