A cost-model-based online method for distributed caching

Presents a method for distributed caching to exploit the aggregate memory of networks of workstations in data-intensive applications. In contrast to prior work, the approach is based on a detailed cost model as the basis for optimizing the placement of variable-size data objects in a distributed, possibly heterogeneous two-level storage hierarchy. To address the online problem with a priori unknown and evolving workload parameters, the method employs dynamic load tracking procedures and an approximative, low-overhead version of the cost model for continuous reoptimization steps that are embedded in the decisions of the underlying local cache managers. The method is able to automatically find a good tradeoff between an “egoistic” and an “altruistic” behavior of the network nodes, and proves its practical viability in a detailed simulation study under a variety of workload and system configurations