Abstract
In context-aware systems, the contextual information about human and computing situations has a strong temporal aspect i.e. it remains valid for a period of time. This temporal property can be exploited in caching mechanisms that aim to exploit such locality of reference. However, different types of contextual information have varying temporal validity durations and a varied spectrum of access frequencies as well. Such variation affects the suitability of a single caching strategy and an ideal caching mechanism should utilize dynamic strategies based on the type of context data, quality of service heuristics and access patterns and frequencies of context consuming applications. This paper presents an investigation into the utility of various context-caching strategies and proposes a novel bipartite caching mechanism in a Cloud-based context provisioning system. The results demonstrate the relative benefits of different caching strategies under varying context usage scenarios. The utility of the bipartite context caching mechanism is established both through simulation and deployment in a Cloud platform.
Highlights
Context in computing terms is the information related to the users of computing systems, which includes their personal situations, digital and physical environmental characteristics
With a significantly large number of users, devices, data sources and services involved in the end-to-end cycle of acquisition, reasoning, delivery and consumption of context information, inadequate infrastructure support in terms of storage, processing, and provisioning of contextual information can be the biggest hurdle in adoption of context-aware systems over a large scale
We analyze the mean query satisfaction time of these caching strategies in the cases where scope distribution varies from being fully focused on short validity (SV) scopes to long validity (LV) scopes in increments of 25% i.e. the distributions range from (1.0 SV/0.0 LV), (0.75 SV/0.25 LV), (0.5 SV/0.5 LV), (0.25 SV/0.75 LV) and (0.0 SV/1.0 LV)
Summary
Context in computing terms is the information related to the users of computing systems, which includes their personal situations, digital and physical environmental characteristics. The temporal properties of contextual data are not utilized by existing contextaware systems to improve context provisioning performance through caching, grid and cloud based platforms. Context information remains temporally valid for a certain duration, which depends on the type of context data. This property of the context data can be exploited by employing context caches in context provisioning systems to improve the overall system performance as done routinely in distributed systems. Caching is a well established performance improvement mechanism in distributed systems, and if employed in Cloud based context provisioning systems, can augment its infrastructure strengths and further improve the context provisioning function
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