Cache Partitioning is a technique which maps the data pertaining to each core to acorresponding partition in the cache memory exclusively. Cache partitioning has beenshown to improve performance metrics like fairness and throughput in most cases byeliminating inter-core conflict misses in shared cache of modern multi-core processors.Recently real-time management of cache partitioning is being studied to accommodate thevariations in the intrinsic behavior of threads running the cores; thereby further improvingcache utilization.This dissertation presents a novel scheme for real-time management of cache partitioningusing a constrained-extended Kalman filter. This approach is named Predictive Modelbased Cache Partitioning (PMCP). The design of PMCP utilizes an evolving approximatemodel of the nonlinear relationship between observed performance of each thread and theallocated cache partition size. The Gradient Projection method is used to model theperformance model which predicts the next partition configuration. It also utilizes thehistory of the transient behaviors of the active threads to predict the cache partitioning fora low computation and space overhead. The key contribution of the research is that thecache performance curves are generated dynamically and is used to predict partitioningstrategies such that cache utilization is optimized. PMCP is evaluated on the GEM5simulator using the SPEC CPU2006 benchmarks. The results show that the throughput ofthe system improves by up to 35% with PMCP over shared cache.