Auspice: Automatic service planning in cloud/grid environments .

摘要

Scientific advancements have ushered in staggering amounts of available data and processes which are now scattered across various locations in the Web, Grid, and more recently, the Cloud. These processes and data sets are often semantically loosely-coupled and must be composed together piecemeal to generate scientific work flows. Understanding how to design, manage, and execute such data-intensive work flows has become increasingly esoteric, confined to a few scientific experts in the field. Despite the development of scientific work flow management systems, which have simplified work flow planning to some extent, a means to reduce the complexity of user interaction without forfeiting some robustness has been elusive. This violates the essence of scientific progress, where information should be accessible to anyone. A high-level querying interface tantamount to common search engines that can not only return a relevant set of scientific work flows, but also facilitate their execution, may be highly beneficial to users.;The development of such a system that can abstract the complex task of scientific work flow planning and execution from the user is reported herein. Our system, Auspice: A Utomatic Service Planning In Cloud/Grid Environments , consists of the following key contributions. Initially, a two-level metadata management framework is introduced. In the top-level, Auspice captures semantic dependencies among available, shared processes and data sets with an ontology. Our system furthermore indexes these shared resources for facilitating fast planning times. This metadata framework enables an automatic work flow composition algorithm, which exhaustively enumerates relevant scientific work flow plans given a few key parameters - a marked departure from requiring users to design and manage work flow plans.;By applying models on processes, time-critical and accuracy-aware constraints can be realized in this planning algorithm. During the planning phase, Auspice projects these costs and prunes work flow plans in an a priori fashion if they cannot meet the specified constraints. Conversely, when feasible, Auspice can adapt to certain time constraints by trading accuracy for time. To simplify user interaction, both natural language and keyword search interfaces have been developed to invoke the said work flow planning algorithm. Intermediate data caching strategies have also been implemented to accelerate work flow execution over emerging Cloud environments. A focus on cache elasticity is reported, and to this end, we have developed methods to scale and relax resource provisioning for cooperating data caches. Finally, costs of supporting such data caches over various Cloud storage and compute resources have been evaluated.