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Unformatted text preview: USENIX Association FAST 10: 8th USENIX Conference on File and Storage Technologies 1 quFiles: The right file at the right time Kaushik Veeraraghavan # , Jason Flinn # , Edmund B. Nightingale and Brian Noble # University of Michigan # Microsoft Research (Redmond) Abstract A quFile is a unifying abstraction that simplifies data management by encapsulating different physical repre- sentations of the same logical data. Similar to a quBit (quantum bit), the particular representation of the logi- cal data displayed by a quFile is not determined until the moment it is needed. The representation returned by a quFile is specified by a data-specific policy that can take into account context such as the application requesting the data, the device on which data is accessed, screen size, and battery status. We demonstrate the general- ity of the quFile abstraction by using it to implement six case studies: resource management, copy-on-write versioning, data redaction, resource-aware directories, application-aware adaptation, and platform-specific en- coding. Most quFile policies were expressed using less than one hundred lines of code. Our experimental results show that, with cachingandother performanceoptimiza- tions, quFiles add less than 1% overhead to application- level file system benchmarks. 1 Introduction It has become increasingly common for new stor- age systems to implement context-aware adaptation , in which different representations of the same object are re- turned based on the context in which the object is ac- cessed. For instance, many systems transcode data to meet the screen size constraintsof mobile devices [5, 12]. Others display reduced fidelity representations to meet constraints on resources such as network bandwidth [8, 27] and battery energy , display redacted representa- tions of data files when they are viewed at insecure loca- tions [22, 42], and create different formats of multimedia data for diverse devices . These systems, and many others, have been successful at addressing specific needs for adapting the representa- tion of data to fit a given context. However, they suffer from several problems that inhibit their wide-scale adop- tion. First, building such systems is time-consuming. Most required several person-years to build a prototype; porting them to mainstream environments would be dif- ficult at best. Second, each system presents a different abstraction and interface, so each has a learning curve. Third, these systems typically present only a single logi- cal view of data, making it difficult for users to pierce the abstraction and explicitly choose different presentations. Why are there so many systems that share the same premise, yet have completely separate implementations?...
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- Spring '11