Home Strategy Activities Grants Publications People Sponsors Blog Contact Us 
  

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

publications:publi:rhjwfgjlvjz15sarp [2019/04/25 08:49]
publications:publi:rhjwfgjlvjz15sarp [2019/09/12 08:51] (current)
Line 1: Line 1:
  
 +<​html><​div id="​bib">​
 +
 +<p> <h1> RhJwFgJlvJz15sarp</​h1>​
 + <​p><​span class="​BibAuthor">​R. Han, J. Wang, F. Ge, J.L. Vazquez-Poletti,​ J. Zhan</​span>​. <span class="​BibInProceedingsTitle">​SARP:​ producing approximate results with small correctness losses for cloud interactive services</​span>​. In <span class="​BibInProceedingsBooktitle">​12th ACM International Conference on Computing Frontiers (CF'​15)</​span>,​ 2015.</​P><​p>​
 +<p>
 +<a name="​abstract"></​a><​h2>​ Abstract ​ </h2> <​P> ​
 +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 ownersÂ’ 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<​p>​
 + <a name="​keyword"></​a>​ <​h2>​Keywords </h2> <p> [ <a href="/​doku.php?​id=publications:​keyword:​tin2012-31518">​Tin2012-31518 </a> ] [ <a href="/​doku.php?​id=publications:​keyword:​cloud">​ Cloud</​a>​ ] 
 +<a name="​contact"></​a><​h2>​ Contact ​ </h2> <​P> ​
 +<a href="​mailto:​jlvazquez@fdi.ucm.es">​Jose Luis  Vazquez-Poletti</​a> ​ <a href="/​jlvazquez">​ <img src="/​lib/​exe/​fetch.php?​w=&​h=&​cache=cache&​media=html_icon.png"​ align=top border=0 alt =""></​a><​br> ​
 +
 +<a name="​bib"></​a><​h2>​ BibTex Reference ​ </h2> <​P> ​
 +@InProceedings{RhJwFgJlvJz15sarp,​ <​br>&​nbsp;&​nbsp;&​nbsp;​Author = {Han, R. and Wang, J. and Ge, F. and Vazquez-Poletti,​ J.L. and Zhan, J.},<​br>&​nbsp;&​nbsp;&​nbsp;​Title = {SARP: producing approximate results with small correctness losses for cloud interactive services},<​br>&​nbsp;&​nbsp;&​nbsp;​BookTitle = {12th ACM International Conference on Computing Frontiers (CF'​15)},<​br>&​nbsp;&​nbsp;&​nbsp;​Number = {0},<​br>&​nbsp;&​nbsp;&​nbsp;​Year = {2015}<​br>​} <​br><​p>​
 +
 +</​div></​html>​
Admin · Log In