"Preference-Aware Query and Update Scheduling in Web-Databases" -Huiming Qu, Alexandros Labrinidis
QC: Quality Contracts -> combines response time (QoS) and staleness (QoD)
QUTS (Query Update Time Sharing): adaptive algorithm to maximize the total profit from submitted QCs
QUTS make use of two level scheduling scheme that dynamically allocates CPU resources to updates and queries according to user preferences.
There are 3 policies:
1. FIFO: queue containing both updates and queries; are executed according to their arrival time
2. FIFI-UH: two queues -one for the updates and one for the queries. Updates have priority
3. FIFI-QH: two queues -one for the updates and one for the queries. Queries have priority
Average Staleness = # of unapplied updates
QUTS: is able to take the "best" profit dimension of the other policies: high QoS from QH and high QoD from UH using static QC design
QUTS: perform very close to ideal case when we vary qos_max and qod_max over time
Reference:
"Preference-Aware Query and Update Scheduling in Web-Databases"
or
"Preference-Aware Query and Update Scheduling in Web-Databases"
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annotation
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web service
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uncertainty
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API
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QoD
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bio
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confidence intervals
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data streams
(1)
grammar
(1)
load shedding
(1)
load shedding ; aggregate queries
(1)
load shedding ; continuous queries ;
(1)
load shedding ; dynamic data streams
(1)
meta-scheduling
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monitor dropped packets
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multi-values
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online
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propagation
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provenanace
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punctuation; security
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record matching
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review_paper
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semantics;
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Monday, June 15, 2009
Monday, December 8, 2008
ACM 2004: sampling operator
Theodore Johnson, S. Muthukrishnan, Irina Rozenbaum, "Sampling Algorithms in a Stream Operator", ACM2004
Tuesday, November 18, 2008
IEEEE 2007 -windowed stream join with time correlation
Bu ˘ gra Gedik , Kun-Lung Wu, Philip S. Yu , Ling Liu, "GRUBJOIN: An Adaptive Multi-Way Windowed Stream Join with Time Correlation-Aware CPU Load Shedding" IEEE 2007, page 1363-1380
workshop07- monitor dropped packets in a network traffic flow
Jarle Søberg, Kjetil H. Hernes, Matti Siekkinen, Vera Goebel, Thomas Plagemann, "A Practical Evaluation of Load Shedding in Data Stream Management Systems for Network Monitoring", in some workshop, may 2007, Italy
This is only a paper that can give an example how to monitor dropped packets in a network traffic stream. They suggest that TelegraphCQ does not count correctly the number of packets dropped and that STREAM does not have a mechanism for load shedding. What about the sample operator in STREAM ?!
Friday, November 14, 2008
ACM 2005, Adaptive Load Shedding
Bu˘ gra Gedik, Kun-Lung Wu, Philip S. Yu, Ling Liu, "Adaptive Load Shedding for Windowed Stream Joints" , ACM 2005, page. 171-178
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