Cross-Tier Application & Data Partitioning of Web Applications

for Hybrid Cloud Deployment

Nima Kaviani Eric Wohlstadter

Rodger Lea

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Motivation – Cloud Computing

• Virtualization• Multi-tenancy• Scalability• Cheaper Deployments

However …Loss of controlRegulations (HIPAA, PCI)AuditingOutages…

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Motivation – Cloud Computing

• Virtualization• Multi-tenancy• Scalability• Cheaper Deployments

However …Loss of controlRegulations (HIPAA, PCI)AuditingOutages…

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Motivation – Hybrid Cloud• Combine Private cloud and Public IaaS• Split Architecture

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Motivation – Hybrid Cloud• Combine Private cloud and Public IaaS• Split Architecture• Software = Code + Data

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Motivation – Hybrid Cloud• Combine Private cloud and Public IaaS• Split Architecture• Data Placement Constraints

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Motivation – Hybrid Cloud• Combine Private cloud and Public IaaS• Split Architecture• Code Placement Constraints

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Motivation – Hybrid Cloud• Combine Private cloud and Public IaaS• Split Architecture• Inter code/data placement constraints

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Motivation – Hybrid Cloud• Combine Private cloud and Public IaaS• Split Architecture• Trade-off cross-cuts the system

Trade-off between Cost and Control

CHALLENGE…

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Motivation – Hybrid Cloud• Combine Private cloud and Public IaaS• Split Architecture• OLTP-style Web Applications

Trade-off between Cost and Control

CHALLENGE…

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Motivation – Hybrid Cloud• Combine Private cloud and Public IaaS• Split Architecture• OLTP-style Web Applications

Trade-off between Cost and Control

CHALLENGE…

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Code = Software FunctionsData= Database Tables

Related Work

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Partitioning Problem

Integer ProgrammingLibrary

Premise

Monolithic Profiling

Cloud

Application Partitioning

CodeProfiling Application-

Tier BIP

BIP Solver

Code PlacementMANTICORE

Nima Kaviani, Eric Wohlstadter, Rodger Lea. MANTICORE: a Framework for Partitioning Software Services for Hybrid Cloud. In Proceedings of the 4th IEEE International Conference on Cloud Computing Technologies and Science, December 2012, Taipei, Taiwan, pages 333-340.

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Cross-tier Partitioning

CodeProfiling Application-

Tier BIP

BIP Solver

Code PlacementMANTICORE

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Cross-tier Partitioning

CodeProfiling Application-

Tier BIP

EXPLAINPLAN

CreateObjectiveFunction

CreateConstraints

Join Orders

Combined Partitioning

Solver

Code Placement

Table Placement

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Data-tier Partitioning

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Engineers know: Database knows:

• Code and data dependencies• Data placement constraints

• Inter-data dependencies• Query cost models

Model the combined knowledge & Solve it!

Example Cross-tier Request FlowPull up user information and Stock holdings for a given user id

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AppTierAppTier

DataTierDataTier

Request

Query

Example Cross-tier Request FlowPull up user information and Stock holdings for a given user id

doGetUserStock

getUserInfo

executeQuery

renderHTML

ha

p userprofile (p)

holdings (h)account (a)

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AppTierAppTier

DataTierDataTier

Request

Query

Example Cross-tier Request FlowPull up user information and Stock holdings for a given user id(USER DATA ON PREMISE)

doGetUserStock

getUserInfo

executeQuery

renderHTML

ha

p userprofile (p)

holdings (h)account (a)

USER DATA: Constrained to be on premise

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Intra-Query Dependencies

ha

p

pa

h

Local Execution

Pull up user information and Stock holdings for every user

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doGetUserStock

getUserInfo

executeQuery

renderHTMLTwo choices

1.How bad would it be if we move or not move holding table to the cloud

2.How bad would it be if we join accounte and userporfile tables first

Intra-Query Dependencies

ha

p

pa

h

Pull up user information and Stock holdings for every user

Two cuts /two round-trips

Approach 1: Keep sensitive data on private premise

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doGetUserStock

getUserInfo

executeQuery

renderHTML

Intra-Query Dependencies

ha

p

pa

h

Pull up user information and Stock holdings for every user

More premiseresource usage

Approach 2: Push everything to private premise

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doGetUserStock

getUserInfo

executeQuery

renderHTML

Intra-Query Dependencies

ha

p

pa

h

Alternative Execution

Approach 3: Findalternative plans

1. One cut / less round-trip2. Less premise resource usage

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doGetUserStock

getUserInfo

executeQuery

renderHTML

Data Dependency Analysis

1. Analyze Alternative Plans2. Collect Data on Every Plan3. Create Mathematical Model

1. Define BIP Constraints2. Define BIP Objective

4. Combine with Code Partitioning

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1. Analyze Alternative Plans

Problem: Find performance for various plans

Solution:-Improve complexity

- Encode 2M alternative plans

- M is the number of tables in a plan

- M < 10

- M stable with the application size

- O (Q x 2M) vs. O (2N)

Straw man 1:-Try all 2N placements for tables

-N >> 10 and grows with the application size

Straw man 2:-Have all the analysis on one machine

-Inapplicable data

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2. Use EXPLAIN PLAN for Profiling

EXPLAIN PLAN FOR SELECT p.*, h.* FROM

holding h, accountprofile p,

account a WHERE

h.accountid = a.accountid AND

a.userid = p.userid AND h.quote_symbol = ? AND

a.accountid = ?

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Creating the Mathematical Model

• Encode join alternatives• Encode placement of tables in a join• Encode the objective–Minimizing cost –Minimizing performance

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4. Combine with Code Partitioning

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doGetUserStock

getUserInfo

executeQuery

renderHTML

pa

h userprofile (p)

holdings (h)account (a)

Combining objectives and constraints:

Generate the Results

Code on premiseCode in the cloud

Data on premiseData in the cloud

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Evaluation• Apache Day Trader – Open Source Trading Web App

• RUBiS – Open Source Auctioning Web App

• 2 Premise Machines

– Database Machine (2.5GHz dual core, 4GB RAM)

– Application Server (3.5GHz dual core, 4GB RAM)

• Cloud Instance– Amazon West Region (Oregon)

2 EC2 Large Compute Units

• User sensitive tables are pinned to the premise– Account & AccountProfile tables for Apache Day Trader

– Users table for RUBiS

• 15ms latency

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Evaluation - Deployments

doGetUserStock

getUserInfo

executeQuery

renderHTML

pa

h userprofile (p)

holdings (h)account (a)

Private Premise42

Evaluation - Deployments

doGetUserStock

getUserInfo

executeQuery

renderHTML

pa

h userprofile (p)

holdings (h)account (a)

doGetUserStock

getUserInfo

executeQuery

renderHTML

pa

h userprofile (p)

holdings (h)account (a)

Private Premise Naïve Hybrid43

Evaluation - Deployments

doGetUserStock

getUserInfo

executeQuery

renderHTML

pa

h userprofile (p)

holdings (h)account (a)

Code Split44

Evaluation - Deployments

doGetUserStock

getUserInfo

executeQuery

renderHTML

ha

p userprofile (p)

holdings (h)account (a)

doGetUserStock

getUserInfo

executeQuery

renderHTML

pa

h userprofile (p)

holdings (h)account (a)

Code Split Cross-tier45

Evaluation - PerformanceHow is the performance affected with different models of deployment?

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Cross-Tier deployment exhibits:28 - 56% improvement compared to Naïve-

Hybrid15 - 45% improvement compared to Split-Code

Evaluation – Deployment CostsHow is the cost affected with different models of deployment?

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Cross-Tier deployment exhibits:20 - 54% cheaper than Naïve-Hybrid11 - 29% cheaper than Split-Code

Evaluation – ScalabilityHow is the scalability affected with different models of deployment?

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Cross-Tier deployment exhibits:best scalability compared to all deploymentsNo performance degradation

Challenges

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Contributions

•Cross-tier partitioning provides options between cost, performance, and privacy

•We solved the combined partitioning problem

•Our evaluations show effectiveness of cross-tier partitioning

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