NDLI: SARP: producing approximate results with small correctness losses for cloud interactive services

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SARP: producing approximate results with small correctness losses for cloud interactive services

Content Provider	ACM Digital Library
Author	Ge, Fengming Han, Rui Zhan, Jianfeng Vazquez-Poletti, Jose Luis Wang, Junwei
Abstract	Despite the importance of providing fluid responsiveness to user requests for interactive services, such request processing is very resource expensive when dealing with large-scale input data. These often exceed the application owners' budget when services are deployed on a cloud, in which resources are charged in monetary terms. Providing approximate processing results is a feasible solution for such problem that trades off request correctness (quantified by output quality) for response time reduction. However, existing techniques in this area either use partial input data or skip expensive computations to produce approximate results, thus resulting in large losses in output quality on a tight resource budget. In this paper, we propose SARP, a Synopsis-based Approximate Request Processing framework to produce approximate results with small correctness losses even using small amount of resources. To achieve this, SARP conducts full computations over the statistical aggregation of the entire input data using two key ideas: (1) offline synopsis management that generates and maintains a set of synopses that represent the statistical aggregation of original input data at different approximation levels. (2) Online synopsis selection that considers both the current resource allocation and the workload status so as to select the synopsis with the maximal length that can be processed within the required response time. We demonstrate the effectiveness of our approach by testing the recommendation services in E-commerce sites using a large, real-world dataset. Using prediction accuracy as the output quality, the results demonstrate: (i) SARP achieves significant response time reduction with very small quality losses compared to the exact processing results.(ii) Using the same processing time, SARP demonstrates a considerable reduction in quality loss compared to existing approximation techniques.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450333580
DOI	10.1145/2742854.2742858
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Interactive service Cloud Output quality Result correctness Synopsis Approximate results
Content Type	Text
Resource Type	Article

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