1 ActiveSLA: A Profit- Oriented Admission Control Framework for Database-as- a-Service Providers Pengcheng Xiong (Georgia Tech); Yun Chi (NEC Labs America); Shenghuo Zhu (NEC Labs America); Junichi Tatemura (NEC Labs America); Calton Pu (Georgia Tech); Hakan Hacigumus (NEC Labs America)
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1 ActiveSLA: A Profit-Oriented Admission Control Framework for Database-as-a-Service Providers Pengcheng Xiong (Georgia Tech); Yun Chi (NEC Labs America);
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DaaS provider consolidates multiple clients in shared infrastructures (multi-tenancy) greater economies of scale fixed cost distribution
Problem: system overload due to unpredictable and more bursty workloads dynamic provisioning, queuing and scheduling, and admission control
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Admission control related work
Macro level (feedback based): keep the mean query execution time at a specific level by tuning the best multiple programming level (MPL) for a given workload, e.g., ICDE2006
Micro level (query-by-query based): estimate every single query’s execution time by query type and query mix, e.g., WWW2004, ICDE2010
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None of them has well addressed the problem to directly maximize DaaS provider’s profits by
satisfying different SLAs for their clients!
First issue Merely estimating the query
execution time is not enough to make profit-oriented decisions. We need to know the probabilities of a query meeting and missing its deadline.
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Accept!
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Second issue We may have to make different
admission control decisions even when the queries have the same deadline and the same probability of meeting the deadline due to different SLAs.
Query Sets with PostgreSQL server TPC-W1 (browsing queries) TPC-W2 (mixture of browsing and
administrative queries) TPC-W3 (mixture of browsing,
administrative, and updating queries)
Prediction error
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False positive False negative
Total number
Prediction module evaluation
For different query sets(TPC-W2,3), different SLA settings (30s, 45s, 60s), we observe a steady decrease of errors when we use non-linear model, classification model, and include more features
Details on the Machine Learning Model
Positive value->more likely to miss deadlineNegative value->unlikely to miss deadline
CPU waiting% for IO
Q10 is the update querythat needs exclusive lock
Lots of lock contention
Very likely tomiss deadline
Details on the Machine Learning Model
Query Plan A
Query Plan B
Overhead and feature sensitivity
Overhead Training overhead. 72ms to build an initial
model by using 12,000 samples. Evaluation overhead. 8ms
Feature sensitivity
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The more features, the better
The gain by using more features is less than the gain by using a better model.
Multiple Query Decision Admitting q into the database server may
slow down the execution of other queries that are currently running in the server and make them miss deadline.
Admitting q will consume system resources and change the system status. This may result in the rejection of the next query, which may otherwise be admitted and bring in a higher profit.
ActiveSLA, for admission control in cloud database systems. Prediction module to predict the possibility
that a query can meet/miss deadline. Decision module to make the profit-oriented
decision. Future work
Improve the inaccuracy for the query features such as the number of sequential I/O due to the incorrect statistics and cardinality estimates of a query execution plan.
Extend our prediction module by including the level of replication as one of the system variables.
Extend our ActiveSLA to deal with different types of database systems to manage data and serve queries, e.g., NoSQL databases.