In context-aware systems, the contextual information about human and computing situations has a strong temporal\r\naspect i.e. it remains valid for a period of time. This temporal property can be exploited in caching mechanisms that\r\naim to exploit such locality of reference. However, different types of contextual information have varying temporal\r\nvalidity durations and a varied spectrum of access frequencies as well. Such variation affects the suitability of a single\r\ncaching strategy and an ideal caching mechanism should utilize dynamic strategies based on the type of context\r\ndata, quality of service heuristics and access patterns and frequencies of context consuming applications. This paper\r\npresents an investigation into the utility of various context-caching strategies and proposes a novel bipartite caching\r\nmechanism in a Cloud-based context provisioning system. The results demonstrate the relative benefits of different\r\ncaching strategies under varying context usage scenarios. The utility of the bipartite context caching mechanism is\r\nestablished both through simulation and deployment in a Cloud platform
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