Getting ready for approximate computing

Trancoso, Pedro

doi:10.1145/2597917.2597936

Cited by 2 publications

References 25 publications

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SARP: Synopsis-Based Approximate Request Processing for Low Latency and Small Correctness Loss in Cloud Online Services

Han

Zhan

Luis

2016

Int J Parallel Prog

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Despite the importance of providing quick responsiveness to user requests for online services, such request processing is very resource expensive when dealing with large-scale service datasets. These often exceed the service providers' budget when services are deployed on a cloud, in which resources are charged in monetary terms. Providing approximate processing results in request processing is a feasible solution for such problem that trades off result correctness (e.g. prediction or query accuracy) for response time reduction. However, existing techniques in this area either use parts of datasets or skip expensive computations to produce approximate results, thus resulting in large losses in result correctness on a tight resource budget. In this paper, we propose Synopsis-based Approximate Request Processing (SARP), a SARP framework to produce approximate results with small correctness losses even using small amount of resources. To achieve this, SARP conducts computations over synopses, which aggregate the statistical information of the entire service dataset at different approximation levels, based on two key ideas: (1) offline synopsis management that generates and maintains a set of synopses that represent the aggregation information of the dataset 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 B Rui Han 123 Int J Parallel Prog 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 result correctness metric, the results demonstrate: (i) SARP achieves significant response time reduction with very small correctness losses compared to the exact processing results; (ii) using the same processing time, SARP demonstrates a considerable reduction in correctness loss compared to existing approximation techniques.

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SARP: Synopsis-Based Approximate Request Processing for Low Latency and Small Correctness Loss in Cloud Online Services

Han

Zhan

Luis

2016

Int J Parallel Prog

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Sarp

Han

Wang

et al. 2015

Proceedings of the 12th ACM International Conference on Computing Frontiers

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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 Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. CF'15, May 18-21, 2015, Ischia, Italy Copyright 2015 ACM 978-1-4503-3358-0/15/05 ...$15.00 http://dx.doi.org/10.1145/2742854.2742858 .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.

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Getting ready for approximate computing

Cited by 2 publications

References 25 publications

SARP: Synopsis-Based Approximate Request Processing for Low Latency and Small Correctness Loss in Cloud Online Services

SARP: Synopsis-Based Approximate Request Processing for Low Latency and Small Correctness Loss in Cloud Online Services

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