Adaptive SSP forecast and memory reclamation using belief nets

Abstract

The expectations from computing systems are increasing every year. For systems to multitask and still be highly responsive, the necessary references and dependencies should be readily available in memory. Since the memory is limited, memory needs to be freed up from relatively old references so that new references can be loaded. In case of Distributed Systems having remote reference dependencies, Stub-Scion Pair (SSP) Creation and Recollection is a factor in responsiveness of the system. In this paper, Intelligent SSP Forecast and Memory Reclamation Strategy is proposed. It learns and adapts memory reclamation as per user behaviour and reference dependencies. Proposed method addresses better management of references and SSP by learning process dependency and usage patterns and adapting the local and remote reference creation and reclamation. Proposed strategy learns the user and process behaviour and builds a Bayesian Belief Net. Memory Reclamation Decision and Predictive SSP Forecast is based on status and inference from Belief Net.