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 SLAMultiple 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 SLAMultiple 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!