ActiveSLA: A Profit-Oriented

wonderfuldistinctAI and Robotics

Oct 16, 2013 (3 years and 10 months ago)

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ActiveSLA
: A Profit
-
Oriented
Admission
Control Framework
for
Database
-
as
-
a
-
Service Providers

Pengcheng

Xiong

(Georgia Tech); Yun
Chi;
Shenghuo

Zhu;
Junichi
Tatemura
;
Calton

Pu
;
Hakan

Hacigumus



Presented by Yu Li

Outline


Introduction


Related work


Prediction module design


Prediction module evaluation


Decision module design


Decision module evaluation


Conclusions

Introduction


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


Introduction


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


None of them has well addressed the problem to directly
maximize
DaaS

provider’s
profits
by satisfying different SLAs
for their clients!


Introduction


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.

Introduction


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.

System architecture of
ActiveSLA

Prediction module design


What kind of models to use?


The model selection between linear and
nonlinear models, between regression and
classification models


What features to use?


The rich set of features for
DaaS

providers

Model selection


Linear
vs. Nonlinear


The execution time of a query depends on many factors in
a non
-
linear fashion, i.e., isolation levels and available
buffer size


Regression vs. Classification


From the machine learning point of view, a direct model
of classification usually outperforms a two
-
step regression
based approach.


Feature collection


Query Type and Mix (TYPE, Q
-
Cop,
ActiveSLA
)


Query Features (
ActiveSLA
)


E.g., the estimated number of sequential I/O


Database and System Conditions (
ActiveSLA
)


Buffer cache: the fraction of pages of each table that
are currently in the database buffer pool.


System cache: the fraction of pages of each table that
are currently in the operating system cache.


Transaction isolation level: Read Committed(FALSE) or
Serializable
(TRUE).


CPU, memory, and disk status: the current status of
CPU, memory, and disk in the operating system.

Description of the data and Environment


Prediction module evaluation


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


False positive

False negative

Total

number

Prediction module evaluation

Details on the Machine Learning Model


Positive value
-
>more likely to miss deadline


Negative value
-
>unlikely to miss deadline

Details on the Machine Learning Model


Overhead and feature sensitivity


Overhead


Training overhead. 72ms to build an initial model by using
12,000 samples.


Evaluation overhead. 8ms


Feature sensitivity


The more features, the
better


The gain by using more
features is less than the
gain by using a better
model.


Decision module design

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.


Model this as opportunity cost o.

Decision module design


Result with stationary workload (static
Poisson arrival rate)


Result with non
-
stationary workload
(dynamic Poisson arrival rate according
to 1998 World Cup Trace)


Single SLA


Multiple SLAs(service differentiation)


Decision module design


Result with stationary workload (static
Poisson arrival rate)


Result with non
-
stationary workload
(dynamic Poisson arrival rate according
to 1998 World Cup Trace)


Single SLA


Multiple SLAs(service differentiation)


Result with stationary workload


Result with non
-
stationary workload


Profit
-
oriented service differentiation

Conclusion

We proposed a framework,
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.

Thank you!